Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured approach: Each unit within the plant gets represented by one ML model. After fitting the models to the data, the models are connected into a flowsheet-like directed graph. We find that for smaller plants, this approach works well, but for larger plants, the complex dynamics arising from large and nested cycles in the flowsheet lead to instabilities in the solver during model initialization. We show that a high accuracy of the single-unit models is not enough: The gradient can point in unexpected directions, which prevents the solver from converging to the correct stationary state. To address this problem, we present a way to fine-tune ML models such that initialization, even with very simple solvers, becomes robust.

翻译：理想化的化工厂第一性原理模型可能存在不精确性。另一种替代方案是直接将机器学习模型拟合到工厂传感器数据。我们采用结构化方法：工厂内每个单元由独立机器学习模型表示。在将模型与数据拟合后，这些模型被连接成类似流程图的带方向图结构。研究发现，对于小型工厂，该方法效果良好；但面对大型工厂时，流程图中的大规模嵌套循环产生的复杂动力学特性会导致模型初始化求解器出现不稳定现象。我们证实仅提高单单元模型精度并不足够：梯度可能指向意外方向，阻碍求解器收敛至正确稳态。针对该问题，我们提出一种机器学习模型微调方法，即使使用最简求解器，也能实现稳健的初始化过程。