- author = "Daniel Hein and Daniel Labisch",
- title = "Trustworthy {AI} for Process Automation on a {Chylla-Haase} Polymerization Reactor",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "1570--1578",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, GPRL, Theory of computation, Reinforcement learning, Computing methodologies, Applied computing, Industry and manufacturing, Interpretable reinforcement learning, process automation, real-world application",
- isbn13 = "978-1-4503-8351-6",
- timestamp = "Fri, 03 Sep 2021 10:51:17 +0200",
-
biburl = "
https://dblp.org/rec/journals/corr/abs-2108-13381.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
-
URL = "https://arxiv.org/abs/2108.13381",
-
DOI = "
doi:10.1145/3449726.3463131",
- size = "9 pages",
- abstract = "genetic programming reinforcement learning (GPRL) is used 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 uses 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.",
-
notes = "Siemens AG, Technology, Munich, Germany.
Also known as \cite{DBLP:journals/corr/abs-2108-13381}. GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
