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Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integra...

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Main Authors: Ul Haq, Zeeshan, Ullah, Hafeez, Khan, Muhammad Nouman Aslam, Naqvi, Salman Raza, Abdul Ahad, Abdul Ahad, Saidina Amin, Nor Aishah
Format: Article
Published: Elsevier Ltd 2022
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TP Chemical technology
Online Access:http://eprints.utm.my/101251/
http://dx.doi.org/10.1016/j.biortech.2022.128008
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spelling my.utm.1012512023-11-13T06:17:47Z http://eprints.utm.my/101251/ Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction Ul Haq, Zeeshan Ullah, Hafeez Khan, Muhammad Nouman Aslam Naqvi, Salman Raza Abdul Ahad, Abdul Ahad Saidina Amin, Nor Aishah TP Chemical technology In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R2 = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield. Elsevier Ltd 2022 Article PeerReviewed Ul Haq, Zeeshan and Ullah, Hafeez and Khan, Muhammad Nouman Aslam and Naqvi, Salman Raza and Abdul Ahad, Abdul Ahad and Saidina Amin, Nor Aishah (2022) Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction. Bioresource Technology, 363 (NA). pp. 1-11. ISSN 0960-8524 http://dx.doi.org/10.1016/j.biortech.2022.128008 DOI : 10.1016/j.biortech.2022.128008
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TP Chemical technology
spellingShingle TP Chemical technology
Ul Haq, Zeeshan
Ullah, Hafeez
Khan, Muhammad Nouman Aslam
Naqvi, Salman Raza
Abdul Ahad, Abdul Ahad
Saidina Amin, Nor Aishah
Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
description In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R2 = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.
format Article
author Ul Haq, Zeeshan
Ullah, Hafeez
Khan, Muhammad Nouman Aslam
Naqvi, Salman Raza
Abdul Ahad, Abdul Ahad
Saidina Amin, Nor Aishah
author_facet Ul Haq, Zeeshan
Ullah, Hafeez
Khan, Muhammad Nouman Aslam
Naqvi, Salman Raza
Abdul Ahad, Abdul Ahad
Saidina Amin, Nor Aishah
author_sort Ul Haq, Zeeshan
title Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
title_short Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
title_full Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
title_fullStr Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
title_full_unstemmed Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
title_sort comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction
publisher Elsevier Ltd
publishDate 2022
url http://eprints.utm.my/101251/
http://dx.doi.org/10.1016/j.biortech.2022.128008
_version_ 1783876348247277568
score 13.211869

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