Trustworthy AI for Process Automation on a Chylla-Haase Polymerization Reactor
This work addresses control challenges in chemical industry reactors like CSTRs, offering incremental improvements through automated, interpretable policies.
The paper tackled the challenging problem of controlling a Chylla-Haase polymerization reactor by using genetic programming reinforcement learning (GPRL) to generate human-interpretable control policies, resulting in high performance in reactor temperature control deviation as empirically evaluated on the original reactor template.
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.