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...

Full description

Bibliographic Details
Main Authors: Ewees Ahmed A., Elaziz Mohamed Abd
Format: Article
Language:English
Published: De Gruyter 2018-09-01
Series:Journal of Intelligent Systems
Subjects:
Online Access:https://doi.org/10.1515/jisys-2017-0641
_version_ 1818720391111114752
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
format Article
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
work_keys_str_mv AT eweesahmeda improvedadaptiveneurofuzzyinferencesystemusinggraywolfoptimizationacasestudyinpredictingbiocharyield
AT elazizmohamedabd improvedadaptiveneurofuzzyinferencesystemusinggraywolfoptimizationacasestudyinpredictingbiocharyield