Speaker
Description
The aim of the Horizon 2020 SPIRE project MORSE (Model-based Optimisation for efficient use of ResourceS and Energy) is to develop model-based, predictive raw material and energy optimisation software tools for a whole process route. This approach is demonstrated in several pilot cases within the steel industry, to improve resource and energy efficiency, as well as product quality. This paper presents a new software application for material and energy optimisation during the AOD process within the stainless steel production route at Outokumpu in Tornio, Finland. A dynamic model of the AOD process developed by BFI has been adapted to the converter and process characteristics at Outokumpu steel plant and integrated into a process monitoring, optimisation, and control application developed by Cybernetica. This model predictive control (MPC) application has been installed and tested within the online automation environment at Outokumpu’s steel plant in Tornio. It continuously monitors the current heat state, predicts the end-point of the AOD treatment based on given target criteria, provides real-time predictions of expected heat trajectories using related recipe information, and dynamically adapts defined set-points of these recipes to achieve the target specification with minimum material, resources, and energy consumption. Preliminary results regarding achieved performances of the model application and related benefits for the steel plant are presented.