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塑料热解过程的预测建模用于评估激活能量:基于可解释的人工智能的全面见解

Sanjeevani Hooda¹, Prasenjit Mondal¹

¹Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India.

Journal of environmental management

|May 17, 2024

概括

机器学习准确地预测塑料废物热解动力学,识别固定碳和灰含量等关键因素. 这有助于优化废物转化能源流程的可持续性.

科学领域:

化学工程是化学工程的重要组成部分.
材料科学材料科学材料科学
人工智能的人工智能

背景情况:

塑料废物是一个重大的全球性挑战,需要有效的管理策略.
热解提供了一个有前途的热化学途径,用于将塑料废物转化为有价值的能源.
了解热解动力学对于工艺设计和扩展至关重要,但传统方法缺乏原料影响的洞察力.

研究的目的:

应用机器学习来预测塑料废弃物热解动力学的建模.
调查原料特性对激活能量的影响.
为了确定影响热解激活能量的显著预测因素.

主要方法:

使用随机森林,XGBoost,CatBoost和AdaBoost回归模型进行动力预测.
使用RandomizedSearchCV.基于多对线性和超参数调的执行特征选择.
采用可解释的AI (XAI) 技术,包括特征重要性和SHAP总结图.

主要成果:

随机森林模型表现出优异的性能,其中R2 = 0.941,RMSE = 14.69和MAE = 8.66.
固定碳,碳含量,灰含量和转化程度被确定为关键预测因素.
固定碳含量成为激活能量的最有影响力的参数.

结论:

机器学习为理解复杂的热解动力学提供了强大的工具.
预测模型增强了对原料特性对激活能量影响的洞察力.
这种方法对于优化和扩大废塑料热解过程至关重要.

关键词:

超参数调整超参数调整动力学研究动力学研究机器学习机器学习塑料废弃物的热解处理.形状相加的解释.

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