Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms. (July 2022)

• Record Type: Journal Article
• Authors: Khan, Muzammil; Ullah, Zahid; Mašek, Ondřej; Raza Naqvi, Salman; Nouman Aslam Khan, Muhammad
• Is Part Of: Bioresource technology. Volume 355(2022)
• Publication Date: 2022-07
• Subjects: Pyrolysis -- Biochar -- Optimization -- Machine Learning -- Neural Networks; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.127215
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: ANN coupled with metaheuristic algorithms were used to predict biochar yield. ANN integrated with Rao-2 algorithm outperformed all other models (R2 ∼ 0.93). Pyrolysis temperature and residence time were the most important features. Partial dependence analysis revealed inside details for the pyrolysis process. An easy-to-use graphical user interface was developed for biochar yield prediction. Abstract: In this study, an integrated framework of artificial neural networks (ANNs) and metaheuristic algorithms have been developed for the prediction of biochar yield using biomass characteristics and pyrolysis process conditions. Comparative analysis of six different metaheuristic algorithms was performed to optimize the ANN architecture and select important features. The results suggested that the ANN model coupled with the Rao-2 algorithm outperformed (R 2 ∼ 0.93, RMSE ∼ 1.74%) all other models. Furthermore, the detailed information behind the models was acquired, identifying the most influencing factors as follows: pyrolysis temperature (56%), residence time (23%), and heating rate (8%). The partial dependence plot analysis revealed how each influencing factor affected the target variable. Finally, an easy-to-use software tool for predicting biochar yield was built using the ANN-Rao-2 model. This study demonstrates huge potential that machine learning presents in predictive modelling of complex pyrolysis processes, and reduces the time-consuming and expensive experimental work for estimating the biochar yield.