Vaiyapuri Thavavel
College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Entropy (Basel). 2025 Jan 17;27(1):83. doi: 10.3390/e27010083.
The co-gasification of biomass and plastic waste offers a promising solution for producing hydrogen-rich syngas, addressing the rising demand for cleaner energy. However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data. Additionally, the interpretability of these models is a key concern. This study aims to bridge these gaps through two primary objectives: (1) modeling the co-gasification process using seven different ML algorithms, and (2) developing a framework for evaluating model interpretability, ultimately identifying the most suitable model for process optimization. A comprehensive set of experiments was conducted across three key dimensions, generalization ability, predictive accuracy, and interpretability, to thoroughly assess the models. Support Vector Regression (SVR) exhibited superior performance, achieving the highest coefficient of determination (R2) of 0.86. SVR outperformed other models in capturing non-linear dependencies and demonstrated effective overfitting mitigation. This study further highlights the limitations of other ML models, emphasizing the importance of regularization and hyperparameter tuning in improving model stability. By integrating Shapley Additive Explanations (SHAP) into model evaluation, this work is the first to provide detailed insights into feature importance and demonstrate the operational feasibility of ML models for industrial-scale hydrogen production in the co-gasification process. The findings contribute to the development of a robust framework for optimizing co-gasification, supporting the advancement of sustainable energy technologies and the reduction of greenhouse gas (GHG) emissions.
生物质与塑料废弃物的共气化提供了一种生产富氢合成气的前景广阔的解决方案，满足了对清洁能源日益增长的需求。然而，优化这一复杂过程以实现氢气产量最大化仍然具有挑战性，尤其是在平衡多种原料和提高过程效率方面。虽然机器学习（ML）在模拟和优化此类过程中已显示出巨大潜力，但对于共气化最有效的回归模型尚无明确共识，特别是在实验数据有限的情况下。此外，这些模型的可解释性也是一个关键问题。本研究旨在通过两个主要目标弥合这些差距：（1）使用七种不同的ML算法对共气化过程进行建模，（2）开发一个评估模型可解释性的框架，最终确定最适合过程优化的模型。在泛化能力、预测准确性和可解释性这三个关键维度上进行了一系列全面的实验，以全面评估这些模型。支持向量回归（SVR）表现出卓越的性能，实现了最高的决定系数（R2）为0.86。SVR在捕捉非线性依赖关系方面优于其他模型，并有效缓解了过拟合问题。本研究进一步突出了其他ML模型的局限性，强调了正则化和超参数调整在提高模型稳定性方面的重要性。通过将Shapley值分解法（SHAP）纳入模型评估，本研究首次详细洞察了特征重要性，并证明了ML模型在共气化过程中工业规模制氢的操作可行性。这些发现有助于开发一个强大的共气化优化框架，支持可持续能源技术的进步和温室气体（GHG）排放的减少。
