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Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic

Imane Boumanchar1,2, Younes Chhiti2, Fatima Ezzahrae M'hamdi Alaoui2

  • 11 Laboratory of Catalysis and Corrosion of Materials (LCCM), Chemistry Department, Chouaïb Doukkali University, El Jadida, Morocco.

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA
|December 20, 2018
PubMed
Summary

Predicting the energy content of municipal solid waste (MSW) is crucial for its use as a fuel. This study developed accurate models using ultimate analysis to estimate the higher heating value (HHV) of MSW, outperforming traditional methods.

Keywords:
Energygenetic programminghigher heating valuemultiple regressionmunicipal solid wasteprediction

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Area of Science:

  • Waste Management and Energy Recovery
  • Chemical Engineering and Material Science
  • Environmental Science and Sustainability

Background:

  • Effective municipal solid waste (MSW) management is a global challenge.
  • Understanding the calorific value of MSW is essential for its utilization as a sustainable energy source.
  • Experimental measurement of higher heating value (HHV) using oxygen bomb calorimeters is accurate but costly.

Purpose of the Study:

  • To develop empirical models for predicting the higher heating value (HHV) of municipal solid waste (MSW).
  • To utilize data from ultimate analysis as input for predictive modeling.
  • To compare the efficacy of multiple regression analysis and genetic programming for HHV prediction.

Main Methods:

  • Multiple regression analysis was employed to establish relationships between ultimate analysis components and HHV.
  • Genetic programming (GP) was utilized as a machine learning approach for developing predictive models.
  • Model performance was evaluated using correlation coefficient (CC) and root mean square error (RMSE).

Main Results:

  • Both multiple regression analysis and genetic programming yielded satisfactory results in predicting MSW HHV.
  • Genetic programming demonstrated superior accuracy compared to multiple regression.
  • The developed GP models achieved a high correlation coefficient and a low root mean square error, surpassing previously published results.

Conclusions:

  • Empirical models based on ultimate analysis provide a cost-effective alternative to experimental HHV measurement.
  • Genetic programming offers a highly accurate and reliable method for predicting the higher heating value of municipal solid waste.
  • This research contributes to optimizing waste-to-energy strategies through improved calorific value assessment.