Faruk Geyik - Academia.edu (original) (raw)

Papers by Faruk Geyik

Research paper thumbnail of A Study of Maturity Model for Assessing the Logistics 4.0 Transformation Level of Industrial Enterprises

Research paper thumbnail of The strategies and parameters of tabu search for job-shop scheduling

Journal of Intelligent Manufacturing, 2004

This paper presents a tabu search approach for the job-shop scheduling problem. Although the prob... more This paper presents a tabu search approach for the job-shop scheduling problem. Although the problem is NP-hard, satisfactory solutions have been obtained recently by tabu search. However, tabu search has a problem-speci®c and parametric structure. Therefore, in the paper, we focussed on the tabu search strategies and parameters such as initial solution, neighborhood structure, tabu list, aspiration criterion, elite solutions list, intensi®cation, diversi®cation and the number of iteration. In order to compare some neighborhood strategies and tabu list length methods, a computational study is done on the benchmark problems.

Research paper thumbnail of Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model

Bioresource Technology, 2012

An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (L... more An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.

Research paper thumbnail of Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

Bioresource Technology, 2011

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency o... more A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.

Research paper thumbnail of Examining the Relation Between the Number and Location of Tuck Stitches and Bursting Strength in Circular Knitted Fabrics

Fibres and Textiles in Eastern Europe

Single jersey fabrics are the most common type among knitted fabrics. Their patterns are designed... more Single jersey fabrics are the most common type among knitted fabrics. Their patterns are designed by loops, tuck and float stitches and their combinations. The tuck stitch has important influences on fabric properties as it increases the fabric weight, thickness and width, and makes the fabric more porous than others. In this research 12 different circular knitted fabrics were produced with different numbers of tuck stitches and were also dyed. As the most important performance property for knitted fabrics, their bursting strength properties were investigated. The aim of the study was to examine the relation between the tuck stitches and bursting strength in circular knitted fabrics according to the number and location of tuck stitches in the pattern. To achieve the most correct result, graphics and statistical analyses were used.

Research paper thumbnail of A linguistic approach to non-identical parallel processor scheduling with fuzzy processing times

Applied Soft Computing, 2017

Display Omitted This study presents a potentially contribution to the limited literature of the n... more Display Omitted This study presents a potentially contribution to the limited literature of the non-identical or unrelated parallel processor scheduling problem under uncertainty.A linguistic approach to non-identical parallel processor scheduling under fuzzy processing times with learning effects is the first time studied.The Mamdani inference method is also the first time used to obtain the crisp processing times of non-identical processors by the help of a rule base with expert knowledge, in spite it has been used in many mechanic and electronic control problems and many decision problems before. This study presents an application of non-identical parallel processor scheduling under uncertain operation times. We have been motivated from a real case scheduling problem that contains some uncommon welding operations to be processed by workers in an automotive subcontract company. Here each operator may weld each job but in different processing times depending on learning effect because of operators ability and experience, and batch sizes. To determine the crisp operation times in such a fuzzy environment, a linguistic reasoning approach (with a 75-If- Then rules) considering the learning effect is proposed in the study. Since the fuzzy linguistic approach allows the representation of expert information more directly and adequately, it can be more possible to make realistic schedules under uncertainty. With the objective to balance the workload among all operators, the longest processing time heuristic algorithm is been used and measured average makespan. For evaluating the effectiveness of this approach, it is compared with the scheduling method that use the random operation times generated from a uniform distribution. Results showed that the proposed fuzzy linguistic scheduling approach has balanced the workload of operators with a standard deviation of 0.37 and improved the Cmax value as 16%. A general conclusion can be drawn the proposed approach is able to generate realistic schedules and especially useful to solve non-identical parallel processor scheduling problem under uncertainty. An important contribution of this study is that Mamdani inference method with learning effect is the first time used to obtain the crisp processing times of non-identical processors by the help of a rule base with expert knowledge.

Research paper thumbnail of Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization

SIMULATION, 2017

A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, a... more A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, and the presence of complex interactions between supply chain members, can become quite challenging to optimize and requires a complex model. At this point, the Simulation Optimization (SO) model gains a better understanding of the complex and messy phenomenon of the inventory control of supply chain members. By creating SO models for Distribution Center (DC)s and Suppliers, we wish to present flexible and comprehensible research on the important decision of whether to minimize the differences between total overordering cost and total underordering cost (Model 1) or to minimize the total supply chain cost (Model 2). We also try to point out several important issues: the optimal value of the initial inventory, the reorder point, and the order-up-to level in continuous (s, S) policy for each DC and each Supplier; whether SO models can successfully integrate the supplier selection and contin...

Research paper thumbnail of (R, s, S) inventory control policy and supplier selection in a two-echelon supply chain: An Optimization via Simulation approach

2015 Winter Simulation Conference (WSC), 2015

Research paper thumbnail of Predicting the Crushing Strength of Cold-Bonded Artificial Aggregates by Genetic Algorithms

Research paper thumbnail of Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility

The International Journal of Advanced Manufacturing Technology, 2015

Flexibility is an important task for effectively utilizing resources in a manufacturing system an... more Flexibility is an important task for effectively utilizing resources in a manufacturing system and responding demands rapidly. In manufacturing systems, there exist different types of flexibility levels. In this study, the stochastic flexible job shop scheduling problem is considered to measure the impact of routing flexibility on shop performance. Thus, an integrated genetic algorithm-Monte Carlo method is proposed to analyze the effect of routing flexibility. To make the problem more realistic, system parameters (processing times, operation sequences, etc.) are generated randomly via Monte Carlo. An experimental design is utilized to analyze main and interaction effects of the factors considered (i.e., number of parts, number of machines, number of operations, and flexibility levels) by using a genetic algorithm which is specifically designed for the stochastic flexible job shop scheduling problem. In developed genetic algorithm, different initial strategies which not only improve solution quality but also decrease solution time are used. Makespan is specified as the objective function to be minimized. Results are analyzed with a full factorial analysis of variance. Comprehensive discussions of results are given case by case.

Research paper thumbnail of Artificial Neural Network and Genetic Algorithm Hybrid Technique for Nucleus–Nucleus Collisions

International Journal of Modern Physics C, 2008

Selecting the optimal topology of a neural network for a particular application is a difficult ta... more Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28, and S 32 on nuclear emulsion. An efficient NN has been designed by GA to predict the distributions that are not present in the training set and matched them effectively. The proposed method shows a better fitting with experimental data. The hybrid technique GA–ANN simulation results prove a strong presence modeling in heavy ion collisions.

Research paper thumbnail of Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

Neural Computing and Applications, 2013

This paper presents an optimization via simulation approach to solve dynamic flexible job shop sc... more This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.

Research paper thumbnail of Artificial Neural Network and Genetic Algorithm Hybrid Technique for Nucleus–Nucleus Collisions

International Journal of Modern Physics C, 2008

Selecting the optimal topology of a neural network for a particular application is a difficult ta... more Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C12, O16, Si28, and S32 on nuclear emulsion. An efficient NN has been designed by GA to

Research paper thumbnail of Tabu Arama Tekniği ile Klasik İş-Atölyesi Çizelgeleme

Research paper thumbnail of Use of artificial neural networks and genetic algorithms for prediction of sorption of an azo-metal complex dye onto lentil straw

Bioresource Technology, 2013

h i g h l i g h t s " Predictive modeling of sorption of Lanaset Red (LR)G on lentil straw was st... more h i g h l i g h t s " Predictive modeling of sorption of Lanaset Red (LR)G on lentil straw was studied. " Artificial neural network (ANN) was found to be excellent model in representing the sorption kinetics data. " The sorption at various operating factors in a single equation was described by gene expression programming (GEP).

Research paper thumbnail of The strategies and parameters of tabu search for job-shop scheduling

Journal of Intelligent Manufacturing, 2004

This paper presents a tabu search approach for the job-shop scheduling problem. Although the prob... more This paper presents a tabu search approach for the job-shop scheduling problem. Although the problem is NP-hard, satisfactory solutions have been obtained recently by tabu search. However, tabu search has a problem-specific and parametric structure. Therefore, in the paper, we focussed on the tabu search strategies and parameters such as initial solution, neighborhood structure, tabu list, aspiration criterion, elite solutions list, intensification, diversification and the number of iteration. In order to compare some neighborhood strategies and tabu list length methods, a computational study is done on the benchmark problems.

Research paper thumbnail of Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model

Bioresource Technology

An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (L... more An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.

Research paper thumbnail of Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

Bioresource Technology, 2011

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency o... more A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.

Research paper thumbnail of A Study of Maturity Model for Assessing the Logistics 4.0 Transformation Level of Industrial Enterprises

Research paper thumbnail of The strategies and parameters of tabu search for job-shop scheduling

Journal of Intelligent Manufacturing, 2004

This paper presents a tabu search approach for the job-shop scheduling problem. Although the prob... more This paper presents a tabu search approach for the job-shop scheduling problem. Although the problem is NP-hard, satisfactory solutions have been obtained recently by tabu search. However, tabu search has a problem-speci®c and parametric structure. Therefore, in the paper, we focussed on the tabu search strategies and parameters such as initial solution, neighborhood structure, tabu list, aspiration criterion, elite solutions list, intensi®cation, diversi®cation and the number of iteration. In order to compare some neighborhood strategies and tabu list length methods, a computational study is done on the benchmark problems.

Research paper thumbnail of Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model

Bioresource Technology, 2012

An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (L... more An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.

Research paper thumbnail of Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

Bioresource Technology, 2011

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency o... more A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.

Research paper thumbnail of Examining the Relation Between the Number and Location of Tuck Stitches and Bursting Strength in Circular Knitted Fabrics

Fibres and Textiles in Eastern Europe

Single jersey fabrics are the most common type among knitted fabrics. Their patterns are designed... more Single jersey fabrics are the most common type among knitted fabrics. Their patterns are designed by loops, tuck and float stitches and their combinations. The tuck stitch has important influences on fabric properties as it increases the fabric weight, thickness and width, and makes the fabric more porous than others. In this research 12 different circular knitted fabrics were produced with different numbers of tuck stitches and were also dyed. As the most important performance property for knitted fabrics, their bursting strength properties were investigated. The aim of the study was to examine the relation between the tuck stitches and bursting strength in circular knitted fabrics according to the number and location of tuck stitches in the pattern. To achieve the most correct result, graphics and statistical analyses were used.

Research paper thumbnail of A linguistic approach to non-identical parallel processor scheduling with fuzzy processing times

Applied Soft Computing, 2017

Display Omitted This study presents a potentially contribution to the limited literature of the n... more Display Omitted This study presents a potentially contribution to the limited literature of the non-identical or unrelated parallel processor scheduling problem under uncertainty.A linguistic approach to non-identical parallel processor scheduling under fuzzy processing times with learning effects is the first time studied.The Mamdani inference method is also the first time used to obtain the crisp processing times of non-identical processors by the help of a rule base with expert knowledge, in spite it has been used in many mechanic and electronic control problems and many decision problems before. This study presents an application of non-identical parallel processor scheduling under uncertain operation times. We have been motivated from a real case scheduling problem that contains some uncommon welding operations to be processed by workers in an automotive subcontract company. Here each operator may weld each job but in different processing times depending on learning effect because of operators ability and experience, and batch sizes. To determine the crisp operation times in such a fuzzy environment, a linguistic reasoning approach (with a 75-If- Then rules) considering the learning effect is proposed in the study. Since the fuzzy linguistic approach allows the representation of expert information more directly and adequately, it can be more possible to make realistic schedules under uncertainty. With the objective to balance the workload among all operators, the longest processing time heuristic algorithm is been used and measured average makespan. For evaluating the effectiveness of this approach, it is compared with the scheduling method that use the random operation times generated from a uniform distribution. Results showed that the proposed fuzzy linguistic scheduling approach has balanced the workload of operators with a standard deviation of 0.37 and improved the Cmax value as 16%. A general conclusion can be drawn the proposed approach is able to generate realistic schedules and especially useful to solve non-identical parallel processor scheduling problem under uncertainty. An important contribution of this study is that Mamdani inference method with learning effect is the first time used to obtain the crisp processing times of non-identical processors by the help of a rule base with expert knowledge.

Research paper thumbnail of Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization

SIMULATION, 2017

A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, a... more A real-world inventory control system, due to its nonlinear, stochastic, time-dependent nature, and the presence of complex interactions between supply chain members, can become quite challenging to optimize and requires a complex model. At this point, the Simulation Optimization (SO) model gains a better understanding of the complex and messy phenomenon of the inventory control of supply chain members. By creating SO models for Distribution Center (DC)s and Suppliers, we wish to present flexible and comprehensible research on the important decision of whether to minimize the differences between total overordering cost and total underordering cost (Model 1) or to minimize the total supply chain cost (Model 2). We also try to point out several important issues: the optimal value of the initial inventory, the reorder point, and the order-up-to level in continuous (s, S) policy for each DC and each Supplier; whether SO models can successfully integrate the supplier selection and contin...

Research paper thumbnail of (R, s, S) inventory control policy and supplier selection in a two-echelon supply chain: An Optimization via Simulation approach

2015 Winter Simulation Conference (WSC), 2015

Research paper thumbnail of Predicting the Crushing Strength of Cold-Bonded Artificial Aggregates by Genetic Algorithms

Research paper thumbnail of Integration of genetic algorithm and Monte Carlo to analyze the effect of routing flexibility

The International Journal of Advanced Manufacturing Technology, 2015

Flexibility is an important task for effectively utilizing resources in a manufacturing system an... more Flexibility is an important task for effectively utilizing resources in a manufacturing system and responding demands rapidly. In manufacturing systems, there exist different types of flexibility levels. In this study, the stochastic flexible job shop scheduling problem is considered to measure the impact of routing flexibility on shop performance. Thus, an integrated genetic algorithm-Monte Carlo method is proposed to analyze the effect of routing flexibility. To make the problem more realistic, system parameters (processing times, operation sequences, etc.) are generated randomly via Monte Carlo. An experimental design is utilized to analyze main and interaction effects of the factors considered (i.e., number of parts, number of machines, number of operations, and flexibility levels) by using a genetic algorithm which is specifically designed for the stochastic flexible job shop scheduling problem. In developed genetic algorithm, different initial strategies which not only improve solution quality but also decrease solution time are used. Makespan is specified as the objective function to be minimized. Results are analyzed with a full factorial analysis of variance. Comprehensive discussions of results are given case by case.

Research paper thumbnail of Artificial Neural Network and Genetic Algorithm Hybrid Technique for Nucleus–Nucleus Collisions

International Journal of Modern Physics C, 2008

Selecting the optimal topology of a neural network for a particular application is a difficult ta... more Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28, and S 32 on nuclear emulsion. An efficient NN has been designed by GA to predict the distributions that are not present in the training set and matched them effectively. The proposed method shows a better fitting with experimental data. The hybrid technique GA–ANN simulation results prove a strong presence modeling in heavy ion collisions.

Research paper thumbnail of Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

Neural Computing and Applications, 2013

This paper presents an optimization via simulation approach to solve dynamic flexible job shop sc... more This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.

Research paper thumbnail of Artificial Neural Network and Genetic Algorithm Hybrid Technique for Nucleus–Nucleus Collisions

International Journal of Modern Physics C, 2008

Selecting the optimal topology of a neural network for a particular application is a difficult ta... more Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C12, O16, Si28, and S32 on nuclear emulsion. An efficient NN has been designed by GA to

Research paper thumbnail of Tabu Arama Tekniği ile Klasik İş-Atölyesi Çizelgeleme

Research paper thumbnail of Use of artificial neural networks and genetic algorithms for prediction of sorption of an azo-metal complex dye onto lentil straw

Bioresource Technology, 2013

h i g h l i g h t s " Predictive modeling of sorption of Lanaset Red (LR)G on lentil straw was st... more h i g h l i g h t s " Predictive modeling of sorption of Lanaset Red (LR)G on lentil straw was studied. " Artificial neural network (ANN) was found to be excellent model in representing the sorption kinetics data. " The sorption at various operating factors in a single equation was described by gene expression programming (GEP).

Research paper thumbnail of The strategies and parameters of tabu search for job-shop scheduling

Journal of Intelligent Manufacturing, 2004

This paper presents a tabu search approach for the job-shop scheduling problem. Although the prob... more This paper presents a tabu search approach for the job-shop scheduling problem. Although the problem is NP-hard, satisfactory solutions have been obtained recently by tabu search. However, tabu search has a problem-specific and parametric structure. Therefore, in the paper, we focussed on the tabu search strategies and parameters such as initial solution, neighborhood structure, tabu list, aspiration criterion, elite solutions list, intensification, diversification and the number of iteration. In order to compare some neighborhood strategies and tabu list length methods, a computational study is done on the benchmark problems.

Research paper thumbnail of Prediction of removal efficiency of Lanaset Red G on walnut husk using artificial neural network model

Bioresource Technology

An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (L... more An artificial neural network (ANN) model was used to predict removal efficiency of Lanaset Red (LR) G on walnut husk (WH). This adsorbent was characterized by FTIR-ATR. Effects of particle size, adsorbent dose, initial pH value, dye concentration, and contact time were investigated to optimize sorption process. Operating variables were used as the inputs to the constructed neural network to predict the dye uptake at any time as an output. Commonly used pseudo second-order model was fitted to the experimental data to compare with ANN model. According to error analyses and determination of coefficients, ANN was the more appropriate model to describe this sorption process. Results of ANN indicated that pH was the most efficient parameter (43%), followed by initial dye concentration (40%) for sorption of LR G on WH.

Research paper thumbnail of Artificial neural networks (ANN) approach for modeling of removal of Lanaset Red G on Chara contraria

Bioresource Technology, 2011

A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency o... more A three-layer artificial neural network (ANN) was constructed to predict the removal efficiency of Lanaset Red (LR) G on Chara contraria based on 2304 experimental sets. The effects of operating variables (particle size, adsorbent dosage, pH regimes, dye concentration, and contact time) were studied to optimize the sorption conditions of this dye. The operating variables were used as the input to the constructed neural network to predict the dye uptake at any time as the output. This adsorbent was characterized by FTIR. Pseudo second-order model was also fitted to the experimental data. According to values of error analyses and determinations coefficient, the ANN was more appropriate to describe this adsorption process. Result of this model indicated that pH regimes had the highest importance effect (49%) on the dye uptake.