Biochar production from agricultural biomass through microwave-assisted pyrolysis: predictive modelling and experimental validation of biochar yield

Shardul R. Narde () and Neelancherry Remya ()
Additional contact information
Shardul R. Narde: Indian Institute of Technology Bhubaneswar
Neelancherry Remya: Indian Institute of Technology Bhubaneswar

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 9, No 25, 11089-11102

Abstract: Abstract A machine learning model for microwave-assisted pyrolysis technology was developed in this study. The data set of 112 unique experiments was created by analysing the published literature based on biochar. The linear, interactive and quadratic regression models were trained with the selected data set. Out of three regressions models, the quadratic model $${\text{Biochar Yield}} = - 601.78 + 17.336 \times {\text{VM}} + 25.338 \times {\text{AC}} - 0.26367 \times T - 0.293 \times {\text{VM}} \times {\text{AC}} - 0.10305 \times {\text{VM}}^{2} - 0.1893 \times {\text{AC}}^{2} + 1.59 \times 10^{ - 3} \times T^{2}$$ Biochar Yield = - 601.78 + 17.336 × VM + 25.338 × AC - 0.26367 × T - 0.293 × VM × AC - 0.10305 × VM 2 - 0.1893 × AC 2 + 1.59 × 10 - 3 × T 2 was found to have highest R2 value of 0.894. The predicted model developed based on the data from literature was validated with laboratory experimental results. Prediction and validation results showed that data prediction can be a useful tool for the preview of selected feedstock and process parameters. Volatile matter, ash content and temperature were found to be the prominent factors in the trained model. Biochar yield was predicted with a minimum root mean square error of 7 when validated with experimental results. Thus, the predicted model can be used as empirical equation for future experiments to predict biochar yield.

Keywords: Microwave-assisted pyrolysis; Biochar yield; Regression model; Prediction; Validation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10668-021-01898-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:24:y:2022:i:9:d:10.1007_s10668-021-01898-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-021-01898-9

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().