ew 19(23): e9

Research Article

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

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  • @ARTICLE{10.4108/eai.11-6-2019.159119,
        author={Obafemi  Olatunji and Stephen  Akinlabi and Nkosinathi  Madushele and Paul A.  Adedeji},
        title={Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={6},
        number={23},
        publisher={EAI},
        journal_a={EW},
        year={2019},
        month={6},
        keywords={Renewable energy, Climate change; energy recovery, enthalpy, environmental pollution, PSO-ANFIS, MSW},
        doi={10.4108/eai.11-6-2019.159119}
    }
    
  • Obafemi Olatunji
    Stephen Akinlabi
    Nkosinathi Madushele
    Paul A. Adedeji
    Year: 2019
    Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery
    EW
    EAI
    DOI: 10.4108/eai.11-6-2019.159119
Obafemi Olatunji1,*, Stephen Akinlabi2, Nkosinathi Madushele1, Paul A. Adedeji1
  • 1: Department of Mechanical Engineering Science, University of Johannesburg, South Africa
  • 2: Department of Mechanical and Industrial Engineering, University of Johannesburg, South Africa
*Contact email: tunjifemi@gmail.com

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).