Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste

Parthasarathy Velusamy, Jagadeesan Srinivasan, Nithyaselvakumari Subramanian, Rakesh Kumar Mahendran, Muhammad Qaiser Saleem, Maqbool Ahmad, Muhammad Shafiq () and Jin-Ghoo Choi ()
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Parthasarathy Velusamy: Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Coimbatore 641021, India
Jagadeesan Srinivasan: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Nithyaselvakumari Subramanian: Department of Biomedical Engineering, Saveetha School of Engineering, Chennai 602105, India
Rakesh Kumar Mahendran: Department of Computer Science and Engineering, School of Computing, Rajalakshmi Engineering College, Chennai 602105, India
Muhammad Qaiser Saleem: College of Computer Science and Information Technology, Al Baha University, Al Baha 1988, Saudi Arabia
Maqbool Ahmad: School of Digital Convergence Business, University of Central Punjab, Rawalpindi 46000, Pakistan
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
Jin-Ghoo Choi: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea

Sustainability, 2023, vol. 15, issue 7, 1-14

Abstract: Municipal solid waste (MSW) management is an essential element of present-day society. The proper storage and disposal of solid waste is critical to public health, safety, and environmental performance. The direct recovery of MSW into useful energy is a critical task. In addition, the demand for conventional power supplies is high. As a strategy to solve these two problems, the technology to directly convert municipal solid waste into conventional energy to replace fossil fuels has been obtained. The hydrothermal carbonization (HTC) process is a thermochemical conversion process that utilizes heat to convert wet biomass feedstocks into hydrochar. Hydrochar with premium gasoline properties is used for fuel combustion for strength. The properties of fuel hydrochar, including C char (carbon content), HHV (higher heating value), and yield, are mainly based on the properties of the MSW. This study aimed to predict the properties of fuel hydrochar using a machine learning (ML) model. We employed an ensemble support vector machine (E-SVM) as the classifier, which was combined with the slime mode algorithm (SMA) for optimization and developed based on 281 data points. The model was primarily trained and tested on a fusion of three datasets: sewage sludge, leftovers, and cow dung. The proposed ESVM_SMA model achieved an excellent overall performance with an average R 2 of 0.94 and RMSE of 2.62.

Keywords: municipal solid waste; hydrothermal carbonization; slime mould algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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