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Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using

Thavavel Vaiyapuri1

  • 1College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.

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Summary

Optimizing biomass and plastic co-gasification for hydrogen production is crucial for clean energy. Support Vector Regression (SVR) emerged as the best machine learning model, showing high accuracy and interpretability for this complex process.

Keywords:
Shapley Additive Explanations frameworkbiomass gasificationclean energyexplainable artificial intelligenceforce plotsummary plotthermochemical conversion

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Area of Science:

  • Chemical Engineering
  • Sustainable Energy Technologies
  • Machine Learning Applications

Background:

  • Co-gasification of biomass and plastic waste is a key strategy for producing hydrogen-rich syngas to meet growing clean energy demands.
  • Optimizing co-gasification for maximum hydrogen yield is challenging due to diverse feedstocks and process complexities.
  • Existing machine learning (ML) models for co-gasification lack consensus on effectiveness and interpretability, especially with limited data.

Purpose of the Study:

  • To model the co-gasification process using seven distinct ML algorithms.
  • To develop a framework for evaluating ML model interpretability in co-gasification.
  • To identify the most suitable ML model for optimizing hydrogen production from co-gasification.

Main Methods:

  • Conducted comprehensive experiments assessing generalization ability, predictive accuracy, and interpretability of seven ML algorithms.
  • Employed Support Vector Regression (SVR) and compared its performance against other models.
  • Integrated Shapley Additive Explanations (SHAP) for detailed feature importance analysis and model interpretability.

Main Results:

  • Support Vector Regression (SVR) demonstrated superior performance with the highest coefficient of determination (R2) of 0.86.
  • SVR effectively captured non-linear dependencies and mitigated overfitting compared to other models.
  • SHAP analysis provided unprecedented insights into feature importance, confirming ML model feasibility for industrial hydrogen production.

Conclusions:

  • SVR is identified as the most effective ML model for optimizing biomass and plastic co-gasification for hydrogen production.
  • The study establishes a robust framework for evaluating ML model interpretability and performance in this domain.
  • Findings support the advancement of sustainable energy technologies and reduction of greenhouse gas emissions through optimized co-gasification.