Abstract
The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socio-economic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H2O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).
Highlights
About three decades back, the sustainable development criteria have been in the frontline charge of energy policy
From the literature survey to the best of information available to the authors, there is no model which has predicted the High Heating Value of MSW based on PSOANFIS. This current study introduces a Particle Swarm Optimisation (PSO)-adaptive neuro-fuzzy inference systems (ANFIS) model aimed at predicting the enthalpy of combustion of waste-derived fuel based on the moisture content (H2O), Carbon C, Hydrogen H, Oxygen O, Nitrogen N, Sulphur S, and Ash contents
Rather than applying linear regression, where each correlation was proposed, the corresponding coefficient of regression calculated and the error estimated, this study presents an optimized ANFIS model with new correlation and better predictive capacity compared to existing linear models
Summary
The sustainable development criteria have been in the frontline charge of energy policy. The sustainable energy was only defined with a focus on availability as a function of the rate of consumption [1]. Sustainability includes; the generation of fuel from raw materials that are locally sourced, in order to reduce the transportation logistics, and reduce the net greenhouse gas emission [24]. Further development and ongoing discussion around the rising global temperature due to global warming has shown that other aspects of energy generation need to be holistically considered and thoroughly researched. Since energy exploration will obviously impact sustainable development, the environmental effect and demography should be considered. Researchers have redirected to address these fundamental energy issues
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