论文标题
一种在铝提取过程中建模未知物理的新型矫正术语方法
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
论文作者
论文摘要
随着数据的越来越多的可用性，将现代机器学习方法应用于建模和控制等领域引起了人们的兴趣。但是，尽管这种黑盒模型具有灵活性和令人惊讶的准确性，但仍然很难信任它们。结合两种方法的最新努力旨在开发灵活的模型，尽管如此，这些模型仍然可以很好地推广。我们称为混合分析和建模（HAM）的范式。在这项工作中，我们调查了纠正源术语方法（COSTA），该方法使用数据驱动的模型来纠正基于错误的物理模型。这使我们能够开发出可以进行准确预测的模型，即使问题的潜在物理学尚不清楚。我们将Costa应用于铝电解电池中的Hall-Héroult工艺。我们证明该方法提高了准确性和预测稳定性，从而产生了一个更具值得值得信赖的模型。
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However, despite the flexibility and surprising accuracy of such black-box models, it remains difficult to trust them. Recent efforts to combine the two approaches aim to develop flexible models that nonetheless generalize well; a paradigm we call Hybrid Analysis and modeling (HAM). In this work we investigate the Corrective Source Term Approach (CoSTA), which uses a data-driven model to correct a misspecified physics-based model. This enables us to develop models that make accurate predictions even when the underlying physics of the problem is not well understood. We apply CoSTA to model the Hall-Héroult process in an aluminum electrolysis cell. We demonstrate that the method improves both accuracy and predictive stability, yielding an overall more trustworthy model.
