A machine learning digital twin approach for critical process parameter prediction in a catalyst manufacturing line

Abstract

Digital twins (DTs) are rapidly changing how manufacturing companies leverage the large volumes of data they generate daily to gain a competitive advantage and optimize their supply chains. When coupled with recent developments in machine learning (ML), DTs have the potential to generate invaluable insights for process manufacturing companies to help them optimize their manufacturing processes. However, this potential has yet to be fully exploited due to the challenges that process manufacturing companies face in developing and implementing DTs in their organizations. Although DTs are receiving increasing attention in both industry and academia, there is limited literature on how to apply them in the process industry. To address this gap, this paper presents a framework for developing ML-based DTs to predict critical process parameters in real time. The proposed framework is tested through a case study at an international process manufacturing company in which it was used to collect and process plant data, build accurate predictive models for two critical process parameters, and develop a DT application to visualize the models’ predictions. The case study demonstrated the usefulness of the proposed DT–ML framework in the sense that it provided the company with more accurate predictions than the models it previously applied. The study provides insights into the value of applying ML-based DT in the process industry and sheds light on some of the challenges associated with the application of this technology.