Development of lower heating value prediction models and estimation of energy recovery potential of municipal solid waste and RDF incineration

Atul Kumar and Sukha Ranjan Samadder

Energy, 2023, vol. 274, issue C

Abstract: Due to heterogeneity in municipal solid waste (MSW) characteristics and composition, the already available heating value estimation methods cannot be accurately applied for the cities of developing countries. In this study, both linear and non-linear approaches have been used for the development of lower heating value (LHV) prediction models. The models have been developed based on the physical composition, proximate analysis, and ultimate analysis of both mixed MSW and combustible components (food waste, yard waste, plastic, paper & cardboard, textile & rubber) separately. In total, six multiple linear regression (MLR) models and six artificial neural network (ANN) models have been developed. All of them can be used for the timely decision making as per the availability of data and requirements with a sufficient level of accuracy. The physical composition based LHV prediction models (both MLR and ANN) showed highest prediction performance. The models developed using combustible components showed slightly better prediction capabilities than mixed MSW based models. Further, in this study, an appropriate refuse-derived fuel (RDF) mix has been determined based on the ease of recovery of the individual waste components from the mixed MSW stream. Finally, the energy recovery potential from mass-burn incineration and RDF incineration has been evaluated. The energy recovery potential of proposed RDF (combination of plastic, paper & cardboard, textile & rubber and yard waste) and mixed MSW was found to be 1310 kWh/tonne and 837 kWh/tonne of dry weight respectively.

Keywords: Artificial neural network; Heating value; Incineration; Municipal solid waste; Refuse-derived fuel; Regression; Waste to energy (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:274:y:2023:i:c:s0360544223006679

DOI: 10.1016/j.energy.2023.127273

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