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7

7

Sirte University Scientific Journal (SUSJ)

)Periodical Academic Refereed(

Applied Sciences Volume 7, Issue No. 1, June 2017 General Director Dr. Ahmed Farag Mahgoub Editor in Chief Dr. Abdalsalam M. Muftah Editorial Board Dr. Hessien Abomadiena Dr. Mohammed O. Ramadan Dr. Mohamed A. Dow Dr. Ashraf Salem Abdalkafi

Sirte University Scientific Journal Post Box 674 Sirte, Libya Tel: 00218545265704-1178 Fax:00218545262152-1178

Email: [email protected]

Sirte University Scientific Journal (SUSJ)

A scientific refereed journal issued on behalf of Sirte University in a number of issues throughout the year interested in publishing research and documented studies in the field of humanities and applied sciences for university faculty members and other universities from inside and outside Libya . The views expressed in the publication are the individual opinion of the author(s) and they neither represents nor reflect the opinion of the editor and editorial board or Sirte University. The University reserves all copyright and, no re-print or publication of the whole or parts of the journal is allowed without prior permission.

II

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III



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Authors should send their papers to: Editor in Chief of Sirte University Scientific Journal Sirte University, Sirte – Libya or E-mail: [email protected]

IV

Sirte University Scientific Journal General Director's Word In the name of Allah The Most Gracious The Most Merciful All praise be to Allah and peace and Blessings be upon the Prophet Muhammed

Sirte University Scientific Journal is an official refereed journal. It published peerreviewed research in the fields of humanities and applied sciences. As one of its mission has been to improve the scientific research in order to broaden the people’s knowledge and serve the local community, and since the scientific research has been widely recognized as of one of the main standards on which universities’ performances and contributions are assessed in all over the world, we had to do our upmost to ensure the publication of the 1st issuance of 7th volume- Jun. 2017) which includes the humanity and sciences volumes. It is worth mentioning that the SUSJ, in a very short time and with very limited resources, has made significant and tangible achievements, namely, the spearheading of research culture, exchanging of expertise, boosting the spirit of cooperation and encouraging researcher to publish their valuable research papers in different scientific fields.

As the University’s President and the General Director of the journal, I am so delighted to present you with the current edition of the journal. It is also my great pleasure to extend my gratitude to the University’s faculty members, graduate students, researchers and reviewers who have all contributed in the publication of this issue and hoping that the editorial board has selected the most valuable research papers to be published in this edition. I should mention here that we, very much, welcome any comments and/or suggestions which could help in improving the next editions. Finally, I commend all the hard work and efforts exerted by the Editorial Board and to all people who have contributed, directly or indirectly, in bringing this issue into life. Peace be upon you The General Director

 VI

Sirte University Scientific Journal Editor in Chief's Word In the name of Allah The Most Gracious The Most Merciful

It remains my great honour to serve as Editor-in-Chief of the Sirte University Scientific Journal (SUSJ), and it is my pleasure to take this opportunity to thank everyone involved in the continuing success of the journal, and we would never have made it this far without the enthusiasm and assistance of the journal’s Editorial Board, and its readers. There are two special groups who are also included in the above, our referees and our authors. Without the referees’ valuable comments, the quality of the papers would not be as high as it is. Without the authors’ choice of SUSJ in which to publish their best work, the journal’s contribution to our community simply would not be as great as it is. It is also thanks to the hard work of both our authors and our referees that our publications enable all our readers to keep up with the latest developments within our sphere of research as early as possible. We look forward to your most serious and effective efforts, which aspire members of the editorial board and its general director, also aspire to the university in the dissemination of scientific research in all fields of human and applied sciences.

Although every effort has been made to make this journal errors-free and as scientific as possible, no work is perfect and this journal is not an exception. Therefore, we appreciate it if you could give us your feedback, suggestions and ideas in order to improve the next issues. Finally, I wish all of you a healthy and prosperous 2017!

May Pease be Upon You

 Editor in Chief

VI

Contents 1

Effect of Heat Treatment on Grain Size and Mechanical Properties of 316 Austenitic Stainless Steel A. K. Abdulkarim, M. H. Alkathafi, I. M. Ali

CFD Modeling of Elbow and Orifice Meters

15

Abdalsalam M. Muftah

Study of Galvanic Corrosion of Carbon Steel Pipelines Versus Some Types of Stainless Steel

33

Musbah M Abomadina

Multiple Performance Measures Performance in an Emerging Market

and

Organisational

51

Developments in Parabolic Solar Dish Concentrator for Enhanced System Efficiency of Steam Generation

79

Influence of Ground Granulated Blast Furnace Slag as Cement Replacement on Some Properties of Paste and Concrete Mixes

95

Abdallah Amhalhal

Imhamed M. Saleh*, Khalifa Khalifa, Mohamed Bughazem and Nabil Algharbi

Mohammed Ali Abdalla Elsageer and Ayad Abdelmoula Mohammed

VII

Effect of Heat Treatment on Grain Size ..… Sirte University Scientific Journal (Applied Sciences)

Vol. 7 (1), 1–14, June 2017

Effect of Heat Treatment on Grain Size and Mechanical Properties of 316 Austenitic Stainless Steel Abdulkarim K. Abdulkarim1, Moftah H. Alkathafi2, Imhamed M. Saleh Ali3 Mechanical Engineering Department, Faculty of Engineering, Sirte University1,2,3 E-mail: [email protected] Abstract Grain growth is one of the three important stages take place during annealing of cold-worked materials after the process of recovery and recrystallization. This paper presents the experimental analysis of the grain growth kinetics and hardness of 316 austenitic stainless steel having grain size between 6µm and 69µm. The experiments were carried out for three different temperatures and represent microscope images of grain growth during isothermal annealing at (900 oС, 1000°С and 1100oС) within 30, 60, 90 and 120 min respectively. The change of grain size as a function of annealing time for various temperatures have been presented in this study the value of the coefficient n for normal grain growth was determined and found to be in the range from 0.29 to 0.37. The investigation also included the determination of the activation energy for grain growth which found to be 104 KJ/mol. The hardness of 316 austenitic stainless steel has been measured at different grain sizes and the results indicated that the hardness decreased with increasing grain size and the data in general confirm the Hall-Petch equation. . Keywords: Grain growth, Hardness, Austenitic stainless steel, Cold-worked, Microstructure.

1.

Introduction

Austenitic stainless steels are the most common and familiar types of stainless steel. They are selected for numerous applications due to their favorable combination of characteristics such as low price, moderate to good corrosion resistance, excellent ductility and toughness along with good weldability [1]. From a metallurgical point of view, austenitic stainless steels can be made soft enough ( i.e., with a yield strength about 200 MPa) to be easily formed by the same tools 1

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that work with carbon steel, but they can also be made incredibly strong by cold work, up to yield strengths of over 2000 MPa[2].The primary objective of this research is to fundamentally understand the grain growth of austenitic stainless steels and grain size effect on the mechanical properties. Normal grain growth is defined as the uniform increase in the average grain size of a polycrystalline aggregate, due to the annihilation of small grains by grain boundary migration[3]. Grain growth is one of the three important stages take place during annealing of cold worked materials including recovery, recrystallization and grain growth. When a material is plastically deformed at a temperature that is low with respect to its melting point, it is called cold worked. Recovery is the change back of material to a stable and a lower energy condition. The mechanical properties changed due to the cold working, tend to recover their original values with slight or no change in the microstructure, this process takes place at relatively low temperature. During this stage the density of dislocations, number of vacancies and the internal stress are reduced. In addition, hardness and strength are usually somewhat reduced[4]. In recrystallization stage the cold worked grains are replaced by new ones and this usually occurs at a temperature higher than that required for recovery. The new grains nucleate at grain boundaries and increase in size until they impinge upon the neighboring grains. When the second stage of annealing is finished, new grains are formed and as the time passes at constant temperature, these grains start increasing in size. Consequently, the grain boundaries are reduced and the total surface energy is lowered, so the grain growth process occurs spontaneously due to the tendency of the system to reduce its internal energy.

2.

Experimental Procedure

2.1

Materials

A rod of austenitic stainless steel with a chemical composition given in Table1 after 30% reduction in cold working with diameter of eight millimeters and six millimeters height. The specimens were cut by means of cut off machine equipped with an abrasive wheel, during cutting the specimens are cooled by water as coolant to prevent the heating of specimen due to friction, which may change the microstructure at the surface. 2

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Effect of Heat Treatment on Grain Size ..…

Table 1.Chemical composition of the 316 austenitic stainless steel Element (Weight %) C Si Mn P 0.02 0.84 1.6 0.01 2.2

S Cr 0.015 17.8

Ni 13.3

Mo 2.22

N Fe 0.001 Balance

Heat Treatment

The specimens were categorized into five groups where each group divided into four specimens. The five groups were annealed at different temperatures (700,800,900,1000 and 1100o C) for different period of time (30, 60, 90 and 120 min), thereafter, each specimen was air cooled to the room temperature. All specimens were labeled in order to be distinguished from each other. 2.3

Specimens Preparation

The specimens were prepared for hardness and microhardness testing using metallographic laboratory . The specimens were ground on a belt sander with using water to avoid the heating that may effect on the properties and microstructure of the samples. This process was to remove all the scratches that may occurred during cutting. Due to the small size of specimens, mounting was carried out in the plastic mold, so the polishing process can be carried out more easily and faster. In the polishing silicon carbide was used as abrasive paper with 300, 400, and 600 mesh, respectively. The specimens were washed with running water and swabbed with cotton to remove adhering abrasive, then dried in the blast of warm air and stored in the desiccator. Etching carried out using a solution consists of 30 ml H2SO4, 100 ml HCl, 100 ml NHO3, and 100 ml H2O. 2.4

Hardness and Microhardness Measurements

The hardness and microhardness testing was carried out for each specimen, Vickers hardness instrument uses square based diamond pyramid with an angle of 136O between opposite faces. Microhardness test uses the same indenter but with a small test loads ranging from one to one thousand grams. The Vickers hardness load was 5Kg, while 200grs for microhardness test, the length of

diagonal for square indentation was measured using microscope. The Vickers

hardness number (VHN) is the ratio of the load applied to the indenter to the surface area of the indentation and can be calculated by this formula: 3

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VHN =

=

(1)

Where: F = the applied load, Kg L= the mean diagonal of the indentation, mm 2.5

Grain Size Measurement

Measurements of various grain geometry parameters, grain area (A), equivalent diameter (deq), maximum dimension (dmax), grain perimeter (p),and calculation of αand β were performed from micrographs taken of two randomly selected fields from each specimen, the shape factor α = dmax/deq and β = p/deq. The grain area (A) and the equivalent diameter (deq) can be directly used to characterize the grain size of individual aggregates. On the other hand α and β describe the shape of individual aggregates, with α being sensitive to grain elongation (α = 1 for a circle) and β being sensitive to the curvature of grain boundary. All the measurements were analyzed in terms of mean value of area, E(A), mean value of grain equivalent diameter E(d) and coefficients of a variation, CV(A) and CV(d). (2) Where: SD(X) is standard deviation.

3

Results and Discussion

3.1 Grain Growth The grain size as a function of the annealing time with various temperatures is shown in Table 3 and the results are presented in Figures 1,2 and 3 that show the different of grains growth during isothermal annealing at 900oC, 1000oC and 1100oC, respectively. In an effort to identify the beginning of recrystallization, annealing was done at low period of time (5,10 and 15min) and no recrystallization was observed. When annealing was carried out at 900oC for 30 min the recrystallized structure was observed. Figures 1,2 and 3 demonstrate the effects of holding time on grain growth at different temperatures, as it is clear, the average grain size increases with 4

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Effect of Heat Treatment on Grain Size ..…

increasing holding time from 30 min to 120 min, as well as, at annealing temperature increases the boundaries between annealed grains migrate and larger grain grow by an increase in the average grain size. The experimental results are plotted in Figure 4 on logarithmic coordinates. The points which have obtained experimentally in this work fall reasonably well on nearly parallel straight lines, the results confirm relatively closely to the following known empirical equation (the equation of grain growth) [5]:

(3) Where D is the average grain size, K is a constant of proportionality that relates heating temperature and activation energy for grain growth, and t is the total time of annealing, which is the sum of the time necessary for complete recrystallization and that of the grain growth[6].The values of n were determined from the slopes of the straight lines plotted in Figure4, which found to be ranged from 0.29 to 0.37.In Table 4 and Figure5 that show the logarithm of the slopes of these parallel lines as a function of the reciprocal of the temperature (log D2/t plotted versus1/T). The data give straight lines from which estimated that the activation energy Q for grain growth in 316 austenitic stainless steel is about 104 KJ/mole.

There is a difference between the n-values which are obtained experimentally from one investigator to another and this depends on the accuracy in measurements as well as on the material tested. In general the n-values are smaller than 0.5 which is obtained theoretically, for a constant distribution of grain size the grain boundary velocity is proportional to the driving force for growth, and inversely proportional to the grain size [7]. During the heat treatment of austenitic stainless steel there are different precipitation reactions can take place, the type of precipitates and their place of formation is strongly dependent on the time and temperature of annealing and the amount of cold working prior to annealing[8].Since the specimens are relatively heavily deformed about 30% cold worked, so it would not be surprising to expect the formation of intermetallic phase during recrystallization at grain boundaries and triple grain junctions as well as precipitation of carbide M23C6 at grain boundaries and as the recrystallization process progresses more carbide is precipitated at these grain boundaries. In 5

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the temperature of annealing of 9000C which is relatively low, it is unlikely that these precipitates ( carbides and intermetallic phases) retard the grain growth process, unless after long time, so the driving force for grain growth is high which can lead to high rate of grain growth ( i.e. high time exponent, n= 0.37).The coalescence of precipitate particles occurs in all the metallic systems at elevated temperatures which is a special significance in view of the effect of particle size on the energy barrier which can retard grain growth when coalescence has permitted the particle size to reach a critical size, so the driving force for grain growth is decreased[9]. This hypothesis is in satisfactory agreement with the present work results, where the annealing at higher temperature (10000C), the rate of grain growth was lower (i.e. the time exponent n was reduced to lower value n=0.29). So, the reduction in the value of time exponent n can be attributed to the coalescence of precipitates. At higher temperature of annealing (11000C), the time exponent was observed again increasing (n=0.37).The grain growth rate can be increased when the coalescence causes the particle size to exceed a critical value and dissolution of the precipitates is not a necessary requirement for growth to progress[9]. In the present study the time of annealing was two hours maximum, so this time was too short for dissolution of the precipitated particles to occur, but the coalescence of particles and exceeding the critical size could be a reason for that change in the rate of growth.

3.2 Hardness and Microhardness Measurements The results for the hardness and microhardness are given in Figures6,7and Table 5. These results indicated that the hardness decreases noticeably with increasing the grain size. The relationship between the hardness and grain size can be described by the Hall-Petch equation:

Hv = Ho + KH d-1/2

(4)

Where Hv is the Vickers hardness, Ho is intercept and KH is the slope and these values can be obtained from the Figure 6 where Ho= 114 kg/mm2 and KH= 30.7 kg/mm3/2. Therefore, the Hall-Petch equation can be expressed as:

6

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Effect of Heat Treatment on Grain Size ..…

Hv (kg/mm2) = 114 kg/mm2 + 30.7 kg/mm3/2 d-1/2 (mm-1/2)

(5)

A careful examination of the obtained experimental points suggests the existence of three linear relationships instead of one as shown in Figure 6.Each set of points contains the data for the specimens annealed at the same constant temperature which was different for each case. The dependence of hardness on grain size is numerically different in the three regions with different values of Ho and KH as indicated in Table6. The selection of three lines is based on the fact that when a single line is drawn using the entire data points, the result was shown in somewhat a poor fit and the points are more scattered. The three lines were proposed where the data points almost exactly fall on three lines. The three different annealing temperatures are likely to produce three different types of microstructure with varying content of precipitates. Such conclusion is further supported by detected differences in grain growth kinetics. The microstructure of specimens differ in the degree of grain size uniformity as measured by CV(A) and this effect should also be taken into account.

The microhardness measurements were carried out for the same material under the same conditions. According to the Hall-Petch equation the values of Ho and K are 127 kg/mm2 and 37 kg/mm3/2,respectively. The values are different from the values of hardness as expected due to the applied load and type of indenter. The measurements for the same grain size were found to be highly scattered and especially for small grain sizes. For the microhardness where low loads were used, the hardness within the grain interiors and in the vicinity of grain boundaries could be varied due to the difference between grain boundaries and grain interiors and particularly visible for fine grained material where more boundaries are present and probability that indentation can be localized at boundaries increasing. Generally, the microhardness measurements are less accurate and not reliable.

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30min

60min

90min

120min

Figure 1.Variation in grain growth with time at 900°С(500X)

30min 10A

60min 10B

8

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10D

Effect of Heat Treatment on Grain Size ..… 10B

10A

90min

10C

120min

10D

Figure 2. Variation in grain growth with time at 1000°С (500X)

30min11A

11C

90min11C

60min

11B

120min

11D

11D

Figure 3.Variation in grain growth with time at 1100°С (500X)

9

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o o o

Figure 4. Grain size as a function of the annealing time and temperature

Figure 5. The logarithms of the slope of the isotherms as a function of the reciprocal of absolute temperature

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Figure 6. Variation in Vickers hardness with d-1/2 175 170

Microhardness

165

160 155 150 145 140 135 130 10

15

20

25

30

35

40

d-1/2(um) -1/2 x100 Figure 7. Variation in Vickers microhardness with d-1/2

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Table2. Measurements of various grain geometry parameters Specimen E(A) SD(A) CV(A) E(d) SD(d) ( m)2 ( m) 42.6 63.4 84.2 113.9 411.1 517.5 896.4 951.3 2137 2863 3501.5 4623.2

9A 9B 9C 9D 10A 10B 10C 10D 11A 11B 11C 11D

41.066 60.103 77.043 106.72 501.95 595.13 1061.3 949.4 2147.7 2777.1 2860.7 4549.2

0.964 0.948 0.915 0.937 1.221 1.150 1.184 0.998 1.005 0.970 0.817 0.984

6.7 8.2 9.5 11.0 20.1 22.5 29.4 31.3 47.0 54.0 61.5 69.2

3.015 3.813 4.1135 4.983 10.8339 12.2175 16.758 15.337 22.607 27.054 26.0145 33.216

CV(d) 0.450 0.465 0.433 0.453 0.539 0.543 0.570 0.490 0.481 0.501 0.423 0.480

Table 3. Grain size as function of annealing time and temperatures Temperature(oC ) t ( min.) log t D ( m) 30 60 90 120 30 60 90 120 30 60 90 120

900

1000

1100

6.7 8.2 9.5 11.0 20.1 22.5 29.4 31.3 47.0 54.0 61.0 69.0

1.477 1.778 1.954 2.079 1.477 1.778 1.954 2.079 1.477 1.778 1.954 2.079

1.354 1.322 1.300 1.344 1.336 1.385 1.311 1.305 1.317 1.284 1.175 1.497

3.807 3.796 3.689 3.842 4.000 4.111 3.900 3.726 3.802 3.748 3.545 4.168

Log D 0.8260 0.9138 0.9777 1.0414 1.3032 1.3522 1.4683 1.4955 1.6720 1.7324 1.7850 1.8388

Table 4. Change oflog(D2/t) as a function of the reciprocal of the absolute temp. avg. log(D2/t) T (K) 1/T (K-1) D ( m) D2( m)2 t (min) log(D2/t) 6.7 8.2 9.5 11.0 20.1 22.5 12

44.89 67.24 90.25 121.00 404.00 506.25

30 60 90 120 30 60

0.1700 0.0490 0.0120 0.0036 1.1290 0.9260

0.0586

1173

0.008525

0.9560

1273

0.007855 Vol. 7 (1), 1–14, June 2017

Effect of Heat Treatment on Grain Size ..…

29.4 31.3 47.0 54.0 61.0 69.0

864.40 979.70 2209.0 2916.0 3721.0 4761.0

90 120 30 60 90 120

0.8500 0.9190 1.8670 1.6800 1.6160 1.5900

1.6800

1373

Table 5. Hardness and microhardness measurements Specimen Hardness Microhardness Grain Size 2 Designation HV5(Kg mm ) HV5(Kg mm2) d ( m) 9A 9B 9C 9D 10A 10B 10C 10D 11A 11B 11C 11D

151.0 146.4 146.1 145.0 136.0 133.2 133.7 132.1 127.1 126.9 125.5 125.9

165.2 165.6 163.8 160.0 149.3 146.3 144.3 142.0 140.0 139.8 137.2 137.0

6.7 8.2 9.5 11.0 20.1 22.5 29.4 31.3 47.0 54.0 61.5 69.2

Table 6. Values of Ho and KH in three different regions Constant Region I Region II 2 119 126 Ho ( Kg/mm ) 3/2 17 12.26 KH ( Kg/mm )

4.

0.00728

d-1/2 ( m)-1/2.102 38.633 34.921 32.444 30.151 22.305 21.082 18.442 17.874 14.586 13.608 12.751 12.021

Region III 128 17.88

Conclusions

 At relatively low temperature 900°С the rate of grain growth is high with time exponent in the grain growth law (n=0.37). At this temperature some carbides and intermetallic phases are nucleated, but their size is a small for a short annealing time, so the driving force is high.  At annealing temperature of 1000°С, the rate of grain growth is reduced with time exponent n=0.29 and this is probably due to the critical size of particles, resulting from the coalescence 13

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of precipitates (carbides and intermetallic phases )where they can retard the migration of grain boundaries and grain growth process.  At higher temperature of annealing (1100°С) the formed precipitates exceeds the critical size and their effect on grain growth process can be neglected and therefore the rate of grain growth is accelerated with time exponent n=0.37.  The grain growth is not significant at short times less than 15min even at high temperatures(1100o C).  Hardness in general decreases with increasing grain size.  Careful examinations of the obtained experimental data have given three sets of points with different values of

Ho

and

KH.

Each set contains the data from the specimens annealed at a

constant temperature.

References [1] Clara Herrera, Angelo F. Padilha and Ronald L. Plaut, ''Microstructure Evolution During Annealing Treatment of Austenitic Stainless Steels'', Materials Science Forum, Vols. 715-716, 2012, P.913. [2] www.asminternational.org. [3] Byung-Nam Kim, KeijiroHiraga and Koji Morita, ''Kinetics of Normal Grain Growth Depending on the Size Distribution of Small Grains'', Materials Transactions, Vol.44, No.11, 2003, PP.2239-2244. [4] Eric J. Mittemeijer, ''Fundamentals of Materials Science'', 2011, Springer Berlin Heidelberg, PP. 463-464. [5] R. Abbaschian,L. Abbaschian and R. E. Reed-Hill ''Physical Metallurgy Principles'', Fourth Edition,2009. [6] Chongxiang Yue, Liwen Zhang, Shulun Liao, and Huiju Gao, "Kinetic Analysis of the Austenite Grain Growth in GCr15 Steel," Journal of Materials Engineering and Performance'', Vol. 19, No. 1, 2009, PP. 112-115. [7] Esther T. Akinlabi, Stephen A. Akinlabi," Characterising the Effect of Heat Treatment on 3CR12 and AISI 316 Stainless Steel", International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering, Vol.8, No.2, 2014,PP. 254-259. [8] A.F.Padilha and P.R.Rios," Decomposition of Austenite in Austenitic Stainless Steels", ISIJ International, Vol.42,No.4,2002,PP. 325-337. [9] A. Di Schino and J. M. Kenny, "Analysis of the Recrystallization and Grain Growth Processes in AISI 316 Stainless Steel", Journal of Materials Science, Vol. 37, 2002,PP. 5291-5298.

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CFD Modeling of Elbow and Orifice Meters Sirte University Scientific Journal (Applied Sciences)

Vol. 7 (1), 15–32 June 2017

CFD Modeling of Elbow and Orifice Meters Abdalsalam M. Muftah Mechanical Department, Faculty of Engineering, Sirte University, Libya E-mail: [email protected] Abstract Elbow and Orifice meters are two of the common flow measurement systems which are used to determine the pressure difference occurring as a fluid flows change by resistance. This differential pressure exists when a flowing changes direction due to a pipe turn as in the case of Elbow meter. The pressure difference results from the centrifugal force. Since pipe elbows exists in plants and its cost is very low. However, the accuracy is very poor [2]. Due to the fine pressure measurements required for the orifice meter, small changes in the physical geometry leads to large errors in the flow meter calculation. For example, if the fluid flow rate is too large for a given orifice, cavitation can occur, causing wear on the orifice. The same would be true for fast moving fluid with solid particulates included in the flow. For this reason, an additional study was conducted on an orifice plate with a slightly enlarged orifice to determine the sensitivity of flow measurements in regards to the orifice diameter. The purpose of this paper is to run a CFD Model (Computational Fluid Dynamics) at Elbow and Orifice meters using Solidworks Flow Simulation [1] with different pipe sizes, ranging from nominal diameter. The CFD Simulation will be extended to run with using different fluid viscosities, varying as air, steam, oil and water. The goal is to determine the sensitivity of flow measurements in regards to these parameters. The results will be compared to a corresponding theoretical solution to investigate how much the accuracy can be improved by changing the geometry of the pipes and fluid viscosities.

Keywords: Computational Fluid Dynamics (CFD), Elbow meter, Orifice meter Pressure drop, Cavitations.

1.

Theoretical Overview

1.1 Elbow meter Elbow meter, flow measurement device, is the most widely applied in industrial and laboratory practice. Several investigations have been reported to determine the friction factor and pressure 15

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drops in horizontal [3] and vertical [4]. The principal of operation of this device consists basically in determining the pressure difference which is measured on points at inner and outer side of elbow duct. Sometimes, instead of preparing characteristic of the device, simple algebraic relations are used derived from experimental data, it has been shown that Bernoulli’s equation can be modified to relate the pressure and elevation at these pressure points by introducing a bend coefficient term

which varies from 1.3 to 3.2 depending on the geometry

of the elbow [5].

By setting all terms equal to V, we get

Then substituting this equation into the relation

The pressure difference (

Where

, we get

) can be determined by setting all terms equal to

and

The pressure difference

and velocity

in CFD simulation can be calculated by the

following equations:

Where

16

is the radius of curvature,

is pipe diameter and

is the flow density.

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CFD Modeling of Elbow and Orifice Meters

Figure 1 Elbow Meter Geometric 1.2 Orifice Meter An orifice meter consists of an orifice plate (plate with a hole drilled in the middle) placed inside the pipe to force a moving fluid through the hole in the plate. This reduction in area causes the flow to continue to converge to a theoretical minimum flow area known as a vena contracta. By placing a pressure tap near or at the location of the vena contracta, and placing another pressure tap a short distance upstream of the orifice, it is possible to compare the differential pressure at these two locations to determine the flow rate of the fluid. A typical arrangement of the pressure taps is one nominal diameter downstream and one-half nominal diameter downstream of the orifice plate.

Figure 2 Orifice Meter Flow and Tap Placements Bernoulli’s equation when applied to an incompressible fluid is

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p = static pressure γ = weight of fluid per unit volume V = velocity g = acceleration due to gravity z = elevation above reference point Qi = ideal volumetric flow rate Qa = actual volumetric flow rate Cv = coefficient of velocity A0 = area of hole in orifice plate Cc = contraction coefficient C = orifice coefficient G = mass flow rate D1 = pipe ID D2 = orifice ID

Using the volumetric flow rate relation:

, the velocity term V1 can be replaced

in Bernoulli's equation, yielding the ideal flow rate:

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To determine the actual flow rate, an experimentally derived coefficient is introduced into the equation to form:

Since the area of the vena contracta is actually smaller than the area of the hole in the orifice plate, a contraction coefficient is applied to the area of the hole in the orifice to derive the new A2. A2 = CC A0 which can be substituted back into the previous equation to form:

Where C is derived from experimental data to form the curve fit equation:

The mass flow rate G can be found as

In addition, the total permanent pressure drop in the system due to the orifice restriction is also derived through experimental data, and is found to be

2.

CFD Model Geometry and Parameters

2.1 Orifice Meter Model Geometry For the five different analyses run on the Orifice Meter, standard ANSI pipe sizes were used from 19

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Abdalsalam M. Muftah

for all calculations. The detailed geometry of the orifice was modeled using values from the Avco Valve manufacturer’s data sheet for paddle type orifice plates (found at avcovalve.com). Static pressure measurements were taken one nominal diameter upstream of the orifice and one-half diameter downstream, just inside of the pipe wall. The straight pipe section upstream of the orifice was modeled with a length of 1.5 times the nominal diameter. The downstream pipe length was set equal to six times the nominal diameter.

2.2 Elbow Meter Model Geometry Elbows utilized on the CFD Simulation have an angle of curvature of and nominal diameter are ranging from

, average curvature

. in general, the curvature pipe section

length was equal to 1.5 times the nominal diameter, the straight pipe section length on the inflow side was given a length of two times of nominal diameter, while the straight pipe section length on the outflow side was given a length of four times the nominal diameter. Static pressure sensors were located at inner and outer of curvature section shown in Figure (1). The described elbows are characterized by a high level of smoothness both on inner and outer surfaces. 2.3 CFD Model Parameters Inlet Mass flow , Outlet Environment Pressure , Fluid Types are air, steam, oil and water at Turbulence 2% Gravity

.

Pipe roughness

3.

The CFD Meshes and Convergence

The CFD Elbow Models were run successfully for all cases. The initial mesh consists of 1484×1664 cells as shown in figure (3-a). For simplicity and computational time, the mesh was initially settled at level 3. The run was converged at small computation time around 18 second 20

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CFD Modeling of Elbow and Orifice Meters

and iteration equals to 58. Since our objective to improve the accuracy to CFD Models, the meshes were refined until level 6 as shown in figure (3-b). Increasing the refining level more than level 6 never gave any improvement for the systems. A summary of these results is listed in Table 1. The CFD Orifice Models were also run successfully for all test cases. The initial mesh for the CFD analysis was set for the smallest fluid cells to be centered around the orifice plate, with an increasingly coarse mesh as the distance from the orifice plate increased. The initial mesh is shown as Figure 4.

a- Initial mesh

b- Final mesh

Figure 3 CFD Mesh, a) initial mesh, b) final mesh

Figure 4 Initial Mesh for Orifice Meter Analysis (40,000 total fluid cells and 10,000 partial fluid cells)

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Figure 5 Samples of CFD Model Convergence Table 1. The Refined Mesh Elbow size 6

Refined mesh 12756×7496

8"

11872×6248

119

10"

11894×6248

83

12"

11881×6248

118

"

4.

Iteration 101

CFD Simulation Results

This study has been conducted on four CFD specimens with different diameter sizes varying from 4" to 12" inch, and on four CFD specimens with different liquid viscosities such as air, gas, water and oil. 4.1 CFD Results – Elbow Meter

4.1.1 CFD Model with diameter size variation: Table 2 The sensitivity of Pressure difference to Elbow diameter size P1 - P2 (psi)

Model

Theory

"

0.1357

0.1370

Error 0.9 %

"

0.1863 0.2291 0.2652

0.1875 0.2343 0.2740

0.65 % 2.22 % 3.2 %

ND 6

8 10" 12"

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a

b

Figure 6 CFD Elbow Meter Results a) Pressure distribution b) Velocity Profile

Figure 7- a) Comparison of pressure difference in CFD Models with analytical solution. b) The effect of pipe diameter on CFD Error

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4.1.2 CFD Model with Fluid Viscosity variation:

Pressure Distribution

Velocity Profile

Figure 8 CFD Results a) Pressure distribution b) Velocity field

Figure 9- a) Comparison of pressure difference in CFD Models with analytical solution b) The effect of fluid type on CFD Error 24

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CFD Modeling of Elbow and Orifice Meters

Table 3 The sensitivity of Pressure difference to flow viscosity variations P1 - P2 (psi)

ND

Model

Theory

Air

0.005842

0.0065

Error 10.12 %

Steam Oil Water

0.011304 0.210691 0.22906

0.0119 0.2145 0.2343

5.00 % 1.77 % 2.23 %

4.2 CFD Results- Orifice Meter

Pressure Distribution

Velocity Profile

4"

6"

8"

10"

12" Figure 10 The effect of the diameter dimension on CFD Orifice Meter results

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Table 4 The sensitivity of Pressure difference to flow viscosity variations P1 - P2 (psi) ND 4" 6" 8" 10" 12"

Theory 0.008530 0.001198 0.0003933 0.0001557 0.00007402

Model 0.006245 0.001029 0.0003271 0.0001223 0.0000611

Permanent Pressure Drop (psi) Error -0.2524 -0.1524 -0.2043 -0.1668 -0.1653

Pressure Distrubuation

Theory 0.008519 0.001621 0.0005278 0.0002068 0.00009921

Model 0.007241 0.001419 0.0004413 0.0001817 0.00008663

Error -0.1515 -0.1274 -0.1629 -0.1229 -0.1269

Velocity Profile

Oil

Water

Steam

Air

Figure 11 The effect of the fluid viscosity variation on CFD Orifice Meter results Flow visualizations for the 4" nominal diameter configuration are shown as Figures 12, 13, and 14 above. In addition to the static pressure measurements taken at locations representing proper tap locations in the model, an additional averaged static pressure was found at the outlet of the orifice meter model which corresponded to the permanent pressure drop in the system. A 26

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summary of the modeled and analytical results can be found in Table 4.

Figure 12 Water flow through 4” Orifice Meter

Figure 13 Pressure Distribution in 4” Orifice Meter

Figure 14 Gradient Field Showing Velocities through Orifice To model wear on the orifice, an analytical calculation was made assuming that the orifice diameter was enlarged by 5%. The expected pressure difference between the taps was calculated for a mass flow rate of 1.0 lbm/sec. This pressure value was then inputted back into the analytical solution using the initial orifice diameter value in the calculations. A new 27

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mass flow rate was found and was then compared to the 1.0 lbm/sec to determine how much of an error would be produced if the orifice diameter were enlarged by 5% without a recalibration of the meter. These results are shown below in Table 5.

Table 5 Error Produced by Gradual Wear of Orifice (5% increase in diameter) % Error -10.26

Assuming Dworn = Dnew G = 0.8973 lbm/sec

(P1 - P2) for Dworn 0.006828 psi

ND 4”

Figure 15 a) Orifice Meter Pressure Differences Found through CFD and Analytical Solutions, b) Orifice Meter CFD Percent Error

5.

Cavitations

Cavitations is a phenomenon that can be present in several applications such as irrigation pressure-reducing values, sprinkler orifices or even in flow through xylem vessels inside plants. In the present study, numerical predictions of cavitation in a series of orifices were showed in CFD Flow Simulation SW. Model predictions for the orifice cases accurately capture cavitation conceptions. In general, flow simulation provides very reliable simulation for different geometries when different fluid is assumed. According to Knapp et al (1970), cavitation can be occurred due to flow acceleration and consequently an accompanying drop pressure at a point within the liquid flow that causes vapor bubble formation. Bubbles travel downstream until the increase of pressure drop causes the 28

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CFD Modeling of Elbow and Orifice Meters

bubbles to implode. This two-step process is known as cavitation. Cavitating flows often lead to performance degradation and structural damage to many hydraulic devices. The use of CFD in designing engineering devices has increased over the past few years due the availability of commercial codes. The effect of cavitations in circular orifices was experimentally investigated by Nurick (1976). Cavitations occur when the flow passes through a very small orifice which produces a high differential pressure. The Cavitating conditions are generated just after the orifice plates in the main line and hence the intensity of the Cavitating conditions strongly depends on the geometry of the orifice plates. When the flow passes through the orifice plates, the velocities at the orifice increase due to the sudden reduction in the area offered for the flow, resulting in decrease in the pressure. If the velocities are such that their increase is sufficient to allow the local pressure to go below the medium vapor pressure under operating conditions, cavities are formed. Such cavities are formed at downstream of the orifice plate, which also depends strongly on orifice plate cross-section, the velocities decrease giving rise to increasing pressures and pressure drops, which control the different stages of cavitations. The formed cavitations also depend strongly on the type of fluid flow passes through the orifice plate. For example, the passing air and steam flow results local pressure below the medium vapor pressure when a small change in decrease in the pressure and a small increase in velocities will be sufficient for the cavitations to occur. On the other hand, oil flow has higher vapor pressure under operating conditions, in such flow, the pressure drop often was not sufficient to let the cavitations to occur resulting stable flow. Figure 10 shows two cycles of cavitations in downstream beyond the orifice showed unsteady situation, but after the cycles ends it reached a steady situation. The main focus of this section is to investigate the pressure drop for single phase flow in Orifice. The pressure drop in the bend was found to be dependent on the pipe diameter. CFD analysis was performed on fife different elbow size at water flow. Each of these conditions was analyzed in CFD in order to accurately predict the effect of varying the pipe diameter. Pressure contours are presented in figure (3.5). It can be seen that the pressure is higher at before throat the Orifice after troths for all pipe diameter. Figure (4.3) shows that the pressure decreased at the before throat of the pipe when

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the diameter increased (Red color). Collected data in Table (6) show also that the pressure drop decreased from 0.5419497 into 0.0085537 when the pipe diameter increased from 4" to 12".

6.

Discussion and Conclusion

The paper was carried out of two stages. First one was to study and investigate the sensitivity of pressure difference and distribution to the change in Elbow geometry. The results show that pressure difference increased with increasing in pipe diameter. In this paper, the error between the analytical solution and CFD outputs ranged from 0.9% to 3.2%, the larger pipe size giving larger errors. The high accuracy in present model may due to using high level of refining CFD mesh. The second stage of this paper was to study the sensitivity of pressure difference to changing of fluid viscosity. The results show that the pressure difference increase as the fluid transmit from gas to liquid phase. The error between the analytical solution and CFD outputs ranged from 1.77% at oil to 10.12% at air. The results show that errors in Elbow meter was not depend on pipe geometric only but it also depend on the fluid which might be used. The results also recommend that in industries which used steam and liquid flow, elbow meter can give a good accuracy according to this study. In general, the analytical method is general, systematic and significantly more accurate than computer simulations. Although Bernoulli’s equation which basically used to measure the pressure loss in this paper is a simple algebraic relation, its results were not that quite consistent. For example, the result does depend on the bend coefficient term

which varies

from 1.3 to 3.2 depending on the geometry of the pipe. This coefficient needs to be well-tested and reliable measurements are to be made. In addition, the experimental results are at considerable variance with one another in regard the best judgment of value of speaking, it appears that

. Generally

is considered not convinced especially for large pipe which no

information of values has been found [5]. In this paper, it has been used this formula [1] to determine the bend coefficient 30

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CFD Modeling of Elbow and Orifice Meters

Although, the effect of bends were also not considered in the CFD simulation, the results has shown a great approximation towards the analytical solution especially when the mesh has been refined. Generally speaking, it is hard to say that the analytical solution is exact solution with the observation on the bend coefficient or to say that CFD Model can give exact solution due to the computational considerations. Although the error between the analytical results and CFD results ranged from 12-16%, a further refinement of the model did not yield better results. This error was introduced most likely because the analytical/experimental solution incorporates the orifice coefficient, C. This coefficient was initially derived by other experimenters through curve fitting physical data for a range of β. Since these physical experiments will have an inherent surface roughness associated with the physical pipe which was used, an average surface roughness value has been built in to the analytical solution. For this reason, the analytical solution predicted a greater pressure drop as was seen through the CFD analysis. Since the initial source for this experimental data is unknown, the surface roughness of the pipe used in the experiments is also unknown. If this value were given, the CFD model could be modified to include surface roughness to achieve better agreement between the analytical/experimental and CFD results. Significant error was introduced into the analytical mass flow rate calculations by introducing a small change in the size of the orifice (5% increase in diameter). Table 2 shows that for a 4” Orifice Meter configuration, the flow measurements would be off by approximately 10%. This confirms what is often seen in industrial applications, which is that as the orifice becomes worn over time, the measurements of the flow meter produce increasingly poor results.

Lastly, the study concludes that most inline flow measurement devices require a calibration stage in order to determine a more accurate measurement for flow. The CFD analysis stresses 31

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the importance of these calibrations, since even the slightest difference between actual and expected physical features will yield significant errors in the flow calculations. In addition, the values for the bend coefficient and the orifice coefficient are often difficult to extrapolate from previous experimental results, and differences in geometries and pipe roughness only add to the potential error of the flow rate calculations.

Conducting field calibrations will

significantly reduce these errors by determining device-specific coefficients to be used in the calculations.

References [1] [2] [3]

[4]

[5] [6]

Mutsson J. E. (2011). An introduction to Solidworks Flow Simulation 2011, SDC Publications Figliola R. S. and Beasley D. E. (2011). Theory and Design for Mechanical Measurements, Fifth Edition, Wiley Publication. Cole J. S., Donnelly G. F., and Spedding P. L.( 2004). Friction factors in two phase horizontal pipe flow, International Communications in Heat and Mass Transfer, vol. 31, no. 7, pp. 909– 917. Wongwises S. and Kongkiatwanitch W.( 2001). Interfacial friction factor in vertical upward gasliquid annular two-phase flow,” International Communications in Heat and Mass Transfer, vol. 28, no. 3, pp. 323–336. Pritchard P. J. and Leylegain J. C. (2011). Introduction to Fluid Mechanics, Fifth Edition, Wiley Publication. Abdalsalam M. Muftah (2014). 3D Fluid flow in an Elbow Meter-CFD Model. Sirte University Scientific Journal 4(1):35-42.

.

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Study of Galvanic Corrosion of Carbon Steel …… Sirte University Scientific Journal (Applied Sciences)

Vol. 7 (1), 33–49, June 2017

Study of Galvanic Corrosion of Carbon Steel Pipelines Versus Some Types of Stainless Steel Musbah M. Abomadina1 1

Department of Petroleum, Faculty of Engineering, Sirte University, Sirte, Libya

E-mail: abomadina 1@ yahoo.com Abstract In petroleum industry under special conditions (NaCl concentration of 70,000 ppm and temperature of 70 C˚ ) a significant galvanic corrosion problems are appeared . The conditions in the petroleum wells in the eastern region in Libya regarding the characteristics of formation water, which has concentration of NaCl ranging between 40,000 to 70,000 ppm and varied temperature degrees between 40 C˚ and 70 C˚ The constructing materials of these wells are connected with piping system made essentially from carbon steel .The piping system contains for special purposes some joints of stainless steel of grades 304 as well as 316 . The connection points between carbon steel and stainless steel considered as focal points for galvanic corrosion due to the potential difference between both materials in the electromotive series . The objective of this work is to study the galvanic corrosion in such case and the evaluation of rates of corrosion (for carbon steel) by laboratory tests . The experimental technique used in this study is the weight loss method.

Keywords: galvanic corrosion ,carbon steel, SS304,SS316,NaCl concentrations.

1.

Introduction

In many practical applications the contact of dissimilar metals is unavoidable in complex process streams and piping arrangements. Different metals and alloys are frequently in contact with each other due to mechanical and economic consideration. But the connection of metals or alloys together leads mostly to its corrosion due to its difference in galvanic potential . So, its electrical contact will lead to "galvanic corrosion". the magnitude of galvanic corrosion depends not only on the potential different of dissimilar metals, but also on kinetic parameters such as corrosion rate and anode to cathode area ratio. The most common method of predicting galvanic corrosion is by immersion testing of the galvanic couple in the environment of interest.

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Although time-consuming, this is the most desirable method of investigating galvanic corrosion. [1]

2. Experimental work Before selecting a corrosion test measurement technique, it is important to define the reason for doing so. This increases the chances of selecting the best method.

The main reasons for

measuring corrosion are to: • Monitor corrosion as it occurs in the plant. • Evaluate materials and environment effects for future application. • Test the quality of a specific material of known behavior . • Study the mechanisms of corrosion [2] Corrosion tests are divided into two broad categories: (1) tests made in the laboratory under controlled conditions, and (2) tests made in the field under natural or service conditions. The primary purpose of galvanic couple tests is to obtain useful information in predicting or controlling the extent to which galvanic couple action occurs when dissimilar metals are in contact under given conditions of service

.

In order for galvanic couple action to occur, the two or more metallic elements of the couple must be in contact while exposed to an electrolyte; and a difference in the potential must exist between them. When these basic requirements are satisfied, galvanic couple action can and will occur.

2.1 cell of measurements:Resume about the set: The capacity of the set is 20 litter, its function is to stir the liquid and making the liquid running continuously to the samples where the samples are submerged wholly and also making a control in the required temperatures by a special key .

This set is connected to a vessel by tubes where the liquid goes into and out of the vessel through these tubes. The Samples were put into this vessel where a plastic insulator between C

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Study of Galvanic Corrosion of Carbon Steel ……

S samples and SS.304 sample, will be placed. The two samples are tied and hanged by a plastic wire inside the vessel. the temperature key thermometer Switch – on

the samples location the samples location

the liquid running direction

Figure 1. Picture of instrument used.

2.2 Raw materials: Samples: 1-The under test carbon steel samples have the chemical composition shown in the following table :Table 1. The carbon steel samples have the dimensions (7x2x0.25 cm( Composition of carbon steel, wt% C Si Mn P S Cr Mo Ni Cu 0.031 0.013 0.249 0.0139 0.0055 0.008 0.005 0.013 0.0015 Sn Al Co Nb Ti V Zr B Zn 0.0004 0.0354 0.006 0.0024 0.0018 0.0035 0.0002 0.0001 0.0001

2.The stainless steel samples (SS.304,SS.316) have two dimensions which are (7x2x0.25 cm) and (3.5x2x0.25 cm) .

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3.Just before the experiments the investigated samples were mechanically polished using emery papers with grades up to 40-60 80.The polished samples were degreased by acetone and a thoroughly washed by distilled water ,dried and weighed. 

Chemicals:

All solutions are prepared from commercial sodium chloride salt by dissolving the weighed masses in distilled water .

3. Conditions of Working These are selected to undergo the experiments under the following working conditions : 1.

anode to cathode area ratio 1:1,2:1.

2. concentration of NaCl solutions (20,40,50,60,80)g/L. 3. The distance between the anode and the cathode are 1 cm and 3 cm. 4. Temperature of solutions (20,40,60,80) C˚. 5. Time of experiments (2,4,6)hours. 6.

pH 7

4. Weight Loss Experiments The samples to be tested are prepared , weighed , then the samples are fixed in the previously mentioned apparatus for the desired test time, after that the sample is withdrawn from the cell, cleaned, and reweighed .From this weigh loss (mg) is calculated . The mpy is calculated from the weight loss determinations and plotted in different manners . The plotted examined samples are subjected to additional investigations to identify the corrosion products . 4.1 Corrosion rate calculations: The famous formula for expressing corrosion rate was applied :[3] mpy =

534 x W DAT

where mpy = mils per year W = weight loss, mg 36

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Study of Galvanic Corrosion of Carbon Steel ……

D = density of specimen, g/cm³ A= area of specimen, sq. in T= exposure time, hr The experiments were doubled to make sure of results . The results for each case was calculated and recorded regularly.

5. Results and Discussion The plotted results are obtained through many trials of experiments taking into account the change of sodium chloride solution of concentrations between 40-80 g/L, experiment temperature ranging between 20-80 C, surface ratio of anode to cathode 1:1,2:1, and the separation distance between the electrodes which may be one or three cm.The first category of results are presented in Figs (2 ) pointed out the change of temperature against rate of corrosion at different immersion times (2,4,6 h) and at constant sodium chloride concentration of 40g/L. Figs ( 2, 3 ) presents the results obtained at anode to cathode area ratio 1:1 and for separation distance between the electrodes 1, 3 cm . Figs (4 , 5) present the results obtained at anode to cathode ratio of 2:1 for the same separation distance between electrodes (1 , 3 cm) . It is clear from the results of Figs (2 - 5) that the corrosion rate increases with increasing temperature up to 60 C˚ and then decreases at temperature of 80 C˚. This means that maximum rate of corrosion

was observed at 60 C˚ .

Apart from temperature, and looking to the time of experiment, the rate of corrosion are proportional directly to the time of experiments at 20, 40 and 60 C˚ ,but it is inversed at 80 C˚ as shown in Figs (2 - 5)

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Rate at 2h

Rate at 4h

Rate at 6h

0.1 0.08 mpy 0.06

0.04 0.02

0 Temperature c Figure 2 corrosion rate of carbon steel versus SS.304 at concentration(NaCl) 40g/L (Anode to cathode aera ratio 1:1,the distance between the anode and the cathode 1cm)

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Study of Galvanic Corrosion of Carbon Steel ……

Corrosion rate of ( CS v SS.304) Corrosion rate of ( CS v SS.316)

0.1 0.08 0.06 mpy 0.04 0.02

0 Temperature

c

Figure 26 corrosion rate of carbon steel versus SS.304 comparing with corrosion rate of CS versus SS.316 at (NaCl) 40g/L (Anode to cathode aera ratio 1:1,the distance between the anode and the cathode 1cm , in 6h)

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The other observation is the decrease of maximum rate of corrosion rate from Fig(2) to Fig(5) which observed at 60 C˚ for all samples .this may be attributed to the change in the anode to cathode ratio and separation distance between the anode and cathode. Figs (6 - 9) rate of corrosion versus temperature at 50g/L concentration of NaCl . Figs (6 , 7) represent the anode to cathode ratio 1:1 and Figs. (8 , 9) represent the anode to cathode ratio 2:1 for separation distance 1 , 3 cm between anode and cathode. The plots of these figures have the same general features of those obtained in figures (2 - 5) in the electrolyte containing 40g/L NaCl . Figures (10 - 13) represents the corrosion rate versus temperature at applied concentration of 60g/L of NaCl and with different area ratio of anode to cathode which are 1:1 , 2:1 Figs (10 , 11) and with separation distance of 1cm , 3 cm for Figs (12 , 13) . The plots of Figs (10 - 13) have the same general features of the previously mentioned plots in Figs (2 - 5) . These figures indicate that the corrosion rate of the carbon steel electrodes increases with the increase of temperature up to 60 C˚ and then decreases with the increase of temperature . Also , the rate of corrosion the carbon steel electrodes increases with the decrease of area ratio (anode : cathode) and decrease of separating distance . The last group of figures (14 - 17) which represent the change of corrosion rate with the change of temperature from 20 – 80 C˚ at constant concentration of sodium chloride (80g/L) . The general features of the plots of Figs (14 - 17) are the same as the previously mentioned plots of Figs (2 - 13) obtained at different sodium chloride concentration (40,50,60 g/L) . In the presence of 80g/L the rate of corrosion of the carbon steel as given conditions is less than those obtained at other sodium chloride concentrations (40,50,60 g/L). Figures (18 - 21) represent the variation of the corrosion rate of the carbon steel anodes with temperature (20, 60 C˚) in sodium chloride of concentration of 20g/L .The cathode was stainless steel 304 . The anode : cathode area ratio was 1:1 and 2:1 . the separating distance was 1 , 3 cm . These plots indicate that the corrosion rate of the carbon steel anodes increases with increase of temperature , and with the decrease of separating distance. On the other hand, the corrosion rate decreases with increase of anode : cathode area ratio from 1:1 to 2:1 .

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Figures (22 - 25) represent the variation of corrosion rate of carbon steel with temperature (20 80) in 40g/L sodium chloride solution. The cathode was stainless steel 316. the anode : cathode area ratio was1:1 and 2:1 .The separating distance was 1, 3 cm. The general features of the plots of these figures are the same as those obtained in the case of use of stainless steel 304 as a cathode . The plots of Figs (22- 25) indicate that the corrosion rate of carbon steel increases with increase of temperature from 20 to 60 C˚ and then decreases with increases of temperature to 80 C˚ . Also, the rate of corrosion increases with the decrease of separating distance from 3 to 1 cm. On the other hand the corrosion rate decreases with the increases of anode : cathode area ratio from 1:1 to 2:1. In this series of experiments the effect of the type of cathode (SS.304 , SS.316) on the corrosion rate of carbon steel anode in sodium chloride solution was studied , the first set of experiments was carried out using stainless steel 304 as a cathode ,while in the second set of experiments stainless steel 316 was used as a cathode ,the results indicates that under the given condition ,the corrosion rate of carbon steel nodes in the case of using of SS.316 as a cathode was higher than that obtained in the case of using SS.304 as a cathode (figure 26).

6. Conclusion From the obtained results of the studies of the effect of different operating conditions the galvanic corrosion of the carbon steel in sodium chloride solutions the following conclusions can be drawn : 1. The rate of corrosion of carbon steel anodes (mpy) greatly depends on the operating conditions of the experiments . 2. The lowest corrosion rates of carbon steel anodes were obtained in solutions with low and high concentrations of sodium chloride (20, 60,80 g/L ). On the other hand the highest corrosion rate is obtained in NaCl solution of 40 g/L . 3. The lowest corrosion rates of carbon steel in NaCl solutions were obtained at low (20 C˚) and high (80 C˚) temperatures . 4. The lowest corrosion rates of carbon steel anodes were obtained at long exposure time ,high temperature and high concentrations of NaCl . 43

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5. The increase of separating distance between anode and cathode greatly decreases the corrosion rate of carbon steel in NaCl solution. 6. The increase of anode : cathode area ratio greatly decreases the corrosion rate of carbon steel in NaCl solution . 7. The narrow the difference between the galvanic potentials of anodes and cathodes the lower the corrosion rate was obtained .

4. References [1]

[2] [3] [4] [5]

44

Corrosion: Fundamentals, Testing, and Protection was published in 2003 as Volume 13A of the ASM Handbook. The Volume was prepared under the direction of the ASM Handbook Committee. Materials engineering 1 selecting materials for process equipment. Edited by Kenneth j . McNaughton and the staff of chemical engineering. Fontana,M,G. "Corrosion Engineering" New York, 1967. J.X. Jia and others, “Simulation of galvanic corrosion of magnesium coupled to a steel fastener in NaCl solution,” Materials and Corrosion, vol. 56, no. 7, 2005. K.B. Despade, “Validated numerical modelling of galvanic corrosion for couples: Magnesium alloy (AE44)-mild steel and AE44-aluminium alloy (AA6063) in brine solution,” Corrosion Science, vol. 52, pp 3514–3522, 2010.

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Study of Galvanic Corrosion of Carbon Steel ……

Test period TC˚

2h

W W1

4h

6h

W-W1

mpy

W2

W-W2

mpy

W3

W-W3

mpy

20

28.3475 28.3424

0.0051

0.0343

28.3364

0.0111

0.0373

28.3243

0.0232

0.052

40

27.4555 27.4445

0.011

0.0748

27.4322

0.0233

0.0812

27.4167

0.0388

0.0879

60

27.5900 27.5767

0.0133

0.0911

27.5620

0.028

0.0959

27.5478

0.0413

0.0963

80

27.1380 27.1246

0.0134

0.0938

27.1140

0.024

0.0840

27.1080

0.0300

0.0702

Appendix Table 1. carbon steel versus SS.304 

Concentration "NaCl"40g/L Test period

TC˚

2h

W

4h

6h

W1

W-W1

mpy

W2

W-W2

mpy

W3

W-W3

mpy

20

26.8905

26.8860

0.0045

0.0309

26.8799

0.0106

0.0364

26.8740

0.0165

0.0378

40

27.1370

27.1300

0.007

0.048

27.1214

0.0156

0.0535

27.1125

0.0245

0.0560

60

26.2620

26.2500

0.012

0.0829

26.2360

0.026

0.0898

26.2225

0.0395

0.091

80

28.3430

26.3295

0.0135

0.0899

26.3205

0.0225

0.0749

26.3140

0.029

0.0645

 

Anode to cathode area ratio 1:1 The distance between the anode and the cathode 1cm

Table 2. carbon steel versus SS.304   

45

Concentration "NaCl"40g/L Anode to cathode area ratio 1:1 The distance between the anode and the cathode 3cm

Vol. 7 (1), 33–49, June 2017

Musbah M. Abomadina

Test period TC˚

2h

W W1

W-W1

4h mpy

W2

W-W2

6h mpy

W3

W-W3

mpy

20

28.5028 28.5980 0.0048

0.0328

28.5910 0.0118

0.0403

28.5840 0.0188

0.0428

40

28.8530 28.8450 0.008

0.0541

28.8335 0.0195

0.066

28.8204 0.0326

0.0735

60

27.9896 27.9780 0.0116

0.0799

27.9660 0.0236

0.0813

27.9530 0.0366

0.0841

80

25.9225 25.9140 0.0085

0.0612

25.9076 0.0149

0.0536

25.90

0.054

0.0225

Table 3. carbon steel versus SS.304   

Concentration "NaCl"40g/L Anode to cathode area ratio 2:1 The distance between the anode and the cathode 1cm

Table 4 . carbon steel versus SS.304   

46

Concentration "NaCl"40g/L Anode to cathode area ratio 2:1 The distance between the anode and the cathode 3cm

Vol. 7 (1), 33–49, June 2017

Study of Galvanic Corrosion of Carbon Steel ……

Table 5. carbon steel versus SS.304   

Concentration "NaCl"50g/L Anode to cathode area ratio 1:1 The distance between the anode and the cathode 1cm Test period

T C˚

2h

W

4h

6h

W1

W-W1

mpy

W2

W-W2

mpy

W3

W-W3

mpy

20

28.4140

28.4098

0.0042

0.0288

28.4045

0.0095

0.0326

28.3988

0.0152

0.0348

40

25.9493

25.9450

0.0043

0.0312

25.9398

0.0095

0.0338

25.9347

0.0146

0.0353

60

28.8700

28.8600

0.0100

0.0681

28.8495

0.0205

0.0698

28.8440

0.0026

0.0590

80

28.7162

28.7100

0.0062

0.0420

28.7060

0.0102

0.0345

28.7050

0.0112

0.0253

Table 6. carbon steel versus SS.304 Concentration "NaCl"50g/L  Anode to cathode area ratio 1:1  The distance between the anode and the cathode 3cm Test period T C˚ TC˚

Test period 4h 4h

2h 2h

W W W1 W1

W-W1 W-W1

mpy mpy

W2 W2

W-W2 W-W2

6h 6h mpy mpy

W3 W3

W-W3 W-W3

mpy mpy

20 20

29.9170 27.7470 29.9120 0.003 0.005 27.75

0.0344 0.0212

29.9065 27.7438 0.0105 0.0062

0.0361 0.0219

29.9000 0.0094 0.017 27.7406

0.0390 0.0221

40 40

29.9710 27.7784 29.9605 0.0031 0.0105 27.7815

0.0723 0.0219

29.9495 0.0063 0.0215 27.7752

0.0741 0.0222

29.9380 0.0095 0.033 27.7720

0.0758 0.0224

60 60

29.6360 27.3580 29.6235 0.0035 0.0125 27.3615

0.0861 0.0247

29.6098 0.0073 0.0262 27.3542

0.0903 0.0258

29.596 0.0115 0.04 27.3500

0.0919 0.0271

80

29.7886 27.4191 29.7765 0.0032 0.0121 27.4223

0.0834 0.0226

29.7700 0.0056 0.0186 27.4167

0.0641 0.0198

29.7650 0.0078 0.0236 27.4145

0.0542 0.0183

47

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Table 7 .carbon steel versus SS.304  

Concentration "NaCl"80g/L Anode to cathode area ratio 1:1 Test period

TC˚

2h

W W1

W-W1

4h mpy

W2

W-W2

6h mpy

W3

W-W3

mpy

20

29.1100 29.1050 0.005

0.0344

29.0994 0.0106

0.0365

29.0945 0.0155

0.0356

40

29.4050 29.3980 0.007

0.0482

29.3895 0.0155

0.0534

29.3807 0.0243

0.0558

60

29.9867 29.9760 0.0107

0.0737

29.9645 0.0222

0.0765

29.9527 0.034

0.0781

80

29.7350 29.7255 0.0095

0.0654

29.7180 0.017

0.0586

29.7130 0.022

0.0505



The distance between the anode and the cathode 1cm

Table 8. carbon steel versus SS.304   

Concentration "NaCl"80g/L Anode to cathode area ratio 1:1 The distance between the anode and the cathode 3cm Test period

TC˚

2h

W W1

W-W1

4h mpy

W2

W-W2

6h mpy

W3

W-W3

mpy

20

28.7715 28.7685 0.003

0.0206

28.7652 0.0063

0.0217

28.7625 0.009

0.0206

40

28.8680 28.8624 0.0031

0.0213

28.859

0.0224

28.8563 0.009

0.0211

60

29.2070 29.2038 0.0032

0.022

29.2003 0.0067

0.023

29.1970 0.0096

0.0229

80

29.0576 29.0545 0.003

0.0213

29.0520 0.0056

0.0193

29.0509 0.0067

0.0153

48

0.0065

Vol. 7 (1), 33–49, June 2017

Study of Galvanic Corrosion of Carbon Steel …… Table 9 . carbon steel versus SS.316   

Concentration "NaCl"40g/L Anode to cathode area ratio 1:1 The distance between the anode and the cathode 1cm Test period

TC˚

2h

W W1

W-W1

4h mpy

W2

W-W2

6h mpy

W3

W-W3

20

27.2100 27.2050 0.005

0.0353

27.1993 0.0107

0.0378

27.187

40

27.8380 27.8270 0.0110

0.0776

27.8150 0.023

0.0813

27.8005 0.0375

0.0883

60

26.942

0.0912

26.9155 0.0265

0.0937

26.900

0.0989

80

27.6600 27.647

0.0919

27.6353 0.0245

0.0866

27.6265 0.0335

26.9291 0.0129 0.013

0.023

mpy

0.042

0.0541

0.0790

Table 10. carbon steel versus SS.316   

Concentration "NaCl"40g/L Anode to cathode area ratio 1:1 The distance between the anode and the cathode 3cm Test period

TC˚

2h

W W1

W-W1

4h mpy

W2

W-W2

6h mpy

W3

W-W3

mpy

20

29.7040 29.6995 0.0045

0.0318

29.6935 0.0105

0.0371

29.6880

0.016

0.0377

40

27.8665 27.8590 0.0075

0.053

27.8505 0.016

0.0565

27.8418

0.0247

0.0582

60

27.851

0.0799

27.828

0.023

0.0813

27.8162

0.0348

0.082

80

27.8505 27.9395 0.011

0.0778

27.8295 0.021

0.0742

27.8227 0.0278

49

27.8397 0.0113

0.0655

Vol. 7 (1), 33–49, June 2017

Multiple Performance Measures and Organisational …… Sirte University Scientific Journal (Applied Sciences)

Vol. 7 (1), 51–78, June 2017

Multiple Performance Measures and Organisational Performance in an Emerging Market Abdallah M. Amhalhal Faculty of Economics, Sirte University, Libya E-mail: [email protected] Abstract The performance measurement diversity approach suggests that organisations attain superior performance when they place greater emphasis on a broad set of financial (FPMs) and nonfinancial performance measures (NFPMs). This study is an empirical investigation of the relationship between multiple performance measures (MPMs) and organizational performance (OP) in a Libyan context. Cross-sectional questionnaire survey data was obtained from 132 Libyan companies (response rate of 61%). The results indicate that MPMs are commonly used by both manufacturing and non-manufacturing Libyan companies. However, these companies still rely heavily on financial performance measures. The relationships between NFPMs and OP, and MPMs and OP are positive and highly significant. The relationship between FPMs and OP is positive but not significant. Keywords: Organisational Performance, Measurement Diversity, Performance Measures.

1.

Introduction

The ability of organisations to compete successfully depends to a significant extent on the availability and adequacy of information which enables managers to act effectively. Information that is used for planning and controlling business activities is provided mainly by performance measurement systems (PMSs). Therefore, the design, implementation and use of appropriate PMS is one of the major challenges confronting organisations (Santos et al., 2002).

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Neely et al. (1995) suggest that a PMS represents a set of metrics used to quantify the efficiency and effectiveness of actions and that these metrics can be financial or non-financial, short or long term, internal or external. The most common typology is a division into financial performance measures (FPMs) and non-financial performance measures (NFPMs). The move from so-called “conventional, traditional or financial” measures to more wide-ranging “nonfinancial, innovative, integrated, balanced or multiple” measures has been the key development in the performance measurement field (Eccles, 1991). In this context, PMSs evolved “from a characterisation based on the measuring and control of costs to one based on measuring the creation of value and thus on non-cost performance” (De Toni and Tonchia 2001, p.47). Conventionally, all kinds of organisations have measured and evaluated their performance (or effectiveness) by using financial measures which are derived from pure accounting systems (e.g. return on investment, net earnings, sales or cost measures) (Eccles, 1991). However, concerns have been expressed about the sole use of accounting performance measures (Kaplan, 1983; Johnson and Kaplan, 1987; Dixon et al., 1990; Eccles, 1991; Lynch and Cross 1991; Ghalayini and Noble, 1996; Ittner and Larcker, 1998; Neely, 1999; Bourne et al., 2000; Ittner et al., 2003; Jusoh et al., 2008). One reason for their concern is that (traditional) financial performance measures have a number of shortcomings and limitations. In particular, as, they are “too historical and backward-looking, lack predictive ability to explain future performance, reward short-term or incorrect behaviour, provide little information on root causes or solutions to problems, and give inadequate consideration to difficult to quantify intangible assets such as intellectual capital” (Ittner et al. 2003, p. 717). The criticisms about excessive reliance on financial performance measures, changes in the business environment, intensity of competition, and growing improvement initiatives in manufacturing (e.g. TQM approaches, JIT strategies) are the key issues which have forced organisations to develop their PMSs, and resulted in the creation of innovative performance measures which are named multiple/integrated performance measurement systems (Ittner and Larcker, 1998; Neely, 1999; Hoque et al., 2001; Van der Stede et al., 2006; Jusoh and Parnell, 2008; Verbeeten and Boons, 2009). This approach is based on supplementing financial metrics 52

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with a diverse set of non-financial performance measures. It is suggested that NFPMs and FPMs should both shape the PMS of organisations (Kaplan and Norton, 1992, 1996; Said et al., 2003; Jusoh and Parnell, 2008, p.8). Timely information concentrating on the causes and drivers of success can be obtained by NFPMs since NFPMs often place weight on the concept of lagging and leading performance drivers (Luft and Shields, 2002; Thorne, 1995; Banker et al., 2000). This means that NFPMs may be leading indicators of issues which will eventually influence financial performance. Therefore, they can be valuable and helpful in predicting organisations’ future financial performance1 because these measures encompass forward-looking information about performance which is missed by financial indicators (Kaplan and Norton, 1996; Ittner and Larcker, 1998; Banker et al., 2000). NFPMs are also key indicators of intangible assets (e.g. intellectual capital) and major drivers of organisational value (Kaplan and Norton, 1996; Ittner and Larcker, 1998; Jusoh et al., 2008; Jusoh and Parnell, 2008). However, the key characteristics of effective performance systems remains unclear. An effective performance measurement system may be based on using a balanced set of key financial and non-financial critical success factors and key performance indicators which stimulate involvement in continuous improvement. (Geanuracos and Meiklejohn, 1993). Therefore, organisational performance may depend on the diversity of performance measures used. This means that a company may achieve superior performance when it can place a greater emphasis on a broad set of financial and non-financial performance measures (Ittner et al., 2003; Van der Stede et al., 2006). However, although multiple performance measures (such as quality, productivity, innovation and customer satisfaction) have received a lot of attention from practitioners and academics since the early 1990s, many empirical studies have failed to provide clear evidence about the effectiveness of these measures, particularly in emerging market contexts. For example, some argue that the use of multiple performance measurement systems 1

This argument is based on cause-and-effect relationships, where managerial actions lead to outcomes such as quality, innovation and customer satisfaction that, in turn, drive future financial performance (Banker et al., 2000).

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(e.g. BSC) does not make any difference to business performance (Neely, 2008). Additionally, several studies reported that the relationship between multiple measures and organisational performance is unclear (Henri, 2004, 2006; Jusoh et al., 2008). Therefore, this paper seeks to identify the effectiveness of multiple performance measures in an emerging market. More specifically, the focus of this study is concerned with the extent of the use of MPMs in a Libyan setting and the relationship between multiple measurement techniques and organisational performance. It might be expected that in an emerging market, organisations may be likely to be less aware of and less likely to use NFPMs than in developed economies. The next section outlines the literature and hypotheses development. The research method applied is described in section 3. Section 4 introduces the study findings and discussion. The conclusion and limitations appear in the final section.

2.

Literature and Hypotheses Development

The first aim of this paper is to evaluate the type of financial and non-financial performance measures adopted by Libyan companies operating in both manufacturing and nonmanufacturing sectors. A number of studies have addressed this issue in other geographical contexts (Paladino, 2000; Kald and Nilsson, 2000; Ismail, 2007; Jusoh et al., 2008; Neely, 2008; Yongvanich and Guthrie, 2009). Neely (2008) stated that by 2001 the balanced scorecard model had been adopted by 44% of organisations worldwide (57% in the UK, 46% in the US and 26% in Germany and Austria). A survey of 225 listed Chinese firms found that only 39% of these firms had adopted some form of MPMs (Chow et al., 2007). Ismail (2007) found evidence that 150 Egyptian firms relied on both financial and non-financial measures of performance evaluation. Profit margin was the most commonly used financial performance measure, whereas customer satisfaction was the most commonly used non-financial performance measure. The BSC was used widely in the Egyptian companies surveyed, but the level of use of multidimensional indicators was low. The most significant obstacle to the adoption of BSC was the inadequacy of information systems. Jusoh et al. (2008, p. 120) found that approximately 30% of 54

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Multiple Performance Measures and Organisational ……

the Malaysian manufacturing companies surveyed had adopted the BSC as a performance measurement system, either wholly or partially. In a survey of 126 Thai manufacturing and service firms, Yongvanich and Guthrie (2009) found that the extent of BSC use did not vary significantly between different types of use of BSC. They also found that the extent and manner of BSC use was not associated significantly with organisational performance. Moreover, there were no significant differences in the satisfaction and perceived benefits gained from using different types of BSC. The majority of these studies have been conducted in a manufacturing setting and in developed countries, specifically in the USA, UK and Australia. Only a few of them have been conducted in emerging economies (Ismail, 2007; Jusoh et al., 2008; Yongvanich and Guthrie, 2009). The use of financial measures are still of great importance to most companies in both developing and developed countries (Bryant et al., 2004; Gosselin, 2005; Ismail, 2007; Jusoh et al., 2008; Neely, 2008; Fakhri, 2010; Al Sawalqa, 2011). The use of multiple performance measurement systems remains uneven, particularly in emerging market contexts (Paladino, 2000; Kald and Nilsson, 2000). Due to the descriptive nature of the current part of this research, specific hypotheses were not developed. Rather, the following questions were posed: -

What is the state of the multiple performance measures adopted by Libyan companies?

-

Do Libyan companies still place a greater emphasis on using traditional (financial) measures, rather than MPMs, in evaluating their performance?

The results of the empirical studies which have focused implicitly or explicitly on the measurement diversity approach-performance relationship, are inconsistent. This may be due to the variation in the design and ways in which these multiple measures are used. Although there is widespread interest in diverse performance measurement systems (e.g. BSC); so far few empirical studies have looked directly at the effectiveness of MPMs’ usage (i.e. how these measures are used) (Davis and Albright, 2004; Jusoh et al., 2008). Indeed, the association between non-financial measures and organisational performance has been found to be unclear to date Ittner and Larcker, 2001, Henri 2004, 2006; Jusoh et al., 2008).

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A number of studies have found a positive relationship between the use of multiple performance measures and organisational performance (Banker et al., 2000; Hoque and James, 2000; Davis and Albright, 2004; Bryant et al., 2004; Van der Stede et al., 2006; Jusoh et al., 2008; Fleming et al., 2009). Using time-series data covering 72 months for 18 US hotels and interviews with their senior managers, Banker et al. (2000) found that current customer satisfaction is significantly and positively related to future financial performance. When non-financial measures were included in the compensation contract, managers aligned their efforts more closely to those measures, resulting in improved organisational performance. Survey data from 66 Australian manufacturing firms indicated a significantly positive relationship between the use of typical BSC measures and firm performance (Hoque and James, 2000). A quasiexperiment (longitudinal approach) conducted by Davis and Albright (2004) compared the performance of branches in US banks (BSC user and non-BSC user). The findings indicated that four branches implemented the BSC and that the remaining five were non-BSC branches. The study also found the branches which had implemented the BSC approach outperformed the branches which had not. Based on archival data from 125 US companies, Bryant et al. (2004) found that when companies implement a multiple performance measurement system which included both financial and non-financial measures, they benefited more than those companies which relied only on traditional accounting-based measures. Van der Stede et al. (2006) investigated the MPMs-firm performance relationship in 128 US and European manufacturing companies. They found that, regardless of strategy, companies which adopted multiple performance measurement systems, particularly those which included objective and subjective non-financial measures, had superior organisational performance. A survey of 120 Malaysian companies in various manufacturing industries found that most Malaysian companies used mainly financial measures based on accounting measures (Jusoh et al., 2008). Further, they found that non-financial measures and MPMs (via overall BSC measures) were both associated positively with organisational performance. Fleming et al. (2009) investigated the firm performance effect of MPMs’ usage using archival and survey data for 104 Chinese manufacturing companies. They 56

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found that greater use of balanced/integrated PMSs by sample firms increased their strategic performance. However, Anderson et al. (1997), Ittner and Larcker (1998), Ittner et al. (2003), Said et al. (2003), Braam and Nijssen (2004), Neely (2008) and Schulz et al. (2010) have identified contradictory evidence. Using cross-sectional annual data for 77 Swedish firms from diverse industries, Anderson et al. (1997) indicated positive contemporaneous associations between customer satisfaction and financial performance (measured by ROI) in Swedish manufacturing organisations, but negative or weaker associations in service organisations. By employing a quasi-experimental design, Neely (2008) collected financial data from two sister divisions of a UK electrical wholesale chain, one of which had adopted the BSC and one of which had not. The findings suggest that the BSC implemented in the electrical wholesale industry had no significant impact in terms of sales growth or gross profit growth over a twelve month period. Ittner and Larcker (1998) looked at the relationship between customer satisfaction and organisational performance in American telecommunications companies, using cross-sectional data. The found modest support for the argument that customer satisfaction measures were leading indicators of accounting performance. However, their analysis of business unit-level data suggested that customer satisfaction measures are positively related to future financial performance. By contrast, their firm-level analysis did not find consistent associations between customer satisfaction and market returns. Ittner et al. (2003) explored the relationship between measurement diversity and performance (measured by satisfaction and economic measures) using survey and archival data for 140 American financial services institutions. However, while they found a positive relationship between MPMs’ usage and system satisfaction, they failed to find a significant relationship between the extensive use of measurement diversity techniques and improved accounting and stock market performance. Schulz et al. (2010) did not find a significant bivariate correlation between the use of comprehensive PMSs and organisational performance using data for 84 Taiwanese high-tech manufacturing companies. On the other hand, Said et al. (2003) used archival data for 2882 manufacturing and service firms in the UK to investigate the relationship between the use of non-financial measures and economic performance. The results reported that firms which use diverse measurement techniques had 57

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significantly higher mean levels of future returns on assets and higher levels of current and future market returns. This means that they found evidence that the adoption of non-financial measures improves firms’ current and future stock market performance. By contrast, they found only partial support for accounting performance improvements. They concluded that the association between measurement diversity and firm performance is contingent on the company’s operational and competitive characteristics. In summary, it is far from clear that there is a positive association between the use of MPMs and organisational performance. Some researchers have found convincing evidence of a positive relationship between both variables. In contrast, others have found that the use of performance measurement diversity might not be associated with enhanced organisational performance. As a result, the second aim of this research is to re-investigate the relationship between the use of multiple performance measures and organisational performance. Therefore, the following hypotheses were developed: -

H1 Organisational performance is negatively associated with the extent of traditional (financial) performance measures usage. H2 Organisational performance is positively associated with the extent of non-financial performance measures usage. H3 Organisational performance is positively associated with the extent of multiple performance measures usage.

3.

Research Method

3.1

Sample and Research Strategy

The population of this research is defined as all Libyan companies, manufacturing and nonmanufacturing, whether small, medium or large, except for: new companies with little experience (less than three years of age, because the respondents were asked to describe selected research variables during the previous three years) and very small companies (less than 10 employees). Earlier studies indicate that the use of management accounting and financial performance measures within small companies is generally very low (e.g. Hoque and James, 58

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Multiple Performance Measures and Organisational ……

2000; Hussain and Hoque, 2002; Chenhall, 2003; Burgess, et al. 2007; Verbeeten and Boons, 2009). Accordingly, the sampling frame included 226 Libyan companies in a variety of industries (76 manufacturing and 150 non-manufacturing). It is worth mentioning that only headquarters were included in order to obtain a more homogenous sample. Subsidiaries, divisions and branches were excluded. Primary data for the research was collected using a self-administered survey questionnaire. The questionnaire was divided into three main parts. All three parts included closed questions, i.e. all the questions had a range of potential answers and the respondents had to select one. The first part consisted of questions concerning general information about the characteristics of participants and their organisations. The second and third parts were concerned with the independent and dependent variables of the study. In these parts, the questions were based on a 5-point Likert scale. 226 questionnaires were distributed and 141 were returned. However, only 132 questionnaires were usable and valid for analysis (which represents a 61 % response rate). This is a good rate compared with other similar studies (e.g. Hoque, 2004; Mia and Winata, 2008; Salleh et al., 2010). The instrument was checked by a pilot study and a reliability test 2. An assessment of normality was performed for the dependent variable only (Field, 2005). The Kolmogorov-Smirnov test was used to evaluate the normality of the dependent variable (organisational performance). The findings confirm that the dependent variable follows a normal distribution3. In addition, and consistent with the literature, the questionnaire survey targeted finance directors, vice-managers, financial controllers and senior accountants because they are likely to be the people, who are responsible for designing and operating the performance measurement systems in their companies (Chenhall and Langfield-Smith, 1998; Verbeeten and Boons, 2009).

2

The results indicate that Cronbach’s alpha coefficients of all the variables were above the minimum acceptable level of 0.60: Multiple performance measures usage (0.919), Financial performance measures (0.767), Non-financial performance measures (0.939) and Organizational performance (0.800). 3 The Kolmogorov-Smirnov test reports the following results: Statistic (.078), df (.132) and Sig. (.059).

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3.2

Demographic Profiles of Respondents and Organisations

The first section of the questionnaire contained two questions about respondents and their organisations. This part of the survey aimed to give a brief description of demographic information about the profiles of respondents and the manufacturing and non-manufacturing companies participating in this study. Table 1 describes the general characteristics of respondents (qualifications, subject, work position and experience) which might affect the quality of their perceptions and their responses to the questionnaire’s questions and the interview schedule. It was essential to ensure that the respondents held senior positions and that they were sufficiently knowledgeable and experienced about organizational and environmental characteristics and MPMs. Thus, the participants were asked about their individual attributes. Table 1. Frequency Distribution of Characteristics of Respondents Items Job Title Financial Manager Vice-Financial Manager Controller Senior accountant Other Qualification Secondary Diploma Bachelor Post-graduate Other Subject Accounting Business Management Finance Economy Other

Items Less than 5 years 5-10 years

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Manufacturing Non-manufacturing (N=49) (N=83) Frequency Per cent Frequency Per cent 18 36.7 % 40 48.2 % 12 24.5 % 15 18.1 % 7 14.3 % 8 9.6 % 9 18.4 % 14 16.9 % 3 6.1 % 6 7.2 % Frequency Per cent Frequency Per cent 2 4.1 % 2 2.4 % 6 12.2 % 3 3.6 % 25 51 % 48 57.8 % 11 22.5 % 19 22.9 % 5 10.2 % 11 13.3 % Frequency Per cent Frequency Per cent 21 42.9 % 40 48.2 % 6 12.2 % 10 12.1 % 15 30.6 % 23 27.7 % 3 6.1 % 2 2.4 % 4 8.2 % 8 9.6 % Experience Experience Experience (in the Job) ( in the company) Frequency Per cent Frequency Per cent 18 13.6 % 14 10.6 % 33 25 % 36 27.3 %

Both (N=132) Frequency Per cent 58 43.9 % 27 20.5 % 15 11.4 % 23 17.4 % 9 6.8 % Frequency Per cent 4 3% 9 6.8 % 73 55.3 % 30 22.7 % 16 12.1 % Frequency Per cent 61 46.2 % 16 12.1 % 38 28.8 % 5 3.8 % 12 9.1 % Full experience Frequency Per cent 7 5.3 % 16 12.1 % Vol. 7 (1), 51–78 June 2017

Multiple Performance Measures and Organisational …… 10-15 years 15-20 years 20 years or more

41 23 17

31.1 % 17.4 % 12.9%

27 24 31

20.5 % 18.2 % 23.5 %

25 38 46

18.9 % 28.8 % 34.8%

Table 2 presents the key characteristics of respondent companies. It covers six main features: the age of the company, the main type of industry, company size (in terms of number of employees and annual revenue) and ownership type. Table 2. Frequency Distribution of Characteristics of Respondent Companies Items Company age Less than 5 years 5-10 years 10-15 years 15-20 years 20 years or more Type of Business Number of companies Number of Employees Less than 100 people 100-250 people 250-500 people 500-1000 people 1000 people or more Annual revenue/sales LD* Less than 1 million 1 m-5 m 5 m-10 m 10 m-15 m 15 million or more Type of ownership State-owned company Private company

Manufacturing Non-manufacturing (N=49) (N=83) Frequency Per cent Frequency Per cent 3 6.1 % 4 4.8 % 4 8.2 % 7 8.4 % 4 8.2 % 15 18.1 % 12 24.4 % 20 24.1% 26 53.1 % 37 44.6 % Frequency Percent Frequency Percent 49 37.1 % 83 62.9 % Company size (CS) Frequency Per cent Frequency Per cent 7 14.3 % 34 41 % 14 28.6 % 18 21.7 % 10 20.4 % 10 12 0% 3 6.1 % 10 12 0% 15 30.6 % 11 13.3 %

Both (N=132) Frequency Per cent 7 5.3 % 11 8.3 % 19 14.4 % 32 24.2 % 63 47.7 % Frequency Percent 132 100 % Frequency 41 32 20 13 26

Per cent 31.1 % 24.2 % 15.2 % 9.8 % 19.7 %

Frequency

Frequency

Per cent

Frequency

Per cent

8 16.3 % 31 17 34.7 % 28 5 10.2 % 12 3 6.1 % 4 16 32.7 % 8 Frequency Per cent Frequency 21 42.9 % 27 14 28.6 % 41 Joint-venture (State & foreign ) 3 6.1 % 5 Joint-venture (State & private) 6 12.2 % 4 Joint-venture (private & foreign) 5 10.2 % 6 * LD: Libyan Dinar. 2.11 LD equals 1 UK pound (Aug. 2012)

37.3 % 33.7 % 14.6 % 4.8 % 9.6 % Per cent 32.5 % 49.4% 6.0 % 4.8 % 7.3%

39 45 17 7 24 Frequency 48 55 8 10 11

29.5 % 34.1 % 12.9 % 5.3 % 18.2 % Per cent 36.4% 41.7 % 6.0 % 7.6 % 8.3 %

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3.3

Measurement of Variables

This section describes how the research variables were measured. It is worth noting that during the preparation of measures and constructs for the research variables, any terms or measures which were specific to a particular sector were excluded in order to make the questionnaire applicable to all sectors (manufacturing and non-manufacturing). The conceptual definitions of these variables are provided in the next sub-sections. Multiple performance measures’ usage (MPMs) refers to the extent to which directors utilise a broad scope of information, resulting from financial and non-financial measures, for assessing performance. This approach was spilt into five major categories which are commonly used by both manufacturing and service organisations. The first four categories were adapted from the work of Scott and Tiessen (1999), Hoque and James (2000), Hoque et al. (2001), Ittner et al. (2003), Bryant et al. (2004), Hoque (2004, 2005), Henri (2006), Van der Stede et al. (2006), Ismail (2007), Jusoh et al. (2008), Bento and White (2010), Salleh et al. (2010) and Jusoh (2010), which are based originally on the work of Kaplan and Norton (1992). The fifth category (community/environment perspective) was modified from the work of Zuriekat (2005), Youssef (2007), Yaghi (2007) and Fakhri (2010). The instrument includes 41 different measures 4. The respondents were requested to indicate on a five-point Likert-type scale ranging from 1 (not used at all) to 5 (used considerably), the extent of their organisation’s use of the identified performance measures over the previous three years. The extent of MPMs’ usage is the overall mean of responses for all the 41 measures indicated above. Organisational performance (OP) refers to the extent to which the organisation is successful in achieving its planned targets or stated aims (Mia and Clarke,1999). It is described as the ultimate outcome variable (dependent variable) in the contingency literature because it explains the implications of a suitable fit between control systems design and other organisational characteristics of a company. It was assessed by a self-rating multiple instrument. The scale included 13 items originally developed by Govindarajan (1984) and used in several previous

4

Firstly, the extent of FPMs usage is the overall mean of responses for the first 11 measures. Secondly, the other 30 measures were selected to measure NFPMs’ usage. Thirdly, the extent of MPMs usage is the overall mean of responses on all 41 measures.

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studies (Chong and Chong, 1997; Hoque, 2004, 2005; Van der Stede et al., 2006; Jusoh et al., 2008). Respondents were required to rate each of the 13 dimensions on a five-point Likert-type scale, ranging from 1 (poor) to 5 (outstanding), to assess their organisation’s performance compared to that of their main competitors over the previous three years. Organisational performance is the overall mean of responses for all items and the score for each organisation was calculated by taking the average for all items (Hoque, 2005; Jusoh et al., 2008).

4.

Results and Discussions

4.1

Descriptive Analysis of the Extent of MPMs Usage in a Libyan Context

This section focuses mainly on the descriptive statistics concerning the first objective (i.e. the status and extent of MPMs’ usage among Libyan companies), which were used primarily to achieve all the second research objectives. Respondents were asked to indicate, on a five-point scale ranging from 1 (not used at all) to 5 (used considerably), the extent to which their organisations had used financial and non-financial performance measures to evaluate business performance over the previous three years. Accordingly, the responses to a scale ranging from “not used at all” to “used considerably” with a neutral response of “used moderately” in the middle, generally, may be equivalent to providing a ‘yes’ or ‘no’ and a confident response (the strength or confidence of measurement in this scale is assessed as the distance away from the neutral response) (Youssef, 2007; Fakhri, 2010). In simple words, for the non-financial performance measures groups, a response to the first two options of the scale “not used at all” and “used slightly” may be equivalent to a negative response for measurement diversity and a yes response for traditional (financial) measurement, whereas a response to the last two options of the scale “used considerably” and “used significantly” may be equivalent to a yes response for measurement diversity and a negative response for traditional measurement. MPMs were operationalised through 41 items which were grouped into five perspectives; namely, financial measures, customer, internal business processes, innovation and learning and community/environment. These five categories are commonly used by both manufacturing and non-manufacturing organisations. This variable was defined as a continuum of two opposite ends; namely, “least used multiple performance measures” and “most used multiple 63

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performance measures”. Consequently, the extent of FPMs’ usage is the average standardised rating for (11) financial measures, NFPMs’ usage is the average standardised rating for (30) non-financial measures, and MPMs’ usage is the average standardised rating for all financial and non-financial measures across the five performance measurement categories. Table 3 summarises respondents’ opinions relating to the extent to which the 41 performance measures are used within Libyan companies across different industries. The results show that MPMs have widespread use in all Libyan companies across different industries; however, comparing the mean scores among performance measures indicates, as expected, that the extent of FPMs usage has a higher level (mean = 3.88) than NFPMs and MPMs, which have mean values of 3.52 and 3.62 respectively. These results are similar to the findings of most prior studies conducted in emerging market contexts (e.g. Hutaibat, 2005; Ismail, 2007; Youssef, 2007; Fakhri, 2010; Al Sawalqa, 2011) which found that many companies use MPMs (financial and non-financial) but to different extents. For example, Fakhri (2010) found that although Libyan banks used FPMs more extensively, they use a variety of NFPMs to ensure the accuracy and validity of their outputs. These results are also in line with the findings of previous studies conducted in some emerging markets contexts such as the UK, the USA and Australia (e.g. Bryant et al., 2004; Gosselin, 2005; Neely, 2008; Jusoh et al., 2008; Verbeeten and Boons, 2009; Jusoh, 2010) which concluded that most businesses continue to use FPMs extensively, i.e. organisations which use measurement diversity approaches (e.g. BSC) do not employ NFPMs more extensively than FPMs. Table 3. Descriptive Analysis of MPMs’ Usage in Libyan Companies Items Net income Revenue/sales growth ROI (Return on investment) ROA (Return on asset) ROE (Return on equity) ROS (Return on sales) Budgets Cash flows Earning per share (EPS)

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1 0.0 1.5 1.5 0.8 0.8 1.5 0.0 1.5 0.8

2 3.0 0.8 3.0 6.8 5.3 1.5 4.5 6.1 7.6

% (N = 132) 3 4 7.6 32.6 13.6 41.7 15.9 34.1 16.7 37.1 11.4 40.9 17.4 33.3 18.2 29.5 17.4 31.1 19.7 26.5

5 56.8 42.4 45.5 38.6 41.7 46.2 47.7 43.9 45.5

Mean

S.D

4.43 4.23 4.19 4.06 4.17 4.21 4.20 4.10 4.08

0.764 0.825 0.917 0.947 0.887 0.891 0.897 0.995 01.01

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Multiple Performance Measures and Organisational …… EVA (Economic value added) 25.0 27.3 23.5 12.9 11.4 Market value added (MVA) 34.1 23.5 19.7 14.4 8.3 Overall financial perspective-based performance measures Non-financial performance measures Safety 16.7 5.3 22.7 30.3 25.0 Cycle time/lead times (product/service) 16.7 6.1 21.2 34.1 22.0 Product/service development 7.6 12.9 22.0 26.5 31.1 Defects rate (product/service) 12.1 8.3 17.4 29.5 32.6 Product/service quality 8.3 9.1 22.0 33.3 27.3 Cost savings 10.6 12.9 14.4 32.6 29.5 Productivity 8.3 8.3 15.9 41.7 25.8 Overall internal operations perspective-based performance measures Market share 3.0 5.3 21.9 34.8 35.6 Customer satisfaction 2.3 3.8 12.9 48.5 32.6 Customer service 12.1 3.0 18.9 40.9 25.0 Number of customer compliances 5.3 11.4 17.4 37.9 28.0 Customer retention 3.8 5.3 23.5 34.1 33.3 Customer loyalty 14.4 8.3 20.5 30.3 26.5 Customer response time 9.8 6.8 20.5 43.9 18.9 On-time delivery (product/service) 6.1 4.5 22 38.6 28.8 Overall customer perspective-based performance measures Employee satisfaction 3.8 6.8 34.1 46.2 9.1 Employee loyalty 3.0 8.3 31.8 41.7 15.2 Skills development 4.5 7.6 32.6 37.1 18.2 Competitive position 5.3 7.6 31.1 41.7 14.4 Research and development activities 3.8 14.4 28.8 35.6 17.4 Employee training 6.1 10.6 25.8 35.6 22.0 Adapting to changes 6.1 8.3 33.3 36.4 15.9 New products/service innovation 6.8 9.8 26.5 33.3 23.5 Overall innovation and learning perspective-based performance measures Meeting environmental commitments 13.6 12.1 18.9 34.1 21.2 (environmentally friendly) Support of charity projects 16.7 19.7 28.8 15.9 18.9 Support of social activities 13.6 25.0 25.0 20.5 15.9 Community regulations 13.6 22.7 28.8 22.7 12.1 Government citations/certification 11.4 20.5 28.8 26.5 12.9 Participation in training and education 13.6 15.2 18.9 29.5 22.7 (Community involvement) Public image 8.3 14.4 12.1 27.3 37.9 Overall environmental and community perspective-based performance measures Overall

2.42 2.39 3.88

01.30 01.31 0.543

3.42 3.39 3.61 3.62 3.62 3.58 3.68 3.56 3.95 4.05 3.64 3.72 3.88 3.46 3.55 3.80 3.76 3.50 3.58 3.57 3.52 3.48 3.57 3.48 3.57 3.53

01.37 01.35 01.26 01.34 01.21 01.32 01.19 1.10 1.03 0.902 1.24 1.15 1.06 1.35 1.17 1.10 .819 0.895 0.950 1.02 1.01 1.06 1.13 1.05 1.15 .866

3.37

1.32

3.01 3.00 2.43 3.09

1.34 1.28 1.22 1.20

3.33

1.35

3.72 3.21

1.33 1.01

N Min Max Mean S.D Variables 1 Financial performance measures (FPMs) 132 2.00 4.91 3.88 0.543 2 Non-financial performance measures (NFPMs) 132 1.13 4.90 3.52 0.713 3 Multiple performance measures (overall 1 and 2) 132 1.83 4.68 3.62 0.551 1= Not used at all, 2 = Slightly used, 3 = Moderately used, 4 = Significantly used, 5 = Considerably used

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The results of descriptive statistics for all 11 FPMs show that except for the last two financial measures (EVA and MVA), all other financial measures were ranked as “used significantly” or “used considerably” by more than 70%5 of the participating companies, with means ranging from 4.06 to 4.43. As can also be seen in this table, EVA and MVA measures were not used frequently - they were the only financial measures to be used less than average (under “used moderately”, 3) among Libyan companies as they have mean scores of 2.42 and 2.39 respectively. A possible explanation for this is that, as preceding research has concluded, recently developed accounting measures, such as EVA, have been criticised by many researchers and practitioners as being complex and difficult to use and understand, costly and not superior to traditional accounting measures (e.g. Ittner and Larcker, 1998; Jusoh et al., 2008). These limitations may be the reason for the low usage of these measures among Libyan companies. Concerning non-financial measures, the descriptive statistics shown in Table 3 suggest that respondents ascribed the highest score to the usage of customer perspective-based PMs, followed by internal operations-based PMs and innovation and learning -based PMs, while environmental and community-based PMs were the least used by Libyan companies. Customer satisfaction was the most commonly used non-financial measure of performance evaluation. By contrast, the results infer that the community regulations-based measure was not a popularly used non-financial measure of performance evaluation; it was used by only 34.8% of the respondent companies with a mean of 2.43. This result was similar to that of Ismail (2007) who found evidence that customer satisfaction is the most commonly used non-financial performance measure in an Egyptian setting. One possible explanation for this is that the companies studied represent a sample of the emerging Libyan business environment most decision-makers in those organisations might be unaware of the importance of environmental and community-based measures in improving their companies’ performance.

5

To describe the levels of significance rates of all performance measurement groups (financial and non-financial), they were counted by the respondents’ answers for the equivalent answers of 4 and 5 in their companies.

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The findings shown in Table 3 indicate that the use of customer-based PMs is quite common among Libyan companies (mean = 3.76). They indicate that market share and customer satisfaction are measures commonly used by Libyan companies. Both customer retention measures and on-time delivery (product/service) measures were ranked as “used significantly” or “used considerably” by 67.4% of the sample companies. Furthermore, a number of customer compliances and customer service levels6 were ranked by 65.9% of the participating companies, while customer loyalty and customer response time seem to be used to a moderate extent as they were ranked as “used Significantly” or “used Considerably” by 62.8% and 56.8% of the respondent companies. These results are in line with Jusoh et al. (2008) who found that the use of customer measures such as on-time delivery, survey of customer satisfaction and number of customer complaints was high among Malaysian manufacturing companies. Similar results were found in other studies by Hoque et al. (2001) and Gosselin (2005). It can be seen from Table 3 that Libyan companies place a similar emphasis on the use of use both internal business process-based PMs (mean = 3.56) and innovation and learning-based PMs (mean = 3.53). For the first category, productivity was at the top of the list because it was ranked as “used Significantly” or “used Considerably” by 67.5% of respondents. There were also two measures - cost savings and defects rate of product/service - which were ranked by a similar percentage (62.1%) of the participating companies. Other measures, such as product/service

quality,

product/service

development,

safety,

cycle

time/lead

times

(product/service) were ranked as “used Significantly” or “used Considerably” by 60.6%, 57.6%, 55.3% and 56.1% respectively. Innovation and learning-based PMs appear to be used to a moderate extent as they all were ranked as “used Significantly” or “used Considerably” by between 57.6% and 52.3% of the respondent companies. Finally, the results indicate that environment and community-based PMs are the least used measures among Libyan companies compared to the other four types of PM. Public image was ranked first among these measures - being reported by 65.2% of respondents as “used Significantly” or “used Considerably”. The findings indicate that 52.2% of the respondent 6

To describe the levels of significance rates of all performance measurement groups (financial and non-financial), they were

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companies use environmental commitment-based PMs and 55.3% of them use community involvement-based PMs, whereas measures based on support of charity projects, support of social activities and government citations perspectives were at the bottom of this list as they were ranked as “used Significantly” or “used Considerably” by only 34.8%, 36.4% and 39.4% respectively. By contrast, community regulations-based PMs were not commonly used by Libyan companies since they have a usage rate of only 34.8%. To sum up, MPMs are commonly used by Libyan companies. This suggests that Libyan companies are like other organisations around the world in which financial and non-financial measures are commonly used; however, they tend to place a greater emphasis on traditional (financial) measures (mean = 3.88) much more than multiple measures (3.62), in evaluating their performance, although organisations are aware of the benefits and importance of measurement diversity techniques in serving their needs and purposes. A possible explanation for the above result is that the implementation of innovative information systems and techniques (ABC, BSC, etc.) is difficult in developing countries due to the lack of infrastructure (Peasuell, 1993). Overall, these descriptive results similar to the findings of most earlier studies conducted in both developing and developed contexts (e.g. Gosselin, 2005; Bryant et al., 2005; Ismail, 2007; Neely, 2008; Jusoh et al., 2008; Fakhri, 2010; Jusoh, 2010; Al Sawalqa, 2011). 4.2

Testing the relationship between MPMs’ usage and organizational performance

This section deals with the testing of the three hypotheses of the research (H1-H2-H3). The statistical technique employed for testing these hypotheses was simple regression analysis. This section seeks to assess the nature and type of direct relationships between the use of financial performance measures, non-financial performance measures, multiple performance measures, and company performance. As can be seen, traditional (financial) performance measures (FPMs), non-financial performance measures (NFPMs), and multiple performance measures (MPMs) were employed as independent variables (predictors), with organisational performance (OP) as a dependent counted by the respondents’ answers for the equivalent answers of 4 and 5 in their companies.

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variable in all three models respectively. Table 4 presents the regression analysis-based statistical findings concerning these hypotheses (H1-H2-H3), which predict a direct relationship between FPMs, NFPMs, MPMs and organisational performance respectively. As can be seen in Table 4, the regression results indicate that the effect of FPMs on organisational performance was positive; however, it is not statistically significant (R² = .011, β = .107, p ˃ .05). On the other hand, the impacts of both NFPMs and MPMs on organisational performance are positive and statistically highly significant (R² = .218, β = .467, p < .05; R² = .222, β = .471, p < .05 respectively). Therefore, FPMs’ usage has no significant effect on organisational performance. This confirms that relying solely on FPMs is not sufficient for enhancing company performance. Hypothesis H1 was not supported at the .05 significance level; therefore, it is rejected. It can also be concluded that the use of non-financial measures has a significant impact on organisational performance, i.e. the use of NFPMs significantly improves the ability to predict (self-rating) organisational performance. Hypothesis H2 was supported at the .05 significance level; therefore, it is accepted. It is clear from the results above that MPMs introduce valuable diverse information which contributes to improving business performance. This suggests that the more extensively multiple performance measures (financial and nonfinancial measures) are used, the better the organisational performance. Hypothesis H3 was supported at the .05 significance level; therefore, it is accepted. Table 4. Relationship between MPMs’ Usage and Organisational Performance Dependent variable (Organisational performance ) Unstand. coefficient Stand. coefficient t-value B Std. Error Beta FPMs’ usage .110 .090 .107 1.223 R = .107, R² = .011, Adjusted R² = .004 , F-value = 1.496, Sig. = .223 NFPMs’ usage .365 .061 .467 6.022 R = .467, R² = .218, Adjusted R² = .212 , F-value = 36.26, Sig. = 000 MPMs’ usage (overall) .477 .078 .471 6.083 R = .471, R² = .222, Adjusted R² = .216 , F-value = 37.000, Sig. = 000 Variable (Predictors)

Sig. .223 .000 .000

The findings reveal that the use of FPMs has no significant impact on the performance of Libyan organisations (H1). This result is in line with most previous research (e.g. Ittner et al., 2003; Van der Stede et al., 2006; Jusoh et al., 2008). Using financial measures alone is not sufficient (Jusoh et al., 2008). Therefore, this research hypothesised that using FPMs alone in 69

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the Libyan business environment can influence negatively organisational performance. However, this does not imply that FPMs are not important. In this context, most authors (e.g. Kaplan and Norton, 1992; Neely, 1999) contend that FPMs are still crucial in assessing performance in any organisation, as they are necessary in order to track revenue, profit and costs. Henri (2004) argued that using NFPMs does not suggest that non-financial measures have to replace FPMs. Instead, it means supplementing FPMs with a diverse set of NFPMs that are believed to provide better information and contribute to improving organisational performance. This can be noted in the results for H2 and H3, where the performance effect of the usage of both NFPMs and MPMs was positive and significant. One explanation for the positive results regarding the NFPMs-OP relationship (H2) is that the NFPMs are future-oriented measures. Hence, top management tries to rely heavily on these measures in making decisions that will be useful to their organisations in the long run (Ghalayini and Noble, 1996; Chenhall and Langfield-Smith, 2007). This significant result is in line with the findings of Ittner and Larcker (2003) and Hoque (2004) who found that the extent of NFPMs usage is positively associated with performance. Archival data from 2882 UK manufacturing and service organisations revealed that the use of non-financial measures improved organisations' current and future stock market performance. These measures were also significantly and positively associated with organisations’ future accounting performance but not with their current accounting performance (Said et al., 2003). The significant and positive findings in relation of H3 are consistent with most previous research which find that the use of the combination of FPMs and NFPMs is positively associated with organisational performance (e.g. Govindarajan and Gupta, 1985; Hoque and James, 2000; Banker et al., 2000; Davis and Albright, 2004; Zuriekat, 2005; Van der Stede et al., 2006; Bryant et al., 2004; Jusoh et al., 2008; Yongvanich and Guthrie, 2009; Fleming et al., 2009; Zhu et al., 2009; Jusoh, 2010; Al-Sawalqa, 2011). For example, Davis and Albright (2004) compared the performance of a number of American banks implementing the BSC with those which were non-BSC users and they found that the branches which had implemented the BSC approach outperform branches which had not. Based on archival data from 125 US 70

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manufacturing and service companies, Bryant et al. (2004) found that when companies implement a multiple performance measurement system, which includes both financial and nonfinancial measures, they benefit more than those companies which rely only on (financial) traditional measures. According to Van der Stede et al. (2006), regardless of strategy, US and European manufacturing companies which have adopted multiple performance measurement systems, particularly those which include objective and subjective non-financial measures, have superior organisational performance. However, they also partly supported the view that the strategy-measurement ‘fit’ influences company performance, where there is a positive impact on performance from pairing a quality-based manufacturing strategy with extensive use of subjective measures, but not with objective non-financial measures. Using survey data for 120 manufacturing companies in Malaysia, Jusoh et al. (2008) reported that the use of non-financial measures, particularly internal business processes and innovation and learning measures, is associated with improved organisational performance. Based on archival and survey data from 104 Chinese manufacturing companies, Fleming et al. (2009) concluded that the greater use of balanced/integrated PMSs by sample companies improved their strategic performance. Al-Sawalqa (2011) found that the use of financial measures does not have a significant impact on organisational performance for 168 Jordanian industrial companies; by contrast, he found that using non-financial measures, a measurement diversity approach and the BSC contributed significantly towards improved organisational performance. On the other hand, our results in relation to H3 contrast with others who have found no evidence for the proposition which suggests that measurement diversity is positively associated with organisational performance (e.g. Anderson et al., 1997; Ittner and Larcker, 1998; Ittner et al., 2003; Braam and Nijssen, 2004; Hoque, 2005; Franco-Santos, 2007; Neely, 2008; Schulz et al., 2010). For example, Ittner et al. (2003) found significant evidence for 140 American financial services institutions that the extensive use of a broad set of financial and non-financial measures is associated with better stock market performance and system satisfaction, but not with improved accounting performance by the organisation. Neely (2008) found that BSC usage had no significant impact on performance in terms of sales growth or gross profit growth over a 71

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twelve month period. Similarly, studies by Hoque (2005) and Schulz et al. (2010) indicated no significant bivariate correlation between the use of MPMs and organisational performance. On the other hand, Braam and Nijssen (2004) concluded that the use of the BSC will not automatically enhance company performance, but that the manner of its use matters: BSC use which complements corporate strategy impacts positively on organisational performance, while BSC use which is not related to the strategy may reduce it. In the same context, a number of studies presented empirical evidence suggesting that the relationship between measurement diversity and organisational performance depends on contingency factors such as business strategy, uncertainty and organisational structure (e.g. Chong and Chong, 1997; Said et al. 2003; Hoque, 2004).

5.

Conclusions

This study employed 41 financial and non-financial measures in an attempt to identify the extent to which Libyan organisations use MPMs. The key descriptive results indicate that MPMs have widespread use in most Libyan companies across different industries; however, comparing the overall mean scores among performance measures indicates that the extent of FPMs’ usage has a higher level (mean = 3.88) than NFPMs’ (mean = 3.53) and MPMs’ (mean = 3.62) usage. Therefore, Libyan organisations using measurement diversity-based systems do not employ non-financial measures more extensively than traditional performance measures. In other words, the Libyan companies surveyed in this research, like many organisations around the world, tend to rely on traditional (financial) measures much more than multiple measures in evaluating their performance, although respondents were aware of the benefits and importance of measurement diversity techniques . These results are similar to the findings of previous studies conducted in emerging market contexts (e.g. Hutaibat, 2005; Ismail, 2007; Youssef, 2007; Fakhri, 2010; Al Sawalqa, 2011) and in some developed contexts (e.g. Bryant et al., 2004; Gosselin, 2005; Neely, 2008; Jusoh et al., 2008; Verbeeten and Boons, 2009; Jusoh, 2010), which concluded that many organisations apply MPMs (financial and non-financial measures); they also indicate that most companies continue to use predominantly financial 72

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performance measures. Furthermore, the results of the regression analysis indicate that NFPMs and MPMs have a significant positive effect on Libyan companies’ performance. However, this positive effect was not significant in the case of FPMs. Consequently, the results supported and accepted the hypotheses H2 and H3, while the hypothesis H1 was rejected. The study contributes to the body of literature looking at the practice of MPMs by investigating the extent to which 41 financial and non-financial measures are used in Libyan companies; this contributes to supporting or contradicting some of the prior theories regarding this theme. The research can therefore be used as a reference point for any future work in this field, particularly in emerging market contexts. Furthermore, this research highlights the importance and usefulness of measurement diversity in enhancing business performance. It thus contributes to the literature looking at the MPMs-organisational performance relationship by providing an empirical investigation which would present a better understanding of the core of MPMs and the effect of their usage on organisational performance. This work contributes also to the literature on organisational performance since many prior studies have defined organisational performance poorly by measuring this variable according to a single dimension only (i.e. financial indictors); this research has instead adopted a measurement diversity approach which includes both financial and non-financial dimensions in measuring the organisational performance of Libyan companies. The study has listed around 41 financial and non-financial measures to investigate the extent of their usage in a Libyan context; therefore, it provides a practical checklist of the measures which might assist Libyan companies in improving and developing suitable performance measurement systems to reach their strategic goals. Additionally, the findings indicate that Libyan companies should be encouraged to put a balanced emphasis on all measures, particularly non-financial measures (e.g. customer, employee, innovation and environmentbased measures) in order to enhance the loyalty of customers and attract new ones and serve other needs of stakeholders. Like any other research study, this study is subject to a number of limitations. These limitations might open new directions for future research. Firstly, this study did not investigate the impact 73

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of each category of the measurement diversity approach (e.g. customer measures, innovation measures, etc.) on organisational performance separately. Rather, it focused on the three main categories of the measurement diversity approach; namely, FPMs, NFPMs and MPMs. Therefore, future research should evaluate these individual relationships in order to gain a deeper understanding and provide explanations for these issues. Secondly, the evaluation of organisational performance by a self-rating scale is subject to criticism in terms of validity or reliability (Abernethy and Guthrie, 1994), although it has been widely used in previous studies. Thus, the search for adequate methods and manners (e.g. archival data, records) of tackling such issues could be an interesting avenue for further research. Thirdly, the current study adopted a cross-sectional questionnaire to investigate the cause and effect relationships between identified research variables via regression analysis. Future research could evaluate these causal relationships through longitudinal field research methods, and to find out whether the interactions among the contingencies, MPMs and performance are consistent over time. Finally, despite these limitations, this study has provided several important insights into issues relating to performance measurement systems. The research is a major contribution to the performance measurement and control literature, particularly in an emerging market context. As a result, this study may assist practitioners and researchers to generate ideas and issues for future research in similar contexts.

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[68] Verbeeten, F. H., and Boons, A. N. (2009). Strategic priorities, performance measures and performance: an empirical analysis in Dutch firms. European Management Journal, 27, 113-128. [69] Yaghi, B. (2007). The moderating effects of performance measurement use on the relationship between organizational performance measurement diversity and product innovation. Unpublished Ph.D Thesis, Cranfield University, UK. [70] Yongvanich, K., and Guthrie, J. (2009). Balanced Scorecard practices amongst Thai companies: performance effects. Pacific Accounting Review, 21(2), 132-149. [71] Youssef, A. E. (2007). The contingent factors that affect the use of performance measurement systems in Egyptian medium and large sized manufacturing companies. Unpublished Ph.D Thesis, University of Durham, UK. [72] Zuriekat, M. I. (2005). Performance measurement systems: an examination of the influence of the contextual factors and their impact on performance with a specific emphasis on the balanced scorecard approach. Unpublished Ph.D Thesis, University of Huddersfield, UK.

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Developments in Parabolic Solar Dish Concentrator …… Sirte University Scientific Journal (Applied Sciences)

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Developments in Parabolic Solar Dish Concentrator for Enhanced System Efficiency of Steam Generation Imhamed M. Saleh*, Khalifa Khalifa, Mohamed Bughazem and Nabil Algharbi Department of Mechanical, Faculty of Engineering, Sirte University, Sirte, Libya E-mail: [email protected]

*

Abstract In this paper, design and fabrication of the parabolic solar dish concentrator for the steam generation have been carried out. The experimental setup consists of the parabolic dish of solar concentrator system is fabricated with highly reflective mirror. The concentrated heat is absorbed by a copper tube which is made up of coil in a curved shape and it is fixed on solar trace path to obtain maximum solar energy. The black coating over the receiver surface to reduce the various losses it is located in the focal point on the solar ray’s concentration and its core application in steam granulation coil receiver, heat transfer fluid as water and it is going through the system. The present study concerning the development and design of the parabolic dish solar concentrator indicates a diameter of 2.4 meters to obtain a concentration ratio of 91.16×. The outdoor experimental of a parabolic dish concentrator has been tested. The performance of parabolic dish concentrator studied for different experimental conditions. The outdoor experimental has also been carried out to estimate outlet temperature. The maximum temperature of 635 °C has been recorded, when the flow rate is 0.4 kg / min.

Keywords: Solar Concentrator, Outdoor Testing, Coil Receiver, Dish Solar Concentrator.

1. Introduction Recent researches are concentrating on renewable energy technology; solar energy is a significant part of this technology. Where variety types of solar energy are used, thermal energy is a promising source with high efficiency. However, available heat flux per m2 is not enough for large projects, which lead to concentrating sun rays on a small area. The concentration of the sun rays is not a new technology. Ali [1-4] describes non-imaging optics as an area dedicated to designing of optical concentrators, where, instead of the using imaging systems, light collecting systems are utilized. O’Gallagher [5] confirms this fact, by “non-imaging optics is a new

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approach to the collection, concentration, and transport of light developed by physicists from the University of Chicago over the past 35 years.” A concentrator, as described by Winston, Miñano and Benítez [6] is, therefore, used to amplify the power density to be absorbed to extremely high levels so as to be able to provide sufficient energy for large-scale uses or generation of electric or motive power. The notion of a Hyperboloid concentrator originated in geometric optics and was later adopted in solar thermal energy. Winston, Miñano and Benítez [6] inform that whether the use of geometrical optics is for image forming purposes or not, it has been the main instrument used in the design nearly all optical systems. Lovegrove, Luzzi et al demonstrated the solar driven closed-loop thermos chemical energy storage system using ammonia of 20 m2 dish solar concentrator [7]. They have shown that the ammonia dissociation receiver/reactors are well suited for high-quality superheated steam production. Based on catalyst material, cavity receiver of 20 reactor tubes filled with iron based catalyst material was used in the system. They investigated the maximizing potential for electrical power production from ammonia synthesis reactor. Mills presented the various solar thermo-electric technologies [8]. Kennedy provided the extensive status of the material for solar reflectors [9]. The development, performance and durability of the solar reflectors have been discussed. The glass with silvered polymer and front-surface mirrors has shown an excellent candidate for solar reflectors. Klaib and Palavras and Bakos dealt with the development and performance characteristics of a low-cost dish solar concentrator and its application in zeolite desorption [10, 11] Hassib discussed the geometric analysis of the compound conical concentrator with receivers of various geometries [12]. The shape of the receiver determines the profile the reflector. It was also shown that the resulting flux distribution determines the shape of the receiver and its position relative to the reflector. El-Refaie described the conical solar energy concentrator with tubular axial absorber [13]. The current study presents the design and experimental analysis of a parabolic solar dish concentrator and coil receiver for the steam generation have been designed, fabricated and the outdoor performance test was also carried out for dish solar concentrator system of 91.16 × concentration ratios. The tests were carried out at SIRTE, LIBYA to obtain the maximum outlet 80

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temperature and daily performance of the dish solar concentrator system. In the daily performance test, the maximum fluid temperature of 635°C was observed.

2. Design of the Solar Parabolic Dish Concentrator 2.1. Concentrator The equation for the parabolic dish in profile coordinates (x , y) is: Y

(1)

where f is the focal length of the parabolic solar concentrator. Figure 1 shows the diameter of the parabolic solar dish surface (D) and the focal length of the parabolic concentrator (f). The schematic diagram of geometrical parameters of solar dish concentrator system is shown in Figure 1.

Figure 1. Schematic diagram of geometrical parameters of solar dish concentrator system

The aperture area of this parabola dish solar concentrator is given by equation (2) where receiver area given by equation (3) (2) (3) 81

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where

is the aperture area of dish solar concentrator and

is the area of receiver.

The Geometric Concentrating Ratio is given by the Equation (4) The focal length of the parabolic concentrator dish is f=

(5)

2.2. Receiver The receiver design is an integral part of any collector, the light concentrated by the dish solar concentrator must focus at the receiver were it will be absorbed as heat and transferred to the working fluid. The efficiency of the concentrating system is defined at the ratio between the useful energy delivered to the working fluid, Qu, to the energy incident on the concentrator’s aperture, Qs; (6) The efficiency of the system is largely determined by the amount of heat lost from the receiver to surrounding environment. This heat loss occurs through conduction, Qlk, convection, Qlc and radiation, Qlr. The total heat loss from the receiver is given by; (7) The receiver should be designed in order to reduce the heat loss; the design must take into account the shape of the receiver.

3. Optical Analysis of Parabolic Dish Concentrator Based on ray trace technique, the parabolic dish geometry has been drawing using CAD software Solid Edge and Solid Works. The internal surface material was a mirror which reflectivity was 0.94. The solar radiation source has been made by Optics works, a ray tracing software, was used in order to determine the optimum dish and receiver dimensions for maximum optical efficiency, (Optics works Ltd, 2010). Defining a light source and reflective properties of the material shows angles at which light is reflected; from this the optical 82

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efficiency of the dish can be determined. A snapshot of the parabolic dish and receiver with the optical rays, can be seen in Figure 2. Receiver

Source of Rays

Receiver

Rays

Parabolic Concentrator Parabolic Concentrator

Figure 2. The ray tracing of Parabolic dish at (θ is 0º) incidence angle. By placing a detector along the underside of the receiver, optics works can graphical demonstrate the distribution of light incident across its surface. This flux distribution, as seen in Figure 3, is highly concentrated at the centre of the receiver.

Figure 3. Flux distribution across the receiver of the 3-D parabolic dish 83

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The detector is also use to determine the total amount of light incident on the receiver. A similar detector is then placed across the surface of the parabolic dish. The ratio between the total amount of light that is incident on the parabolic dish and the total amount of light that is concentrated on the receiver, gives the optical efficiency. The optimum receiver area for a 0.049063 m2 dish is 4.524 m2.

4. Fabrication of Parabolic Dish Solar Concentrator The parabolic dish solar concentrator was constructed out of fiberglass, a concentration ratio of 91.16  is large enough to give the required receiver temperature and assure that the irradiance of input radiation is large in comparison with thermal radiation out of the receiver. The entire concentrator system was limited in size by the parabolic dish solar concentrator. The largest diameter dish is 2.4 m. A focal length of 0.85 m was chosen to give the solar concentrator dish a rim angle of 39°. It was important to fabricate the solar concentrator system by hand using small sections of mirror were made and fitted together to form a continuous solar concentrator dish shape, The fabrication of the parabolic dish solar concentrator is carried out at Sirte University. The process of fabrication of the parabolic dish solar concentrator and the finished solar concentrator dish is shown in Figure 4. The geometrical specifications of solar parabolic dish concentrator are shown in Table 1.

Table 1. Geometrical specifications of solar parabolic dish concentrator Input Data Parameter Reflectivity of the dish R Depth of the parabolic dish Reflect surface Frame of the dish solar concentrator Calculations Aperture area of the dish (Ap) R Ar Diameter of the dish (D) Focal length of the dish (F) Ratio (Focal length/Diameter of the dish) Rim angle of the dish (Φ) Geometric Concentration ratio (CR) Number of Mirror

84

Value 0.90 1.2 1.135 Mirror Fiberglass

Unit m m -

4.524 0.125 0.049063 2.4 0.85 0.3541 39.406 91.16 2052

m2 m m2 m m ° -

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Figure 4. Fabrication process of the parabolic dish solar concentrator

5. Receiver Fabrication The receiver is fabricated by bending and welding process, and formed a coil flat portion to capture the maximum incident solar radiation. The copper tubing of the apparatus consists of copper coils and the copper tubing of 8 mm copper pipe, which was carried water from main through a warm water bath. Thermocouple was attached to measure the inlet temperature, outlet temperature. The inlet diameter of the copper tube was 0.006 m and outlet diameter was 0.008 m total length was 5 m. The hardened copper tube with length of 5 m is connected with the small blocks by brazing process and this set up makes the counter flow arrangement to enhance more heat transfer. The receiver is fully coated by black paint with a high temperature resistant. The purpose of high absorptive and low reflective black paint was used to coat the receiver surface this paint is to absorb more radiation as shown in Figure 4a. The material of the receiver box is mild steel material. The receiver box is fabricated by bending and welding process as shown in Figure ‎4b. The entire receiver assembly is placed within a chamber and covered with glass wool insulation with thermal conductivity 0.037 W/mK to minimize losses as shown in Figure 4b. The measured reflective coefficient for the receiver with and without black paint is shown in Figure 5. It can be observed that the reflectivity of the black coated receiver is very 85

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low in the range of about 6% with maximum of 10%, while the reflectivity of the uncoated receiver increases sharply from about 15% at the wavelength of 250 nm to a maximum of 86% at the wavelength of 850 nm and remain constant. The receiver design specification has shown Table 2.

Table 2. Specifications of receiver design Parameter Length Width of Receiver Aperture area of Receiver Receiver Tube O.D. (Outlet Diameter) Receiver Tube I.D. (Inlet Diameter) Receiver surface Receiver frame Insulation Insulation K-factor Absorber

Receiver Coil

Value 5 0.25 3.14 0.008

Unit m m m2 m

0.006

m

Copper Mild steel material Polyurethane foam 0.0024 Black Coating

m2 W/m oC

Receiver Housing

Hole of Supporting Arm

Figure 4. (a) Coil receiver with coated by black paint; (b) chamber and covered with glass wool insulation

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Figure 5. Measured reflective coefficient for coated and uncoated receiver [3]

6. Experimental Set-up and Parameters Measurements 6.1. Outdoor Experimental Set-up of Parabolic Dish Solar Concentrator

This experimental work mainly studies the solar transformation energy into thermal energy by using a follower solar parabolic dish concentrator with using a manual tracking of the sun. In order to investigate the parabolic dish solar concentrator performance, an experimental unit has been designed, built and tested in Meteorological station. This station is located at Sirte University where Longitude is 16° 35´ 42´´E and Latitude is 31° 3´ 49´´N. The experiment has been carried out on a parabolic dish concentrator of 4.524 m2 and a copper receiver of 0.049063 m2. The system efficiency depends on the heat input, which depends on the temperatures of the water at the inlet and outlet of the heat exchanger and the mass flow rate. The experimental cycle consists of the parabolic dish solar concentrator with copper coil receiver; one end of the receiver is connected to inlet tank and other end to the outlet of the tank. The Schematic of the outdoor open cycle of the dish solar concentrator experimental setup is shown in Figure 6. The inlet and outlet temperatures of water are measured. The flow rate of 0.4 kg/min is allowed to flow through the receiver.

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Figure 6. The Schematic of the outdoor open cycle of the dish solar concentrator experimental setup

The variation of inlet and outlet temperatures with time is shown in figure 6. The maximum outlet temperature of 610 C is recorded for the solar radiation of 850 W/m2. In the flow conditions, water is allowed to flow through the receiver and heated depends on the solar concentration level. The receiver is placed on the top of the solar concentrator and radiation from the solar radiation reached the receiver through reflections in the concentrator and finally absorbed by the receiver. However, the outlet temperature of the solar concentrator system is examined whether the outlet temperatures would be useful for a seawater desalination process. The dish solar concentrator is designed and constructed for experimental testing as shown in Figure 7. The working fluid flows from the inlet tank to the coil receivers and the hot water is collected in the collection tank. Each tank has capacity of 10 litters.

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Solar cell

Receiver Voltammeter

Pump

Out let Water

Data Logger

Solar Concentrator

Water Tank Inlet Water

Figure 7. Experimental dish solar concentrator apparatus located at Sirte University-Libya 6.2. Parameters Measurements and Data Gathering

In this work, difference measuring techniques is implemented. The temperature measurements of the collector system and ambient temperature have been obtained using type-k thermocouples. The wind speed and direction are detected using an in-situ anemometer as shown Figure 8. The global solar radiation flux on the parabolic dish solar concentrator and the solar flux on the horizontal plane are both measured using precision pyrometer sensors. Water mass flow measurement is performed using a conventional glass tube Rotameter. All measurements, including inlet temperature, outlet temperatures, and surface temperatures of the receivers, inlet and collection tank temperatures, and solar radiation data have been recorded using a data acquisition system and then all data is off-line analyzed. The various temperatures such as surface temperature of the receivers, inlet and outlet temperature of the receivers, inlet and collection tank temperatures are measured using K-type thermocouples, these whole measurements are shown on Figure 8.

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Figure 8. The solar radiation and wind speed measurements for dish collector (Meteorological station)

Figure 8 illustrates the test procedure which includes the measurements of climatic parameters such as global solar irradiance on the climatic system (solar radiation system), ambient temperature, wind direction and speed. Operational parameters such as initial inlet water temperature, outlet water temperature, and flow rate are continually observed. These parameters allow evaluating increase or decrease of water temperature and the available energy transferred to the water during the operation period.

7. 7. Results and Discussion Experimental thermal performance analysis was carried out for flow rate of 0.4 kg/min. The working fluid enters from inlet tank to the first flow meter and passes to the receiver and goes to outlet tank. Figure 9 shows the outlet temperatures of the system on 13th of August 2014 for the flow rate is 0.4 kg/min. The temperatures increased when the hour angle increases up to 12:30 pm and then start to decrease. The maximum temperature recorded was approximately 610 °C 90

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and occurred between 12:00 to 13:30 hrs, when the average solar radiation was 871 W/m2. When wind speed was 3 m/s, humidity was 65 % and ambient temperature was 26 °C. Figure 10 (a,b) represents wind speed and wind direction variation measured by the meteorological station during the period from 9:20 am to 18 pm, the wind speed increase with time from morning to evening. The maximum hourly 17:30 reaching 4.5 m/s and minimum hourly 9:30 was 0.5 m/s. Based on the data from the Meteorological station in Sirte (Sirte University). Figure 11 shows the Photograph of the Voltammeter indicate to the temperature recorder was 610 °C.

Figure 9. Diurnal variation of global radiation and outlet temperature with time when flow rate was 0.4 kg /min on 13th August 2014

a

b

Figure 10. Diurnal variation of (a) wind speed and (b) wind direction with time on 13th August 2014 91

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Figure 11. Photograph of the Voltammeter indicates to the temperature recorder.

Figure 12 shows the outlet temperature when the flow rate was 0.4 kg/min, the experiment was carried out from 9:30 to 17:30 hrs on 16th August 2014. Again the temperatures increased when the hour angle increased up to 13:00 hrs and decreased thereafter. The maximum temperature recorded was 635 °C; this was recorded between 11:00 to 13:00 hrs when the average solar radiation was 857 W/m2. Where wind speed was 3.6 m/s, humidity was 59.6% and ambient temperature was 26 °C.

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Figure 12. Diurnal variation of Global radiation and outlet temperature with time when flow rate was 0.4 kg/min on 16th August 2014

4. Conclusion In this design, parabolic dish solar concentrator and coil absorber is placed at the foci point of the concentrator has been carried out. This design of imaging profile increases the solar concentration to produce high temperature with high optical efficiency and less heat losses in the absorber. This paper mainly presents an insight into the design, fabrication and outdoor testing of parabolic dish solar concentrator for high temperature applications. The parabolic dish solar concentrator was fabricated in Sirte - Libya, at Sirte University. This parabolic dish solar concentrator was used for the outdoor testing in Sirte. The experiments were carried out for different conditions to study the performance of solar dish concentrator. The system efficiency depends on the heat input; temperatures of the water at the inlet and outlet of the receiver and the mass flow rate have been carried out. The maximum temperature of 635C was reached when flow rate is 0.4 kg/min. Acknowledgment The authors would like to acknowledge Sirte University and the Faculty of Engineering for their support.

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5. References [1] Imhamed M. Saleh Ali , Tadhg S. O’Donovan, K.S. Reddy, Tapas K. Mallick (2011),Optical study of a 3-D elliptical hyperboloid concentrator, 30th ISES Biennial Solar World Congress 2011 Germany, SWC 2011, volume 4, pages 2672-2678. [2] Imhmaed, M.Saleh Ali., Tadhg S.Reddy, K. S. Mallick, Tapas K., An optical analysis of a static 3-D solar concentrator, Solar Energy, 88 (2013) 57-70. [3] Imhamed M. Saleh Ali , T. Srihari Vikram, Tadhg S. O’Donovan, K.S. Reddy, Tapas K. Mallick, Design and Experimental Analysis of a Static 3-D Elliptical Hyperboloid Concentrator for Process Heat Applications. Solar Energy 102 (2014) 257–266 [4] Imhamed M. Saleh Ali , Tadhg S. O’Donovan, K.S. Reddy, Tapas K.Mallick, Comparison of Optical Performance of 3-D Solar Concentrator for Circular and Elliptical Absorber, World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES) Annual Conference, volume 1, pages 181-188. [5] O’Gallagher, J.J., 2008. Nonimaging Optics in Solar Energy in Solar Energy. Morgan & Claypool: San Rafael, California. [6] Winston, R., Miñano, C.J., & Benítez, V., 2005. Nonimaging optics. Burlington: Elsevier Academic Press. [7] Lovegrove, K., et al., Developing ammonia based thermochemical energy storage for dish power plants. Solar Energy, 2004. 76(1–3): p. 331-337. [8] Mills, D., Advances in solar thermal electricity technology. Solar Energy, 2004. 76(1–3): p. 19-31. [9] Kennedy, C.E.a.K.T., Optical durability of candidate solar reflectors. . ASME Journal of Solar Energy Engineering, 2005. 127(2): p. 8. [10] Palavras, I. and G.C. Bakos, Development of a low-cost dish solar concentrator and its application in zeolite desorption. Renewable Energy, 2006. 31(15): p. 2422-2431. [11] Klaib, H.K., Rainer Nitsch, Joachim Sprengel, Uwe, Solar thermal power plants for solar countries — Technology, economics and market potential. Applied Energy, 1995. 52(2–3): p. 165-183. [12] Hassib, A.M., Compound conical concentrators with elliptical receivers. Solar Energy, 1986. 36(1): p. 89-92. [13] El-Refaie, M.F., Theoretical analysis of the performance of a conical solar concentrator. Applied Energy, 1982. 12(1): p. 37-51.

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Influence of Ground Granulated Blast Furnace …… Sirte University Scientific Journal (Applied Sciences)

Vol. 7 (1), 95–114, June 2017

Influence of Ground Granulated Blast Furnace Slag as Cement Replacement on Some Properties of Paste and Concrete Mixes Mohammed Ali Abdalla Elsageer and Ayad Abdelmoula Mohammed Civil Department, Faculty of Engineering, Sirte University, Libya

E-mail: [email protected] Abstract Portland cement, already being a very expensive material constitutes a substantial part of the total construction cost of any project and the situation has further aggravated by the energy crisis, which has further increased the cost of production of Portland cement. Therefore, it is of current importance for the country to explore and develop cementing materials cheaper than Portland cement. This research, focus on investigating Physical and Chemical properties of Portland cement with partial replacement with Ground Granulated Blast furnace Slag (GGBS), such properties are compressive strength, normal consistency, setting times of neat cement mixes (control) and partial replacement of cement with GGBS levels of 20, 40, 60 and 80%, and the main focus to find the optimum percentage of GGBS that gives the greater compressive strengths improvements of concrete. Ground Granulated Blast furnace Slag was collected from Steel Mills Misurata (Libya) and pulverized to a very fine degree. Concrete of target mean strength 50MPa were produced to determine the compressive strength development under standard curing conditions (200 C). The tests results of normal consistency, setting times showed that the higher the percentage of the GGBS reduce the needed percentage of the water and as the percentage of GGBS increase the initial and final setting time decrease. The strength development appears to be similar to Portland cement concrete strength at all ages for 20% GGBS concrete only, For the concretes with 40, 60 and 80% GGBS the compressive strength was lower than the control concrete during all ages. GGBS collected from Steel Mills Misurata (Libya), better of it is used as aggregate and not as a binder component in cement manufacture.

Keywords: Ground Granulated Blast furnace Slag (GGBS), the compressive strength , Portland cement , Blast furnace Slag.

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1.

Introduction

Concrete is a mixture of cement, fine aggregate, coarse aggregate and water. Concrete plays a vital role in the development of infrastructure such as buildings, industrial structures, bridges and highways…etc. leading to utilization of large quantity of concrete. High Performance Concrete (HPC) is a concrete meeting special combinations of performance and uniformity requirements that cannot always achieved routinely by using conventional constituents and normal mixing. This leads to examine an admixtures to improve the performance of the concrete. On the other side, cost of concrete is attributed to the cost of its ingredients which is scarce and expensive, this leading to usage of economically alternative materials in its production. This requirement is drawn the attention of investigators to explore new replacements of ingredients of concrete. The production of GGBS requires little additional energy as compared with the energy needed for the production of Portland cement, the replacement of Portland cement with GGBS will lead to significant reduction of carbon dioxide gas emission. The Blast Furnace Slag can be used in cement and concrete either as an aggregate (coarse or fine), or as a binder component in concrete manufacture. The Blast Furnace Slag produced in three forms:1. Blast Furnace Rock Slag (BFRS) 2. Granulated Blast Furnace Slag (GBFS) 3. Ground Granulated Blast Furnace Slag (GGBS) The Advantages of Using Blast Furnace Slag Cement:

High resistance against Sulfate and Acid attacks, prevention of chloride ion leakage into concrete, it also decreases the permeability of chloride ions.



Effective against possible expansion due to alkali-silica reaction.



Longer strength development compared to CEM I type cement.



Increasing economic life of buildings.

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Increase of low temperature at early ages.



Low heat of hydration that prevents thermal cracks.



Resistance to high temperature as in case of fire, deformation is less compare to CEM I type cement when subject to fire and high temperatures.

GGBS is therefore an environmentally friendly construction material. It can used to replace as much as 80% of the Portland cement used in concrete. GGBS concrete has better water impermeability characteristics as well as improved resistance to corrosion and sulfate attack. As a result, the service life of a structure is enhanced and the maintenance cost reduced [1,2,3,4,5,6,7,8,9,10]. The main objectives of this paper is to investigate the effect of partial replacement of cement by 20, 40, 60, 80% of ground granulated blast furnace slag (GGBS) on cement properties and on compressive strength of concrete. To investigate the effect of GGBS on normal consistency and setting time of cement, first the normal consistency and setting time of cement has been obtained and then the cement were replaced with GGBS levels of 20, 40, 60 and 80% to obtain the effect of each level of replacement of GGBS on normal consistency and setting time of cement. To obtain concrete of target mean strength 50MPa, concrete mix were produced to determine the compressive strength development under standard curing conditions 20 oC of concrete, with neat Portland cement mix as control mix, and partial replacement of cement with GGBS levels of 20, 40, 60 and 80%. The proportions of the concretes were obtained according to the BRE method (mix design of normal concrete)[12] for concrete with water/cement ratio (w/c) of 0.44.

2. 

Materials, Experimental Procedures and Mix Design of GGBS Concrete. Materials Used:-

The same materials were use throughout this study. All the materials were in accordance with relevant BS and ASTM standards and considered suitable for the scope of this study. 97

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Aggregate:Fine and coarse aggregate was used in this study, it was obtained from Alkhalij 4*350MW Power Plant which are about 15 kilometers west of Sirte. Sieve analysis of fine and coarse aggregate according to BS 882:1992[13]:

Fine Aggregate:-

Sieve analysis of in sand The sieve analysis results of the fine aggregate are shown figure (1):100

Percentage Passing (%)

90 80 70 60 50

sand

40 30

BS limits for fine aggregate

20 10 0 0.30

0.60

1.18

2.36

Nominal BS sieve size (mm)

Figure 1. Grading of sand According to the standard the fine aggregate complies with the specification as medium sand, adding fine aggregate (the ruins of a small size 5mm aggregate) to improve the gradation in the concrete mix proportions. The sieve analysis results of the fine aggregate (the ruins of a small size) are shown in figure (2):According to BS 882:1992[13] the above fine aggregate complies with the specification as single sized aggregate (5mm). Mixing of sand with fine aggregate graduation rates give 98

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appropriate and in conformity with the specifications 30% size 5mm aggregate & 70% fine aggregate as shown in figure (3):-

Sieve analysis of fine aggregate

100 90

Pecentage Passing (%)

80 70

Fine aggregate

60 50 40 30

BS limits for fine aggregate

20 10 0 2.36

5.00

10.00

Nominal BS sieve size (mm)

Figure 2. Grading of size aggregate Sieve analysis of 5mm fine aggregate 100 90

Percentage Passing (%)

80 70 60 50 40

fine aggregate

30 20 10

BS limits for fine aggregate

0 0.30

0.60

1.18

2.36

Nominal BS sieve size (mm)

Figure 3. Grading of combined sand with size (5mm) aggregate According to BS 882:1992[13] the above fine aggregate complies with the specification as medium sand. 99

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Coarse Aggregate:-

The natural coarse aggregate sizes (10mm and 20mm) was used, which is identical to the standard specifications. -

Coarse Aggregate (size 10 mm):-

The results of sieve analysis for coarse aggregate size (10mm) can be seen in figure (4), according to BS 882: 1992 [13] the above coarse aggregate complies with the specification as Sieve analysis of coarse aggregate size (10mm) single sized aggregate (10mm). 100

Percentage Passing (%)

90 80

coarse aggregate size (10mm)

70 60 50 40 30

BS limits for coarse aggregate

20 10 0 2.36

5.00

10.00

14.00

Nominal BS sieve size (mm) Figure 4. Grading of aggregate size (10mm) -

Coarse aggregate size (20 mm):-

The sieve analysis results of the coarse aggregate size (20mm) is shown in figure (5), according to BS 882:1992[13] the above coarse aggregate complies with the specification as single sized aggregate (20mm).

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Influence of Ground Granulated Blast Furnace …… Sieve analysis of coarse aggregate size (20mm) 100

Coarse aggregate size (20mm)

90

Percentage Passing (%)

80 70

BS limits for coarse aggregate

60 50 40 30 20 10 0 5

10

14

20

Nominal BS sieve size (mm)

Figure 5. Grading of aggregate size (20mm)

Mixing 50% for each size to achieves the specifications as shown in figure (6). Sieve analysis of coarse aggregate 100 90

Percentage Passing (%)

80 70 60

Coarse aggregate 50 40 30 20

BS limits for coarse aggregate

10 0 5

10

14

20

Nominal BS sieve size (mm)

Figure 6 Grading of combined 20-10mm of aggregate 101

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Moisture content of fine and coarse aggregate according to (ASTM C-566-84)[14] are:Moisture content percentage (fine aggregate) = 0.68%. Moisture content percentage (coarse aggregate) = 2.06%.



Specific gravity and absorption of coarse and fine aggregate determined according to (ASTM C 127-88)[15], (ASTM C 128-88)[16] respectively are shown in table (1): Table 1. Specific gravity and absorption of coarse and fine aggregate Aggregate Bulk specific Bulk specific gravity Apparent specific Absorption size gravity (gm/cm3) (saturated surface dry) gravity (gm/cm3) %

(gm/cm3)



(20mm)

2.35

2.456

2.633

4.603

(10mm)

2.34

2.453

2.638

4.838

5mm

2.34

2.46

2.621

5.12

Sand

2.61

2.62

2.71

0.664

Cement:Zliten cement was used in this study which can be classified as normal Portland cement type 42.5N and the results of the tests of fineness, specific gravity, normal consistency and setting time were confirm to the specifications.



Ground granulated blast furnace slag (GGBS):The GGBS was obtained in the solid state and it was in the form of aggregate in large volumes, and it's size was approx. (40-80) mm as shown in figure (7).

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Figure 7. Solid GGBS In this case, it is not suitable for use as a substitute for cement, and thus it was necessary to find a way to convert this aggregate to a state close to, although it is difficult to achieve that and the following steps to grind the GGBS:1. Solid GGBS was placed in the machine dedicated to crushing the aggregate and breaks up into small diameter of 5 mm approx as shown in figure (8).

Figure 8. GGBS after crushing 2. Gathering the GGBS that has been crushed into 5mm rocks then the rocks were placed with iron balls into Los Angeles machine for further grinding as shown in figure (9).

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Figure 9. GGBS after grinding inside Los Angeles machine



Fineness of hydraulic cement by No.100 sieve (ASTM C 184-83)[18] :This test method covers determination of the fineness of hydraulic cement by means of the (No.100) sieve. Percentage of retaining on (NO.100) sieve=0.2%