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Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed

Daya Shankar Pandey1, Indranil Pan2, Saptarshi Das3

  • 1Carbolea Research Group, Department of Chemical and Environmental Science, University of Limerick, Ireland.

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|January 11, 2015
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Summary

A novel multi-gene genetic programming model accurately predicts syngas yield and heating value for municipal solid waste gasification. This data-driven approach shows promise for modeling complex thermochemical processes with other fuel types.

Keywords:
Fluidized bed gasifierGasificationGenetic programmingMunicipal solid waste

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Area of Science:

  • Chemical Engineering
  • Computational Science

Background:

  • Municipal solid waste (MSW) gasification is a key thermochemical conversion process.
  • Accurate prediction of syngas yield and lower heating value (LHV) is crucial for process optimization.
  • Existing modeling techniques may struggle with the inherent nonlinearities of gasification.

Purpose of the Study:

  • To propose and evaluate a multi-gene genetic programming (GP) technique for predicting MSW gasification outputs.
  • To assess the model's accuracy against experimental data and its generalization capability.
  • To compare the performance of multi-gene GP against single-gene GP.

Main Methods:

  • Utilized published experimental datasets for training and validation.
  • Developed a multi-gene GP model to predict syngas yield and LHV.
  • Compared multi-gene GP with single-gene GP for performance analysis.

Main Results:

  • The multi-gene GP model demonstrated good agreement with experimental data.
  • The model showed strong generalization capabilities on validation (untrained) data.
  • GP proved effective for solving complex nonlinear regression problems in gasification.

Conclusions:

  • The proposed multi-gene GP technique is a viable data-driven modeling strategy for MSW gasification.
  • This approach shows potential for application to other fuel types.
  • GP offers a robust method for modeling complex thermochemical processes.