基于机器学习的无动力混合灰盒模型来预测硫酸催化化反应
Xingyu Wen1, Wenshuai Bai1, Dayu Zhang1
1Tianjin Key Laboratory of Chemical Process Safety, Hebei Provincial Key Laboratory of Green Chemical Technology and High Efficient Energy Saving, School of Chemical Engineering and Technology, Hebei University of Technology, Tianjin 300130, China.
这项研究验证了化反应中的催化效应. 混合灰盒模型结合高斯过程回归和热平衡,为工业过程提供了卓越的预测准确性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 反应动力学反应动力学
背景情况:
- 2-butanol和propionic anhydride的化是一个关键的工业过程.
- 硫酸和酸表现出催化作用,使传统的运动模型复杂化.
研究的目的:
- 为了验证硫酸和酸的催化作用.
- 为反应预测开发一种可适应的无动力模型.
- 为全面的过程理解创建混合灰盒模型.
主要方法:
- 反应热量计被用来进行实验.
- 斯过程回归 (GPR) 被用作一种无动力黑盒模型.
- 通过将GPR模型与热平衡模型集成,开发了一种混合灰盒模型.
主要成果:
- 证实硫酸和酸都有明显的催化作用.
- 混合灰盒模型表现出强大的预测能力.
- 实现的最大平方根平均误差 (RMSE) 为转换的0.0069,温度的0.6535,热流速的10.6087.
结论:
- 混合灰盒模型为描述复杂的化反应提供了强大的和可适应的方法.
- 这种模型在预测准确度上比传统方法提供了显著的改进.
- 这些发现支持在工业化学过程中应用先进的建模技术.
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