Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass
In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/72245/ http://psasir.upm.edu.my/id/eprint/72245/1/Development.pdf |
| _version_ | 1848857071673409536 |
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| author | Mohammad, Saleem Ethaib Omar, Rozita Mustapa Kamal, Siti Mazlina Awang Biak, Dayang Radiah S., Syafiie |
| author_facet | Mohammad, Saleem Ethaib Omar, Rozita Mustapa Kamal, Siti Mazlina Awang Biak, Dayang Radiah S., Syafiie |
| author_sort | Mohammad, Saleem Ethaib |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R 2) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R 2 of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model. |
| first_indexed | 2025-11-15T11:51:43Z |
| format | Article |
| id | upm-72245 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:51:43Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-722452020-04-20T14:57:27Z http://psasir.upm.edu.my/id/eprint/72245/ Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass Mohammad, Saleem Ethaib Omar, Rozita Mustapa Kamal, Siti Mazlina Awang Biak, Dayang Radiah S., Syafiie In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R 2) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R 2 of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model. Springer 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72245/1/Development.pdf Mohammad, Saleem Ethaib and Omar, Rozita and Mustapa Kamal, Siti Mazlina and Awang Biak, Dayang Radiah and S., Syafiie (2018) Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass. Neural Computing and Applications, 30 (4). 1111 - 1121. ISSN 0941-0643; ESSN: 1433-3058 https://link.springer.com/article/10.1007/s00521-016-2755-0 10.1007/s00521-016-2755-0 |
| spellingShingle | Mohammad, Saleem Ethaib Omar, Rozita Mustapa Kamal, Siti Mazlina Awang Biak, Dayang Radiah S., Syafiie Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| title | Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| title_full | Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| title_fullStr | Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| title_full_unstemmed | Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| title_short | Development of a hybrid PSO-ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| title_sort | development of a hybrid pso-ann model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass |
| url | http://psasir.upm.edu.my/id/eprint/72245/ http://psasir.upm.edu.my/id/eprint/72245/ http://psasir.upm.edu.my/id/eprint/72245/ http://psasir.upm.edu.my/id/eprint/72245/1/Development.pdf |