Project Details
Description
The process industry is continuously looking for new ways to improve resource efficiency due to high dependence on resources (energy, raw materials and utilities). In large scale production even small changes in using raw materials and in energy can significantly improve process efficiency. The MORSE approach is to adopt new software tools for model-based predictive control, multi-criterial through process optimisation and quality management with overall process coordination. The application of these new software tools will lead to process improvements - reducing the use of raw material and energy while increasing the high quality and production rates.
The Morse project aims to further develop and to integrate a set of software tools that have partly already been validated in different process steps in steel industries. These software prototype tools and models were developed and evaluated by six R&D partners of the consortium in collaboration with three process industry partners. 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.
The mission of the Morse project is to develop model-based, predictive raw material and energy optimisation tools for the whole process route. This approach will be demonstrated in steel industry, to increase yield and product quality in production of high-strength carbon steels, stainless steels and cast steels.
| Acronym | MORSE |
|---|---|
| Status | Finished |
| Effective start/end date | 1/10/17 → 28/02/22 |
Collaborative partners
- VTT Technical Research Centre of Finland (lead)
- VDEh-Betriebsforschungsinstitut GmbH (Project partner)
- Pinja Operational Excellence Oy (Project partner)
- Optimización orientada a la sostenibilidad S.L. (IDENER) (Project partner)
- Cybernetica AS (Project partner)
- Outokumpu Stainless Oy (Project partner)
- GRIPS Industrial IT Solutions GmbH (Project partner)
- Maschinenfabrik Liezen u Gießerei GmbH (MFL) (Project partner)
- SSAB Europe Oy (Project partner)
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
-
SDG 9 Industry, Innovation, and Infrastructure
-
SDG 12 Responsible Consumption and Production
-
SDG 13 Climate Action
Funding category
- EU-H2020
Keywords
- H2020-EU.2.1.5.
- H2020-SPIRE-2017
- Production technology
- process engineering
- H2020-EU.2.1.5.3.
-
Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees
Takalo-Mattila, J., Heiskanen, M., Kyllönen, V., Maatta, L. & Bogdanoff, A., 2022, In: IEEE Access. 10, p. 68099-68110Research output: Contribution to journal › Article › Scientific › peer-review
Open Access32 Link opens in a new tab Citations (Scopus) -
Optimisation of Operator Support Systems through Artificial Intelligence for the Cast Steel Industry: A Case for Optimisation of the Oxygen Blowing Process Based on Machine Learning Algorithms
Ojeda Roldán, Á., Gassner, G., Schlautmann, M., Acevedo Galicia, L. E., Andreiana, D. S., Heiskanen, M., Leyva Guerrero, C., Dorado Navas, F. & Del Real Torres, A., 12 Mar 2022, In: Journal of Manufacturing and Materials Processing. 6, 2, 34.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile4 Link opens in a new tab Citations (Scopus)132 Downloads (Pure) -
Methods and Tools of Improving Steel Manufacturing Processes: Current State and Future Methods
Backman, J., Kyllönen, V. & Helaakoski, H., 2019, In: IFAC-PapersOnLine. 52, 13, p. 1174-1179Research output: Contribution to journal › Article in a proceedings journal › Scientific › peer-review
Open Access17 Link opens in a new tab Citations (Scopus)