Open AccessEvaluation and Prediction of Energy Content of Municipal Solid Waste: A review
Author(s) -
Oluwatobi Adeleke,
Stephen A. Akinlabi,
Tien-Chien Jen,
Israel Dunmade
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1107/1/012097
Subject(s) - municipal solid waste , predictive modelling , energy recovery , heat of combustion , linear regression , regression analysis , environmental science , waste management , energy (signal processing) , resource recovery , waste to energy , computer science , process engineering , engineering , mathematics , environmental engineering , machine learning , statistics , wastewater , chemistry , combustion , organic chemistry
Researches in the literature have unveiled the potential of resource and energy recovery from waste, it can therefore no longer be regarded as trash. This study reviews the literature to evaluate and analyses studies which estimated the experimental heating value of waste and the theoretical energy potential recoverable from waste through thermochemical and biochemical routes at different case studies. It was observed in this study that most developing countries are not exploiting the full potential of energy recoverable from waste. Models developed to predict the energy content of municipal solid waste (MSW) based on the elemental analysis, proximate analysis and physical composition were evaluated. A comparative analysis of the energy prediction models was also done. Artificial neural network (ANN) and multiple linear regressions found more applications in energy prediction. Energy prediction based on ultimate analysis using the elemental composition of the waste was predominant and are the most accurate; while proximate analysis based predictions were the least. The prediction accuracy of ANN is greater than the linear regression in the forecast of the energy content of MSW. However, a major limitation in the use of these modelling techniques was identified. Most of the generalized models may not capture the peculiarity of the waste generated at a particular place or municipality and therefore may not be very accurate for specific applications at some municipalities.