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Related Experiment Video

Updated: Jan 6, 2026

Reducing Willow Wood Fuel Emission by Low Temperature Microwave Assisted Hydrothermal Carbonization
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A heating value estimation of refuse derived fuel using the genetic programming model.

Kemal Özkan1, Şahin Işık1, Zerrin Günkaya2

  • 1Department of Computer Engineering, Eskişehir Osmangazi University, Meşelik Campus, 26480 Eskişehir, Turkey.

Waste Management (New York, N.Y.)
|October 4, 2019
PubMed
Summary

Predicting the Higher Heating Value (HHV) of Refuse Derived Fuel (RDF) is crucial for its use in cement kilns. This study developed Genetic Programming models using proximate analysis, with GP Model #2 showing high accuracy for reliable HHV estimation.

Keywords:
Correlation analysisGenetic programmingHigher heating valueRefused derived fuel

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Area of Science:

  • Waste Management & Energy Recovery
  • Computational Chemistry & Materials Science

Background:

  • Refuse Derived Fuel (RDF) is vital for sustainable waste management and energy generation in cement kilns.
  • Higher Heating Value (HHV) is the key parameter for evaluating RDF fuel performance.
  • Direct HHV measurement requires specialized equipment (calorimetric bomb), while indirect methods (ultimate or proximate analysis) also present challenges for cement plants.

Purpose of the Study:

  • To develop accurate predictive models for RDF's Higher Heating Value (HHV) using proximate analysis data.
  • To overcome the limitations of direct HHV measurement and complex indirect methods in cement plant settings.
  • To evaluate the efficacy of two distinct Genetic Programming (GP) models for HHV prediction.

Main Methods:

  • Utilized two Genetic Programming (GP) models: GP Model #1 (nonlinear mapping) and GP Model #2 (inclusive nonlinear correlation).
  • Employed proximate analysis results as input for the GP models to predict HHV.
  • Validated model performance using simulated test data and statistical error metrics.

Main Results:

  • GP Model #1 achieved R² = 0.9951, RMSE = 1.4126, and AAE = 0.0543.
  • GP Model #2 demonstrated superior performance with R² = 0.9988, RMSE = 0.6971, and AAE = 0.0251.
  • Both models showed high accuracy, with GP Model #2 offering enhanced predictive capability.

Conclusions:

  • Genetic Programming models effectively predict the HHV of RDF using proximate analysis.
  • GP Model #2 provides a reliable and accurate method for HHV estimation, suitable for cement plant applications.
  • The developed models offer a practical alternative to traditional HHV determination methods.