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

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 optimizethe 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.

• Publication year

  2022

• Venue

  Bioresource Technology

• Publication date

  2022-04-01

• Fields of study

  Medicine, Chemistry, Environmental Science

• Identifiers
  DOI 10.2139/ssrn.4047675PMID 35470005
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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