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 cycle
solver. We analyze this problem in depth and show that it is not merely a
specialized concern but rather a more pervasive challenge that will likely
occur whenever ML is applied to larger plants. To address this problem, we
present a way to fine-tune ML models such that solving cycles with the usual
methods becomes robust again.

通过将机器学习模型直接拟合到植物传感器数据上，我们采用了一种结构化的方法，将化工厂的每个单元表示为一个机器学习模型，并将这些模型连接成一个类似于流程图的有向图。我们发现，对于较小的工厂，这种方法效果良好，但对于较大的工厂，流程图中大型和嵌套循环引起的复杂动力学导致循环求解器的不稳定性。我们对这个问题进行了深入分析，并表明这不仅是一个专业性问题，而是一个更普遍的挑战，可能在应用机器学习于较大工厂时经常出现。为了解决这个问题，我们提出了一种微调机器学习模型的方法，使得使用常规方法求解循环变得稳定。