Ascher Simon, Wang Xiaonan, Watson Ian, Sloan William, You Siming
School of Engineering, University of Glasgow, University Avenue, Glasgow G12 8QQ, United Kingdom.
Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.
Bioresour Technol. 2022 Nov;364:128062. doi: 10.1016/j.biortech.2022.128062. Epub 2022 Oct 3.
Machine learning has been regarded as a promising method to better model thermochemical processes such as gasification. However, their black box nature can limit how much one can trust and learn from the developed models. Here seven different machine learning methods have been adopted to model the gasification of biomass and waste across a wide range of operating conditions. Gradient boosting regression has been found to outperform the other model types with a coefficient of determination (R) of 0.90 when averaged across ten key gasification outputs. Global and local model interpretability methods have been used to illuminate the developed black box models. The studied models were most strongly influenced by the feedstock's particle size and the type of gasifying agent employed. By combining global and local interpretability methods, the understanding of black box models has been improved. This allows policy makers and investors to make more educated decisions about gasification process design.
机器学习被视为一种有前景的方法，可用于更好地模拟热化学过程，如气化过程。然而，它们的黑箱性质可能会限制人们对所开发模型的信任程度以及从中学习的程度。本文采用了七种不同的机器学习方法，对生物质和废弃物在广泛运行条件下的气化过程进行建模。结果发现，梯度提升回归在十种关键气化输出指标上的平均决定系数（R）为0.90，优于其他模型类型。全局和局部模型可解释性方法已被用于阐明所开发的黑箱模型。研究发现，所研究的模型受原料粒度和所用气化剂类型的影响最大。通过结合全局和局部可解释性方法，对黑箱模型的理解得到了改善。这使得政策制定者和投资者能够在气化工艺设计方面做出更明智的决策。
