Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms

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 (R² ∼ 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.

Keywords: Biochar; Machine Learning; Neural Networks; Optimization; Pyrolysis.