Integration of Systems Engineering-based Paradigms for the Scheduling and Control of an Experimental Hot-rolling Mill

Abstract

This paper presents research work associated with the integration of systems engineering-based paradigms for implementing state-of-the-art mechanisms of scheduling and control on an experimental laboratory-scale hot-rolling mill located at Sheffield University (UK). A comprehensive hybrid model for metal processing was combined with a Genetic Algorithm (GA)-based optimisation method to calculate the optimal rolling schedule, hence realising the concept of right-first-time production of steel alloys. Furthermore, the mill used Model-based Predictive Control (MPC) to guarantee optimal control performance during its real-time operations. Results from hot-rolling experiments are presented to provide a proof-of-concept about the use of integrated model-based systems to solve complex metallurgical problems.