Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield
This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and l...
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Format: | Article |
Language: | English |
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De Gruyter
2018-09-01
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Series: | Journal of Intelligent Systems |
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Online Access: | https://doi.org/10.1515/jisys-2017-0641 |
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author | Ewees Ahmed A. Elaziz Mohamed Abd |
author_facet | Ewees Ahmed A. Elaziz Mohamed Abd |
author_sort | Ewees Ahmed A. |
collection | DOAJ |
description | This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods. |
first_indexed | 2024-12-17T20:22:05Z |
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id | doaj.art-233d8b9d7bee4d35b5fed97ca5aaf042 |
institution | Directory Open Access Journal |
issn | 0334-1860 2191-026X |
language | English |
last_indexed | 2024-12-17T20:22:05Z |
publishDate | 2018-09-01 |
publisher | De Gruyter |
record_format | Article |
series | Journal of Intelligent Systems |
spelling | doaj.art-233d8b9d7bee4d35b5fed97ca5aaf0422022-12-21T21:33:54ZengDe GruyterJournal of Intelligent Systems0334-18602191-026X2018-09-0129192494010.1515/jisys-2017-0641Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar YieldEwees Ahmed A.0Elaziz Mohamed Abd1University of Bisha, Bisha, Kingdom of Saudi ArabiaDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig, EgyptThis paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods.https://doi.org/10.1515/jisys-2017-0641gray wolf optimization (gwo)adaptive neuro-fuzzy inference system (anfis)renewable energy productionbiochar prediction |
spellingShingle | Ewees Ahmed A. Elaziz Mohamed Abd Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield Journal of Intelligent Systems gray wolf optimization (gwo) adaptive neuro-fuzzy inference system (anfis) renewable energy production biochar prediction |
title | Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield |
title_full | Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield |
title_fullStr | Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield |
title_full_unstemmed | Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield |
title_short | Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield |
title_sort | improved adaptive neuro fuzzy inference system using gray wolf optimization a case study in predicting biochar yield |
topic | gray wolf optimization (gwo) adaptive neuro-fuzzy inference system (anfis) renewable energy production biochar prediction |
url | https://doi.org/10.1515/jisys-2017-0641 |
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