可解释性机器学习驱动的煤和污泥超临界水共气化产物预测研究

肖峰; 唐严; 马苗苗; 安广晨; 张淑媛; 苏雷; 陈渝楠; 陈斌

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可解释性机器学习驱动的煤和污泥超临界水共气化产物预测研究

更新时间：2026-07-20

- 可解释性机器学习驱动的煤和污泥超临界水共气化产物预测研究
- Prediction of Products from Supercritical Water Co-Gasification of Coal and Sludge Driven by Interpretable Machine Learning
- 西安交通大学学报 2026年
- 作者机构：
  
  1.西安交通大学绿色氢电全国重点实验室，西安，710049
  2.国家电投集团四川电力有限公司，成都，610213
  3.航空工业陕西航空电气有限责任公司，西安，710065
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TK91
- 收稿：2026-03-06，
  
  修回：2026-06-26，
  
  录用：2026-07-20，
- 稿件说明：
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肖峰, 唐严, 马苗苗, 等. 可解释性机器学习驱动的煤和污泥超临界水共气化产物预测研究[J/OL]. 西安交通大学学报, 2026.

Xiao Feng, Tang Yan, Ma Miaomiao, et al. Prediction of Products from Supercritical Water Co-Gasification of Coal and Sludge Driven by Interpretable Machine Learning[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
肖峰, 唐严, 马苗苗, 等. 可解释性机器学习驱动的煤和污泥超临界水共气化产物预测研究[J/OL]. 西安交通大学学报, 2026. DOI：

Xiao Feng, Tang Yan, Ma Miaomiao, et al. Prediction of Products from Supercritical Water Co-Gasification of Coal and Sludge Driven by Interpretable Machine Learning[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. DOI：

摘要

针对传统方法难以刻画小样本煤与污泥超临界水共气化实验数据中复杂非线性关系与多因素交互作用的问题，本文构建了基于机器学习的产物预测与可解释性分析框架。采用留一法交叉验证评估随机森林（RF）、极端梯度提升（XGBoost）、梯度提升回归（GBR）和支持向量回归（SVR）对H

、CH

、CO和CO

产率的预测性能，并联合网格搜索和Optuna方法对模型超参数进行调优。结果表明，XGBoost模型整体预测表现最优，经Optuna优化后进一步提升（平均

²=0.828，

MAE

=0.614，

RMSE

=0.839）。SHAP特征重要性分析表明温

度对气体产率总体贡献最大（约45%）；偏依赖图进一步揭示了实验参数对气化产物分布的非线性影响规律及其交互作用特征。本研究构建的机器学习框架适用于小样本超临界水气化实验数据，可为煤与污泥共气化过程参数优化及产物调控提供数据驱动方法参考。

Abstract

To address the limitation of traditional methods in capturing complex nonlinear relationships and multi-factor interactions in small-sample experimental data from supercritical water co-gasification of coal and sludge

this study developed a machine learning-based framework for gasification product prediction and model interpretation. Leave-one-out cross-validation was employed to evaluate random forest (RF)

extreme gradient boosting (XGBoost)

gradient boosting regression (GBR)

and support vector regression (SVR) models for predicting H₂

CH₄

and CO₂ yields

while grid search and Optuna were used for hyperparameter optimization. The results showed that XGBoost exhibited the best overall predictive performance

with an average R² of 0.828

MAE of 0.614

and RMSE of 0.839 after Optuna optimization. SHAP analysis identified reaction temperature as the most influential factor

contributing approximately 45% to the model output. Partial dependence plots further revealed the nonlinear effects of key experimental parameters on gasification product distribution and their interaction characteristics. The proposed framework is suitable for small-sample SCWG datasets and provides a data-driven approach for parameter optimization and product distribution regulation in coal and sludge co-gasification.

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