SIMP

System Integrated Metals Processing

In “System Integrated Metals Processing – SIMP” a global unique grouping of leading Finnish companies exporting sought after quality metal and technology products, have come together to address sustainability in quantified manner. The objective of the project is to further improve the already low environmental footprint of the 7.8 billion € export “Metals and metal products” Finnish industrial sector employing around 28,000 people[1] (2011 data) and to further increase its global competitiveness by integrating Digitalization and Sustainability in a system integrated manner. The focus is specifically also on digitalizing complex process models and making them operable in real-time in a gate-to-gate systemic plant environment. Therefore this project relies heavily on software developments by project partners and trusted sub-contractors used by the companies in this project.

As world class players involved in the project, we acknowledge that this is a rather ambitious programme. It is clearly a great challenge to link various existing process simulation models and data resolution/detail, sensors, measurement, data bases, control systems, advanced process control models, big data reduction and multivariate analysis tools, artificial intelligence, physics etc. to each other in order to predict product quality at least one challenging time step ahead. If required, new models will be developed, with the objective to further advance the science and technology of metal production and their control systems. It will therefore be a challenge with poorly measurable processes and sometimes non-existent measurement to calibrate dynamic simulation models to predict ahead in time – but this we know is the true creative part of this project.

In summary: the focus of the project will be to make the sustainable systems work in an industrial setting of world class production facilities in a system integrated and in real-time manner to predict a time-step ahead. This is an enormously challenging task to take (some still academic) models and transform them to work as robust control models in larger resource-to-metal-product process control systems. This challenge will be demonstrated in four Show Case projects with various cross-cutting supporting sub-projects:

  • Show Case 1 (SC1): Flexible copper plant operation with wide range of raw material quality. The challenge in this SC is to link mineral information (poorly measurable) via high temperature and aqueous solutions to final refined copper while capturing the depth of hydro- and pyrometallurgical reactors with a mix of easy and poorly measurable data.
  • Show Case 2 (SC2): Predictive plant wide production and quality control ((i) Dynamic multiphysics modelling as guidance in progressing steel making and (ii) Dynamic modelling and control of microstructure and properties from continuous casting to final product) touching on similar issues as SC1, but then applied to steel and stainless steel.
  • Show Case 3 (SC3): Decision support of metallurgical processes ((i) Plant-wide operation control system for hot metal production, (ii) Improved blast furnace control, (iii) Optimization of coking blend and coke oven leakage detection, and (iv) Intelligent Ferroalloy production process), extending SC1 and SC2 issues to ironmaking, while capturing the rather complex processes and phenomena in the blast furnace.
  • Show Case 4 (SC4): System modelling to optimize low-carbon footprint fuel usage in Finnish metallurgical industry ((i) Production and pre-treatment of alternative reducing agents, (ii) Development of a raw material selection applications, (iii) Modelling of alternative reductants in metallurgical process units, and (iv) Integration of energy, reductants and metal production), which will integrate the methods developed in SC1-SC3 to lower the footprint of the industry by the application of lower footprint reductants.

 

Schedule: 2014-2017

Size: total 43,8 M€

Contact person: Timo Fabritius

More information about SIMP in Dimecc's pages.

Last updated: 12.2.2018