The Machine Learning Life Cycle in Chemical Operations – Status and Open Challenges
Marco Gärtler, Valentin Khaydarov, Benjamin Klöpper, Leon Urbas
Abstract
Abstract Artificial intelligence (AI) has received a lot of attention with many publications in recent years. Interestingly related projects in the industry are mostly still in their early stages. We are convinced that progress will only be possible if the entire machine learning (ML) life cycle is considered. Our study focuses on the practical challenges, uses a recent study as foundation and adopts the life‐cycle description, highlights the life‐cycle practices in other domains and formulates research directions that can help to improve the utilization of AI and machine learning in the process industry.
Topics & Concepts
Process (computing)Learning cycleArtificial intelligenceFoundation (evidence)Computer scienceEngineeringOperations researchManagementPolitical scienceEconomicsLawOperating systemFault Detection and Control SystemsData Stream Mining TechniquesAdvanced Statistical Process Monitoring