在微波大气压等离子体喷射中基于不确定性意识的机器学习预测等离子体参数
Suryasunil Rath1, Priyabrata Das2, Pulak Mohan Pandey2
1Department of Energy Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India. satyananda@dese.iitd.ac.in.
Physical chemistry chemical physics : PCCP
|February 10, 2026
概括
本研究引入了一种可解释的机器学习框架,用于预测微波大气压等离子体喷射 (MW-APPJ) 的关键参数. 梯度增强模型准确地预测了等离子体属性,并量化了不确定性,有助于过程优化.
科学领域:
- 等离子体物理和化学
- 机器学习应用程序 机器学习应用程序
- 化学工程是化学工程的组成部分.
背景情况:
- 微波大气压等离子喷射器 (MW-APPJs) 由于可调节的反应物种生成,提供了多样化的应用.
- 精确预测等离子体参数对于优化MW-APPJ性能至关重要.
- 现有的方法缺乏全面的不确定性量化和解释性.
研究的目的:
- 开发一个不确定性意识,多输出,可解释的监督机器学习 (ML) 框架.
- 预测MW-APPJ的八个关键等离子体参数.
- 为数据驱动的等离子体诊断和过程优化提供可靠的基础.
主要方法:
- 通过变化的输入功率,滑动短位置和气流速生成了441次实验运行的数据集.
- 使用贝叶斯超参数调优化了六个回归模型 (KNN,ET,RF,ANN,GB,XGB).
- 采用了SHapley添加式解释 (SHAP) 来实现模型的解释性,以及用于不确定性定量化的引导.
主要成果:
- 梯度增强 (GB) 模型展示了精度,校准和不确定性可靠性的最佳平衡.
- 在GB模型中,在持久测试组件上,平均绝对百分比误差<3%,R2>0.97.
- SHAP分析确定微波功率和气体流速是影响等离子体参数的主导特征.
结论:
- 开发的ML框架可靠地预测MW-APPJ等离子体参数,并量化不确定性.
- 实验验证证证实了强大的概括能力,在推断方案中增加了适当的不确定性.
- 可解释的框架有助于理解运行条件的影响,使未来的MW-APPJ流程优化成为可能.
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