# Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor

arXiv: https://arxiv.org/abs/2108.13381 · HTML: https://mlsanity.com/paper/2108.13381/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 5 of 5)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Interpretable Policies for Reinforcement Learning by Genetic Programming](https://mlsanity.com/paper/1712.04170/) (2017)
- [Evolving simple programs for playing Atari games](https://mlsanity.com/paper/1806.05695/) (2018)
- [Interpretable Control by Reinforcement Learning](https://mlsanity.com/paper/2007.09964/) (2020)

## Built on by (most cited first, 1 of 1)

- [Discovering Continuous-Time Memory-Based Symbolic Policies using Genetic Programming](https://mlsanity.com/paper/2406.02765/) (2024)

## Abstract (arXiv)

In this paper, genetic programming reinforcement learning (GPRL) is utilized to generate human-interpretable control policies for a Chylla-Haase polymerization reactor. Such continuously stirred tank reactors (CSTRs) with jacket cooling are widely used in the chemical industry, in the production of fine chemicals, pigments, polymers, and medical products. Despite appearing rather simple, controlling CSTRs in real-world applications is quite a challenging problem to tackle. GPRL utilizes already existing data from the reactor and generates fully automatically a set of optimized simplistic control strategies, so-called policies, the domain expert can choose from. Note that these policies are white-box models of low complexity, which makes them easy to validate and implement in the target control system, e.g., SIMATIC PCS 7. However, despite its low complexity the automatically-generated policy yields a high performance in terms of reactor temperature control deviation, which we empirically evaluate on the original reactor template.