This paper presents an approach for developing new model-based predictive tools for steel industry in order to make through process multi-criterial optimisation and quality management. The presented work is done in EU funded Morse project which includes both developing models and related software as well as demonstrations in real industrial environments. Morse software tools are aiming for process improvements - reducing the use of raw material and energy while increasing the high quality and production rates. Morse approach is to use a set of software tools as building blocks for developing more comprehensive tools to manage and optimise the whole production chain of complex steel industry processes. These software tools have already been validated in different process steps in blast furnace, liquid steel making and hot rolling mills. Morse aims to integrate different tools starting from unit process level up to plant-wide optimisation level and therefore provide better optimisation for steel production bottlenecks. Morse software tools will be implemented and demonstrated in three ifferent steel plants; carbon steels and stainless steel both working on a large-scale production level, and cast steel in a foundry working on a small-scale production level. The purpose of Morse optimisation system is that it should be applicable for many different types of plants and industries, having similar and common requirements. This paper describes the Morse concept, related models and software components in detail. It also describes the use cases setting the requirements for the development work. With the enhanced Morse tools companies of the process industry will be enabled to optimise the use of raw materials and energy by coordinated prediction and control of resource input and product quality along the entire process route from raw material and energy intake to customer delivery.
|Conference||METEC & 4th European Steel Technology and Application Days 2019|
|Abbreviated title||METEC and 4th ESTAD 2019|
|Period||24/06/19 → 28/06/19|
- Model-based predictive control
- Plant-wide optimisation
- Process coordination
- Energy and resource efficiency