# Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points

arXiv: https://arxiv.org/abs/2307.13621 · HTML: https://mlsanity.com/paper/2307.13621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Deep Equilibrium Models](https://mlsanity.com/paper/1909.01377/) (2019)
- [Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields](https://mlsanity.com/paper/2208.12104/) (2022)
- [Forecasting Industrial Aging Processes with Machine Learning Methods](https://mlsanity.com/paper/2002.01768/) (2020)
- [Optimization with Trained Machine Learning Models Embedded](https://mlsanity.com/paper/2207.12722/) (2022)
- [Optimized data exploration applied to the simulation of a chemical process](https://mlsanity.com/paper/1902.06453/) (2019)

## Abstract (arXiv)

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.