Local machine learning model-based multi-objective optimization for managing system interdependencies in production: A case study from the ironmaking industry

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first -principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission -critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data -driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi -objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality -based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data -driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real -world industrial settings.