Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points
This work addresses simulation stability issues for chemical engineers, but it is incremental as it builds on existing ML-based modeling approaches.
The authors tackled the instability of machine learning-based chemical plant simulations in large plants by developing a fine-tuning method for ML models that ensures robust initialization and convergence to correct stationary states, even with simple solvers.
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.