This paper presents the application of the Binary Firefly Algorithm (BFA) in tuning PID parameters for a coupled tank system. The agent position in the BFA represents the potential combination the PID parameters. This agent position is modelled using a string of 32 binary bits where each eight bits represents the value o

Azahar, Arman Hadi

Azmi, Ezzatul Farhain

Harun, Mohammad Haniff

Karis, Mohd. Safirin

Mohd. Nordin, Ili Najaa Aimi

Rifin, Rozi

Samsudin, Adam

Yep, Kow Wai

Zainal Abidin, Amar Faiz

English

UTHM Institutional Repository

International Conference on Mechanical Engineering Research (ICMER2013), 1-3 July 2013 Bukit Gambang Resort City, Kuantan, Pahang, Malaysia Organized by Faculty of Mechanical Engineering, Universiti Malaysia Pahang Paper ID: P067 1  MECHANICAL AND THERMAL PROPERTIES OF  WASTE BIO-POLYMER COMPOUND BY  HOT COMPRESSION MOULDING TECHNIQUE    M. Khairul Zaimy A. G. 1, a, Anika Zafiah M. Rus 1, b, *, Najibah Ab Latif 1, Nurulsaidatulsyida S. 1  1 Sustainable Polymer Engineering  Advanced Manufacturing & Material Centre (AMMC) Faculty of Mechanical and Manufacturing Engineering Universiti Tun Hussein Onn Malaysia (UTHM) 86400, Parit Raja, Batu Pahat, Johor, Malaysia. Email: amohdkai86@gmail.com, bzafiah@uthm.edu.my,  *corresponding author  Phone: +60127282205   ABSTRACT  Demands for Bio-Polymer Compound (BPC) has attracted attention in various applications from industrial to medical. Thus, mechanical and thermal stability properties of recycling waste BPC from industrial are very important to be investigated. The waste BPC for this study, based on is the mixture of hydroxylated waste cooking oil with hardeners to produce Waste Bio-polymer Foam (WBF). The granulate of WBF were cast into the mould until all spaces evenly fill and compact into homogenous shape and thickness at 30 – 45 Bar for 2 hours using hot compression moulding. This method of BPC fabrication resultant in the tensile and flexural strength of 4.89 MPa and 18.08 MPa respectively. Meanwhile the thermal stability of BPC laminated was conducted by Thermal Gravimetric Analyzer (TGA) exhibited the first degradation occur at 263 °C of soft segment than subsequently the second degradation at 351 °C and last 416 °C.  Keywords: Bio-polymer compound; Hot compression moulding; Thermal stability.  INTRODUCTION  Due to environment and sustainability issues, this century has witnessed remarkable achievements in green technology in the field of materials science through the development of Bio-composites or Bio-Polymer Compound (BPC). The development of high-performance materials made from natural resources is increasing worldwide (Omar Faruk et al., 2012). Historically a predominantly aerospace material, natural fiber-reinforced composite structure have seen an increased application in other industries such as automotive, marine transportation, civil engineering, sporting goods, medical equipment and prosthetic devices. Natural fiber-reinforced composite structures provide high strength, high stiffness mechanical properties, unique flexibility in design capabilities, and ease of fabrication. Also, they are lightweight, corrosion resistant, impact resistant, and have excellent fatigue strength (Scott Robert et al., 2002). The greatest challenge in working with natural fiber reinforced plastic composites is their large variation in properties and characteristics. A Bio-composite such as WBC‟s properties are influenced by a number of variables, including the temperature, pressure applied to moulding, processing methods, and type of resin that most popular matrices in Bio-composite based on petrochemical (Omar Faruk et al., 2012).   2  Bio-composites or WBC are composed of two or more materials which, when properly combined, form a different material with properties not available from the ingredients alone. Depending on the ingredients chosen and the method of combining them, a large spectrum of material properties can be achieved. A brittle material can be made more ductile (flexible) by adding a softer material; conversely a soft material can be made stiffer. Wood is a good example of a Bio-composite. The cellulose fibers provide the strength and are held together by the resin. Reinforced concrete is another example. The steel re-bars provide excellent tensile strength and the concrete provides compressive strength and transfers the load between the steel bars. Modern composites or FRP (Fiber reinforced polymers, or plastics) are the newest addition to the structural engineers toolbox. Although the materials have been available for decades, a reduction in cost, combined with newer understanding of the versatility and benefits of the material properties, has allowed composites to move into mainstream construction (Scott Robert et al., 2002).   Malaysia, being one of the largest producer and manufacturer of palm oil products, generates large amount of palm oil by-products, which can be recycled into bio-monomer. If this bio-monomer is put into good use, in this case as a main material in bio-polymer compound, then it will largely reduce the cost of high performance composite (M. Hilton Ahmad, 2007). At the same time, it will also reduce the amount of waste generated by the palm oil industry thus achieving a global aim of sustainable development. Not only it reduces the waste, it also preserves the nature by eliminating the need to harvest natural aggregates from natural sources. With the increased use of Bio-composite materials, there is a need to conduct serial of testing to predict the mechanical properties and behaviour of BPC materials and structures made of these materials under a variety of loading and environmental conditions. Furthermore thermal stability of BPC has gained considerable attention because of their potential application in a number of areas. Many applications of BPC require materials that can resist a variety of external stresses such as heat or fire. However, BPC generally have low thermal stability at high temperatures and thermal degradation can begin to occur at processing temperatures above 180°C, depending on the parameters (D. Braun, 1981). The study of the decomposition of biopolymers is particularly difficult since they degrade with the formation of various gaseous products and a number of decomposition steps are typically observed in Thermal Gravimetric Analysis (TGA) experiments. The thermal stability of a material is defined by the specific temperature or temperature-time limit within which the material can be used without excessive loss of properties (Chattopadhyay DK et al., 2009).  The main purpose of this study is to evaluate the degree of conversion and determine the thermal stability of biopolymer by using TGA with temperature of 25oC - 900oC at a rate of 10°C/min.   The exploration of this Waste Bio-polymer Compound (WBC) from waste palm cooking oil using hot compression technique is a good study to undertake. The preliminary study was to conducted base on the processing conditions that influences its mechanical properties and the thermal properties of WBC.          3  EXPERIMENTAL  Preparation Bio-Foam Compound (BFC)    Bio-Foam Granulate (BFG) was prepared (Anika Zafiah M. Rus, 2009) and cut into small size approximately 10 mm X 10 mm X 10 mm cube size and grained using rotor mill to form small particle, started with 6.00 mm, followed by 2.00 mm and 0.50 mm. This small granulate were cast into the mould until all spaces evenly filled and hot compress into homogenous shape and thickness at 30 – 45 bar with temperature of 40 – 55 °C for 2 hours. The sample was laminated with 10 gram of epoxy on top and bottom on both side of BPC.     Characterizations Tensile and flexural strength of WBC was carried out by Universal Testing Machine (UTM) model Lloyd Instruments LR30K as according to standard BS EN ISO 527-2:1996 type 1A with 1.0 mm/min crosshead speed (BS EN ISO, 1996). The flexural (3 point bending test) is accordance to ISO 178:2010 with speed 0.10 mm/min (ISO 178, 2010). The specimens‟ dimensions for tensile were 150 mm long, 10 mm wide and 4.0 mm thick meanwhile flexural was 80 mm long 10 mm wide and 4 mm thick. The Archimedes principle was used to obtained density of BFC and WBC. Scanning Electron Microscope (SEM) model JOEL JSM – 6380LA complete with Energy-dispersive X-ray spectroscopy (EDX) was used to examine the surface fracture morphology of the tensile specimens. The structure of specimen was coated with coating conductor material to allow electrons to flow through the specimen. The weight lost of WBC of a function of temperature was determined by using Linseis Thermo balance Simultaneous Thermal Analysis (STA) (TG + DTA).  Sample weighing between 100-120 mg were placed in a crucible in a furnace and heated in nitrogen between 25°C - 900°C at a rate of 10°C/min.                       Figure 1. BFC of hot compression moulding Figure 3. Flexural strength test specimens Figure 2. Tensile test specimens 4  RESULTS AND DISCUSSION  Mechanical properties of WBC Figure 4 shows the relationship between molding pressure versus tensile strength and flexural strength. The tensile strength of WBC increases with increased molding pressure as well as the flexural strength. The highest the molding pressure revealed highest tensile strength of 4.89 MPa as well as flexural strength of 18.08 MPa for both sample of WBC. Meanwhile, the lowest molding pressure of 31 bar revealed the lowest tensile strength and flexural strength of 1.81 and 9.03 MPa of WBC respectively.                Figure 4. Influence of molding pressure on tensile strength and flexural strength of WBC Figure 5 presents the relationship between tensile strength and density with increasing of molding pressure. This by increasing of the molding pressure, the density and the tensile strength were increased rapidly. The highest density of 44 bar molding pressure is 1.209 g/cm3 with the highest tensile strength value of 4.89 MPa. This also resultant in the lower tensile value influences by the drop of density value to 0.857 g/cm3 consequently. Figure 6 depicts the same and systematic pattern with Figure 4 and Figure 5 with the highest flexural of 18.08 MPa at 44 bar and 9.03 MPa at the lowest molding pressure of 31 bar.                 Figure 5. Influence of molding pressure on tensile strength and density of WBC 5                  Figure 6. Influence of molding pressure on flexural strength and density of WBC  Morphology structure of WBC Mechanical properties of WBC were affected by internal defects such as voids as indicated by surface morphology with black area. This is in correlation with the finding of (Hitoshi Takagi, 2008). Consequently, the density usually serves as a good indicator for the WBC strength as refer to Figure 5 and 6.       SEM micrographs of the fractured surface of the laminated WBC are shown in Figure 7. It can be concluded that at low molding pressure in Figure 7(A), shows debonding of granulate leave holes and void. The granulate have tendency to pull out than breakage indicating of weak adhesion between the granulate. At high molding pressure as shows in Figure 7(C) revealed absence of less holes and void due to improvement for the granulate adhesion. The granulate has covered the holes and void area thus better stress transfer between the granulate. In addition, the fractured surface also shows granulate breakage rather than pull out (Paul et. al, 2008). This is assumed due to increased interfacial shear strength for high molding pressure and good interfacial adhesion between the granulate and epoxy. The SEM pictures prove that high pressure molding set up enhances mechanical properties of the WBC as compared to low pressure molding.    Figure 7. SEM of morphology fracture surface at 30x magnification (A) molding pressure of 31 bar (B) molding pressure of 38 bar (C) molding pressure of 44 bar, with the red circle indicated void area     A B C 6  Brittle fracture is observed as refer to the facture surface of WBC, the identification of brittle fracture is due to the mode of fracture characterized by rapid crack propagation. This type of fracture have glassy smooth surface, flat, bright, shiny and having minimum plastic deformation as well as percentages of fracture with little yielding before the samples breaks. Figure 7 shows brittle fracture of sample (A), (B) and (C) with minimum strain percentage. Fracture surface for Figure 7 (C) shows grew voids as compared to (B) and (A) (Lubarda, V.A., 2001).   Thermal properties of WBC The TGA weight loss curve displays 2 or 3 distinct regions that are reflected in the differential weight loss curve. Figure 8 shows the first curve of WBC occur at 74°C for volatile material such as gaseous (Y. M. Song, 1996) and usually this volatile material is not to be taken as first decomposition temperature. The first decomposition only occur on hard segment of WBC at 260°C and continues to 371°C for second decomposition indicated as the soft segment of WBC structure. The last decomposition occur at 415°C predicted as the byproduct of hard and soft segment structure. Meanwhile Figure 9 shows thermogram of epoxy material used to laminated BPC with hard segment decomposition at 351°C and volatile material with 1 distinct region. Figure 10 of WBC laminated with epoxy shows only the decomposition started in hard segment at 263°C for the cross linking agent, second decomposition occur at 351°C for epoxy and third occur 416°C according to (Y. M. Song, 1996). The tabulated result of decomposition temperature is shows in Table 1.  Figure 8. Thermogram of WBC  Referring to Table 2, the onset degradation temperature, Ton and maximum temperature, Tmax of WBC and WBC with laminated consist 3 step processes. For onset degradation temperature and maximum temperature for hard segment is T 1on -  T 1max of WBC is 220-280°C, and for laminated WBC is 230-280°C. The second T 2on -  T 2max for WBC is 340-380°C and laminated WBC is 330-360°C and last process T 3on -  T 3max is 400-455°C for WBC and 395-460°C for laminated WBC of soft segment.      7  The weight loss of WBC and laminated WBC during first degradation at is    3.00 % and 1.00 %. The second and third stage weight loss for WBC and laminated WBC is 12.00 % and 16.00 % for second stage, 18.00 % and 23.00 % for third stage. At last remaining weight losses are 67.00 % for WBC and 60.00 % for laminated WBC. The maximum epoxy weight loss is occur at temperature 385°C with 68.20% weight loss.     Figure 9. Thermogram of epoxy     Figure 10. Thermogram of laminated WBC with epoxy    Mass (mg) 8  Table 1. The degradation temperature of first, second and third decomposition       Table 2. Degradation temperature value of hard and soft segment  Sample Hard Soft T1 on T1 max T2 on T2 max T3 on T3 max WBC 220 280 340 380 400 455 Epoxy 330 385 385 430 - - Laminated with epoxy WBC 230 280 330 360 395 460   Table 3. Percentages of weight loss of WBC, epoxy and laminated WBC with  epoxy from TGA   Sample WBC Epoxy Laminated with epoxy WBC Weight Percentages (%) 1st  3.00 2.00 1.00 2nd  12.00 5.00 16.00 3rd  18.00 10.00 23.00 4th  67.00 83.00 60.00  Sample First (°C) Second (°C) Third (°C) WBC 260 371 415 Epoxy  351 385 - Laminated with epoxy WBC 263 351 416 9  The Degree of Conversion (α)  The alpha value, (α) is calculated using equation: α = 1- w (t) / wo                                                         (1) Where:  α = degree of conversion (alpha) = weight loss at given temperature wo = initial weight w (t) = weight at any time t = during degradation  Referring to Table 4, the alpha (α) value of WBC show value 0.09 for hard segment and 0.22 for soft segment. Alpha (α) value for WBC laminated is 0.12 at hard segment 0.23 during degradation at soft segment. The value of degree of conversion for epoxy on hard segment is 0.42.  Table 4. Degradation temperature value for hard and soft segment       CONCLUSION  The experimental result shows the mechanical properties such as tensile strength and flexural strength were influences by the density, void area and molding pressure. In order to improve the compatibility of WBC granulated, more than 45 bar is suggested for the hot compression molding setup.  Thus, the tensile and flexural strength of the WBC could be improved. For the thermal properties of WBC with and without laminated of epoxy weight loss shows slightly small different for example at temperature 400°C the WBC show 48.15 % and WBC laminated shows 48.00 %, it demonstrated that laminated with resin (epoxy) do not effected the decomposition WBC. Meanwhile SEM morphology where WBC with high pressured showed less voids and fiber pull out. This phenomenon indicates adhesion between granulate increases due to good interfacial adhesion between the granulated. Furthermore, the maximum tensile strength at highest compression parameter of pressure is 4.89 MPa with the highest density value.   ACKNOWLEDGEMENT  The author would like to thank the Malaysian Government and University Tun Hussein Onn Malaysia (UTHM), Johor for supporting this research under Malaysian Technical University Centre of Excellence (MTUN CoE) research grant Vot C014 and C015, Postgraduate Sample Composition Ratio Hard Soft WBC 0.09 0.22 Epoxy 0.42 - Laminated with epoxy WBC 0.12 0.23 10  Incentive Research Grant (PIRG) Vot 1122, Johor and Malaysian Government for supporting this research.  REFERENCES  Andrew J. C., Leornarde M., Anika Zafiah Mohd Rus.,  Private Comm., DEFRA, 2003. Anika Zafiah M. Rus., Degradation Studies of Polyurethanes Based On Vegetables Oils. (part I)., Photodegradation, Prog in Reaction Kinetic And Mechanism, vol 33, Pp 363-391 2008. 1468-6783@2008 Science Reviews 2000 Ltd Anika Zafiah M. Rus., Degradation Studies of Polyurethanes Based On Vegetable Oils. Part 2; Thermal Degradation And Materials Properties, Prog React Kinet Mech, vol 34, pp 1-43 2009. 1468-6783@2009 Science Reviews 2000 Ltd Anika Zafiah M. Rus, Effect of Titanium Dioxide on Material Properties for Renewable Rapeseed and Sunflower Polyurethane, International Journal of Integrated Engineering (Issues on Mechanical, Materials and Manufacturing Engineering), Volume 1, No 1, April 2009: ISSN:1985-854X  Anika Zafiah M. Rus,  Material Properties of Novelty Polyurethane Based On Vegetable Oils, The 11th International Conference on QiR (Quality in Research), Depok, Indonesia, 3-6 August 2009 BS EN ISO 527-2:1996 type 1A  Method for directly-moulded multipurpose test specimen Chattopadhyay DK, Webster DC. Thermal Stability and Flame Retardancy of Polyurethanes. North Dakota State University, USA. 2009 D. Braun. Thermal Degradation of Polyvinyl Chloride. Deutches Kunststoff Institut, German. 1981. Frederika, J.P., Fracture of Polycarbonate/ABS Blenda (1972) Hitoshi Takagi, Akira Asano. Effects of processing conditions on flexural properties of cellulose nanofiber reinforced „„green‟‟ composites. Composites: Part A 39 (2008) 685–689 ISO 178:2010 determining the flexural properties of rigid and semi-rigid plastics under defined conditions. Lubarda, V.A., Elastic plasticity Theory, United State, Pp 245-247 (2001) M. Hilton Ahmad, Sofian M., and Nurazuwa Md Noor. Mechanical Properties of Palm Oil Clinker Concrete 2007 page 1 Proceedings of EnCon2007 Omar Faruk, Andrzej K. Bledzki, Hans-Peter Fink, Mohini Sain. Biocomposites reinforced with natural fibers: 2000–2010, Progress in Polymer Science 2012 Scott Robert, John Bumpus. Experimental Setup and Testing of Fiber Reinforced Composite Structures. Thesis Master of Applied Science University of Victoria 2002 Smith, W.F., Hashemi, J., Foundations of Materials Science and engineering, Mc Graw Hill. Pp. 550 (1993) Sherely Annie Paul, Abderrahim Boudenne, Laurent Ibos, Yves Candau, Kuruvilla Joseph and Sabu, “Effect of fiber laoding and chemical treatments on thermophysical properties of banana fiber/polypropylene commingled composite materials,” Composites: Part A, vol. 39, pp. 1582-1588, 2008. Y. M. Song, W. C. Chen, T. L. Yu, K. Linliu and Y. H. Tseng, “Effect of isocyanates on the crystallinity and thermal stability of polyurethanes” Journal of Applied Polymer Sciences (1996)  

Degradation Studies of Polyurethanes Based On Vegetables Oils. (part I)., Photodegradation, Prog in Reaction Kinetic And Mechanism, vol 33, Pp 363-391 2008. 1468-6783@2008 Science Reviews

Effect of isocyanates on the crystallinity and thermal stability of polyurethanes”

Fracture of Polycarbonate/ABS Blenda

Mechanical Properties of Palm Oil Clinker Concrete

Thermal Degradation of Polyvinyl Chloride. Deutches Kunststoff Institut,

Application of Binary Firefly Algorithm (BFA) in tuning 

PID parameters for couple tank system

This paper presents the application of the Binary Firefly Algorithm (BFA) in tuning PID parameters for a coupled

tank system. The agent position in the BFA represents the potential combination the PID parameters. This agent position is

modelled using a string of 32 binary bits where each eight bits represents the value o

Harun, Mohamad Haniff

Karis, Mohd Safirin

Universiti Teknikal Malaysia Melaka (UTeM) Repository

                                  VOL. 16, NO. 7, APRIL 2021                                                                                                                 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.  www.arpnjournals.com                                                                                                                                                   757 APPLICATION OF BINARY FIREFLY ALGORITHM (BFA) IN TUNING PID PARAMETERS FOR COUPLE TANK SYSTEM  Arman Hadi Azahar1, Adam Samsudin1, Yep Kow Wai2, Amar Faiz Zainal Abidin1, Rozi Rifin3, Mohammad Haniff Harun1, Mohd. Safirin Karis1, Ezzatul Farhain Azmi4, and Ili Najaa Aimi Mohd. Nordin5 1Faculty of Electrical and Electronics Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia 2School of Science and Technology, Wawasan Open University, Johor Bahru, Malaysia 3Faculty of Electrical Engineering, Universiti Teknologi MARA Cawangan Johor, Kampus Pasir Gudang, Johor, Malaysia 4Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal,  Melaka, Malaysia 5Department of Electrical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, Malaysia E-Mail: arman.hadi@utem.edu.my   ABSTRACT This paper presents the application of the Binary Firefly Algorithm (BFA) in tuning PID parameters for a coupled tank system. The agent position in the BFA represents the potential combination the PID parameters. This agent position is modelled using a string of 32 binary bits where each eight bits represents the value of 𝐾𝑝, 𝐾𝑖, and 𝐾𝑑, respectively. Represents the values of PID parameters. The first five bits represents the decimal value while the remaining are fraction value. The model of the coupled tank system is taken from well-established literature. The proposed approach is then implemented on the model that used a PID controller as the control mechanism. This study proposed a priority based fitness formulation where the agent will give priority to the following parameters in the given order: 1) Sum of Absolute Error (SAE); 2) Overshoot (OS); 3) Settling Time (ST), and Steady-State Error (SSE). The result discussed the effect of number of agent and number of iterations towards the performance of the proposed approach.  Keywords: optimization, computational intelligence, binary firefly algorithm, PID controller, couple tank system.  1. INTRODUCTION A typical Coupled Tank System (CTS) is as shown in Figure-1 where the tank system is the combination of two liquid tanks [1]. The construction of CTS might seem simple yet it had been widely installed in industrial applications such as in petrochemical, papermaking, and water treatment industries [2]. The control of the liquid in the tank and flow of the liquid between tanks is the common problem of the tank system in the industries. The liquids will be processed by chemical or mixing treatment most of the time, but the level of the fluid must be controlled all of the time and the flow between tanks must be regulated.     Figure-1. Coupled tanks system.  The most widely used controller in industrial process control is the Proportional-Integral-Derivative (PID) controller which normally has been applied to control the couple tanks system [1] [3]. A PID controller provides a control loop feedback function where it calculates the error by comparing the measured results with the desired set point and minimizes the errors by adjusting the input values. The three parameters of the PID controller which are Proportional Gain (𝐾𝑝), Integration Gain (𝐾𝑖) and Derivative Gain (𝐾𝑑) can provide control action designed for specific process requirements [4][5].  Although a PID controller can detect error and adjusting to reduce the error, it does not guarantee the best performance of the system nor the system stability due to the selection of PID parameters [4]. The Binary Firefly Algorithm (BFA) is an extension of the Firefly Algorithm which is a metaheuristic algorithm introduced by Xin-She Yang in the year 2007 [6]. This algorithm is inspired by the flashing behavior of the firefly[7]. The main purpose of the firefly’s flash is to attract other fireflies. This matting behavior is adapted to be an optimization algorithm that is proven by the author [5] to be successful in tackling benchmark mathematical optimization problems [8].  The implementation of the optimization algorithm in controlling the PID controller for the CTS application is not uncommon. Ismail et al. [7] [9] proposed the application of two Swarm Intelligence algorithms: Particle Swarm Optimization (PSO) and Cuckoo Search in 2014 [6]. Hussien et al. proposed the priority-based PSO for tuning PID of CTS in the same year [1] [3].   2. METHODOLOGY                                   VOL. 16, NO. 7, APRIL 2021                                                                                                                 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.  www.arpnjournals.com                                                                                                                                                   758 The proposed model of CTS is a closed-loop system with the PID controller, which is illustrated in Figure-2 [1].     Figure-2. Couple tank system with PID controller.  The plant of the system is taken from [6] where the dynamics model of the tank system in s-domain can be written as:   𝐻2(𝑠)𝑉(𝑠)=3.585169𝑠2+196𝑠+1       (1)  where V(s) and 𝐻2(𝑠) are input voltage of the DC motor-driven pump and level of the second tank.  The proposed approach simulation is done using written M-file code. The M-file will automatically force simulation of the Simulink file as shown in Figure-2. The input and output signal of the simulation of the coupled tank will be recorded in a file. The BFA code that is run via M-file than access this file in order to calculate the fitness of the agent.  The algorithm of BFA is taken from [6]. The modelling of the proposed model is by using 8-bits to represent each of the PID parameter values of 𝐾𝑝, 𝐾𝑖, and 𝐾𝑑. Therefore, the location of a firefly in the search space is represented in 24 dimensions: each bit represented by one dimension. This can be expressed mathematically as in Eq. (2):  𝑿 = [𝐾𝑝 𝐾𝑖 𝐾𝑑]−1              (2)  where 𝑿 is the firefly position.  The first five bits represented the decimal value and the remaining three bits represent the fraction value of the parameter. Thus 𝐾𝑝 can be express mathematically as in Eq. (3):  𝐾𝑝 = [𝑏P1 𝑏P2 𝑏P3 𝑏P4 𝑏P5 𝑏P6 𝑏P7 𝑏P8]            (3)  where 𝑏𝑃1 the most significant bit for decimal values and  𝑏𝑃5 is the least significant bit for decimal value. While  𝑏𝑃6 is the most significant bit for fraction value and  𝑏𝑃8 is the least significant bit for fraction value.  The same model applied to 𝐾𝑖 and 𝐾𝑑 as in Eq. (4) and Eq. (5):  𝐾𝑖 = [𝑏I1 𝑏I2 𝑏I3 𝑏I4 𝑏I5 𝑏I6 𝑏I7 𝑏I8]      (4)  𝐾𝑑 = [𝑏D1 𝑏D2 𝑏D3 𝑏D4 𝑏D5 𝑏D6 𝑏D7 𝑏D8]     (5)  Thus, the agent position can be rewritten as Eq. (4)  𝑿 = [𝑏P1 𝑏P2 𝑏P3 𝑏P4 𝑏P5 𝑏P6 𝑏P7 𝑏P8 𝑏I1 𝑏I2 𝑏I3 𝑏I4 𝑏I5 𝑏I6 𝑏I7 𝑏I8 𝑏D1 𝑏D2 𝑏D3 𝑏D4 𝑏D5 𝑏D6 𝑏D7 𝑏D8]−1      (6)  The fitness model used is similar to the priority-based model proposed by [6] where the main priority is to minimize the sum of absolute error (SAE), the second priority is overshoot (OS), the third priority is settling time (ST) and last priority is a steady-state error (SSE).  3. RESULT AND DISCUSSIONS The proposed BFA code is run in MATLAB. The code was run with different numbers of iterations and the number of agents. The higher number of the iteration or agent causing the more time combination will take to finish one cycle run. The number of iteration used were 10, 20, 30, 40, and 50 whilst the number of agent used were 5, 10, 15, 20, and 25 respectively. Each combination of the iteration and agent numbers ran five times per combination.  The overall result is tabulated in Table-1 and Table-2. There were 25 combination of parameters which based on different values of number of agent and iteration. Table-1 shows the average results of the BFA code run in the MATLAB based on the sum of absolute error (SAE), overshoot (OS), settling time (ST), and steady-state error (SSE). The average result in Table-1 was calculated from the five cycles run of each combination of the number of iteration and number of the agent.                                                 VOL. 16, NO. 7, APRIL 2021                                                                                                                 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.  www.arpnjournals.com                                                                                                                                                   759 Table-1. Average result for Sum Absolute Error (SSE), Overshoot (OS), Settling Time (ST) and Steady State Error (SSE).  Number of Agents Fitness Model Number of Iteration 10 20 30 40 50 5 SAE OS ST SSE 7.51114 29.55132 4.69106 0.00096 7.42828 24.97286 5.8084 0 7.39124 22.02984 5.89384 0.00404 7.3138 18.26248 4.2754 0.01692 7.08084 16.28782 4.12956 0.01732 10 SAE OS ST SSE 7.38586 24.34824 5.03702 0.00276 7.23016 22.26372 5.27286 0.0043 7.32594 15.71012 4.84568 0.00458 7.17066 11.22188 4.25918 0.0206 7.09312 13.59774 4.90106 0.00732 15 SAE OS ST SSE 6.98082 14.39244 3.25694 0.021 6.89782 13.16908 4.36038 0.0063 6.89852 14.04794 4.234 0.00522 6.55102 4.31536 2.40488 0.02824 6.76116 9.59902 3.48938 0.0209 20 SAE OS ST SSE 6.89362 11.92706 3.55944 0.01242 6.86558 15.15866 5.076 0.00174 6.38306 7.05378 2.90368 0.01304 6.29894 8.49334 3.09868 0.00376 6.747 8.13505 2.83694 0.02444 25 SAE OS ST SSE 6.99108 15.34554 4.48918 0.00674 6.91392 9.30388 3.7169 0.01098 6.38316 4.86816 2.48766 0.01354 6.4112 9.20486 3.09354 0.02134 6.45912 8.3508 3.02648 0.02724  Table-2. The minimum value for all results.  Number of Agents Fitness Model Number of Iteration 10 20 30 40 50 5 SAE OS ST SSE 7.4224 28.1517 5.1893 0 7.4188 18.148 7.1422 0 7.2552 12.3392 4.2403 0.0202 6.9418 5.2657 2.59 0.0421 6.496 9.5285 3.4 0.0272 10 SAE OS ST SSE 7.204 7.5187 3.3324 0.0138 6.4562 9.7079 3.1947 0.0215 7.1614 0.027 2.7826 0 6.3055 6.6868 2.598 0 5.9585 8.4088 2.6201 0 15 SAE OS ST SSE 6.2623 13.4777 3.6088 0.0019 5.8511 6.4762 2.6119 0.0084 6.0278 8.5821 2.6332 0.0078 5.7151 6.7802 2.5534 0.01 6.3187 4.145 2.5144 0.0241 20 SAE OS ST SSE 6.629 15.562 3.6242 0.0023 5.839 5.6321 2.551 0.005 5.7867 4.6632 2.5307 0.0015 5.7867 4.6332 2.5307 0.0015 6.3067 6.661 2.5982 0.0269 25 SAE OS ST SSE 6.0899 6.3478 2.62 0.0003 6.3769 2.1144 2.0619 0.0117 6.0899 6.3478 2.62 0.0003 6.0899 6.3478 2.62 0.0003 6.1486 12.8515 3.5614 0.0092  The trend that can be clearly seen from Table-1 is that as the number of iteration increases, the fitness for the first priority (SAE) become better (decreases). The same pattern can be seen when number of agents increases. Apart from the analysis of the average of the results, the minimum of the results also an important point to determine the performance of the code. From the minimum result, as shown in Table-2, the value from the number of iteration = 40 and the number of agent = 15 is the cycle run with the best result which is 5.7151 on the Sum Absolute Error (SAE) value. Figure 3 depicts the result of the best minimum result from the MATLAB program shows in a line chart. According to the graph, the SAE value is at about 7.8 at the starting point and ends at                                   VOL. 16, NO. 7, APRIL 2021                                                                                                                 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.  www.arpnjournals.com                                                                                                                                                   760 about 5.7. The trade-off can be seen for OS, ST & SSE where there are certain iteration SAE decreases but either give a counter-productive effect to OS, ST or SSE.    Figure-3. The best minimum result.  CONCLUSIONS This paper presented the implementation of the BFA in tuning PID Controller parameters for Couple Tank System application. The model of BFA proposed explained concisely. The result obtained were discussed adequately. The result show great correlation between number of iteration and number of agent with better solution found by the proposed approach. Having said that, the proposed approach should be tested with larger number of agent and iteration in order to better gauge the effectiveness of the proposed approach with other algorithms.   ACKNOWLEDGEMENT The authors would like to thank Centre for Research and Innovation Management of Universiti Teknikal Malaysia Melaka (UTeM) for sponsoring this work under the Grant PJP/2019/FTKEE (3A)/S01655.  REFERENCES  [1] Khairuddin I. M., Dahalan A. S. A., Abidin A. F. Z., Lai Y. Y., Nordin N. A., Sulaiman S. F., Jaafar H. I., Mohamad S. H. & Amer N. H. 2014. Modeling and simulation of swarm intelligence algorithms for parameters tuning of PID controller in industrial couple tank system. Advanced Materials Research. 903(2014): 321-326.  [2] Hussien S. Y. S., Jaafar H. I., Selamat E. F., Shair E. F. & Abidin A. F. Z. 2015. Development of mathematical model for coupled tanks system using system identification (SI). International Journal of Innovative Technology and Exploring Engineering (IJITEE). 4(12): 73-77. [3] Hussien S. Y. S., Jaafar H. I., Selamat N. A., Daud F. S. & Abidin A. F. Z. 2014. PID control tuning via particle swarm optimization for coupled tank system. International Journal of Soft Computing and Engineering (IJSCE). 4(2): 202-206. [4] M. B. N. Shah et al. 2019. PID-based temperature control device for electric kettle. International Journal of Electrical and Computer Engineering. 9(3): 1683-1693. [5] A. H. Azahar, C. S. Horng, A. M. Kassim and A. F. Z. Abidin. 2018. Optimizing central pattern generators (CPG) controller for one legged hopping robot by using genetic algorithm (GA). International Journal of Engineering & Technology. 7(2.14): 160-164. [6] Yang X. S. 2014. Cuckoo search and firefly algorithm: Theory and application. Springer International Publishing. [7] Ismail M. M., Othman M. A., Sulaiman H. A., Misran M. H., Ramlee R. H., Abidin  A. F. Z., Nordin N. A., Zakaria M. I., Ayob M. N., Yakop F. 2012. Firefly algorithm for path optimization in PCB holes drilling process. 2012 International Conference on Green and Ubiquitous Technology. IEEE. pp. 110-113. [8] Ayob M. N., Hassan F., Ismail A. H., Basri H. H., Azmi M. S., Abidin A. F. Z. 2012. A firefly algorithm                                   VOL. 16, NO. 7, APRIL 2021                                                                                                                 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2021 Asian Research Publishing Network (ARPN). All rights reserved.  www.arpnjournals.com                                                                                                                                                   761 approach for routing in VLSI. 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE). IEEE. pp. 43-47. [9] Ismail M. M. et al. 2013. Route planning analysis in holes drilling process using magnetic optimization algorithm for electronic manufacturing sector. World Applied Sciences Journal 21, 91-97. 

Application Of Binary Firefly Algorithm (BFA) In Tuning PID Parameters For Couple Tank System

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Application of Binary Firefly Algorithm (BFA) in tuning PID parameters for couple tank system

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