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Influence of Biomass Composition and Microwave Pyrolysis Conditions on Biochar Yield and its Properties: a Machine Learning Approach

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Abstract

The investigation of microwave pyrolysis behavior and interactive effects of process parameters through machine learning is necessary to systematically determine the combined effects on the yield and characteristics of biochar. This study involves the prediction of microwave biochar yield and its property using various machine learning approaches. Based on the input data of feedstock characteristics (elemental and proximate composition) and operating conditions of microwave pyrolysis (microwave power, time, weight, absorber), the output targets like biochar yield and higher heating value (HHV) have been predicted. The results suggested that eXtreme Gradient Boosting (XGB) model with optimal hyper-parameters could predict the yield and HHV of microwave-derived biochar with higher correlation coefficient (R2) of 0.91. The impact of each factor on output target and their interactions during microwave pyrolysis has been observed from SHAP (SHapley Additive exPlanations) dependence plots. The study outcome revealed that microwave power is the most significant feature influencing the yield of biochar and its property (HHV). The present work gives an insight through computational approach in improving microwave pyrolysis of biomass for enhanced biochar yield and its properties.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank the Department of Biotechnology and Medical Engineering of National Institute of Technology Rourkela for providing the research facility.

Funding

This work was supported by [Science and Engineering Research Board, Department of Science and Technology (SERB-DST), India] (Grant numbers [ECR/ES/2017/003397]. The author MSS has received PhD research support from Ministry of Education (MoE), Government of India (GoI).

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Authors and Affiliations

  1. Agricultural and Environmental Biotechnology Group, Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    S. Mari Selvam & Paramasivan Balasubramanian

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  1. S. Mari Selvam
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  2. Paramasivan Balasubramanian
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Mari Selvam S], [Balasubramanian P]. The first draft of the manuscript was written by [Mari Selvam S] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Paramasivan Balasubramanian.

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Mari Selvam, S., Balasubramanian, P. Influence of Biomass Composition and Microwave Pyrolysis Conditions on Biochar Yield and its Properties: a Machine Learning Approach. Bioenerg. Res. 16, 138–150 (2023). https://doi.org/10.1007/s12155-022-10447-9

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