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.

化工厂的理想化第一性原理模型可能不准确。另一种方法是将机器学习 (ML) 模型直接拟合到植物传感器数据。我们使用一种结构化方法：工厂内的每个单元都由一个机器学习模型表示。将模型与数据拟合后，模型将连接成类似流程图的有向图。我们发现，对于较小的工厂，这种方法效果很好，但对于较大的工厂，流程图中的大型嵌套循环产生的复杂动态会导致模型初始化期间求解器不稳定。我们证明单单元模型的高精度是不够的：梯度可能指向意想不到的方向，这会阻止求解器收敛到正确的稳态。为了解决这个问题，我们提出了一种微调 ML 模型的方法，这样即使使用非常简单的求解器，初始化也变得稳健。