Project Details
Description
The vision of COCOP is that complex process-industry plants are optimally run by operators with the guidance of a coordinating, real-time optimisation system. COCOP will strengthen the global position of the European process industry, which represents 20 per cent of the European manufacturing base with around 450,000 companies generating €1.6 billion in turnover and 6.8 million jobs.
The project’s objective is to enable plant-wide monitoring and control by using the model-based, predictive, coordinating optimisation concept in integration with plant’s automation systems. This ambitious approach will be developed and verified in co-operation of European universities, research institutes and industry. The Consortium comprises two universities, three research organisations, the leading copper-plant technology provider, two large companies from the process industry (steel and special chemicals) and four SMEs providing automation solutions.
Technical objective is to define, design and implement a concept that integrates existing industrial control systems with efficient data management and optimisation methods and provides means to monitor and control large industrial production processes. The plant-wide monitoring and control comprehend computationally intensive data analysis and large scale optimisation. The social objective is to improve operator plant-wide awareness and reduce mental workload.
COCOP will liaise with standardisation bodies (automation) to ensure a sustained impact of the project’s results. Commercialisation of the solution by its process-automation industry partners will allow plant operators to approach optimal production and result in reduced energy and resource consumption, and decreased on-site material handling time and greenhouse gas emissions.
The project’s objective is to enable plant-wide monitoring and control by using the model-based, predictive, coordinating optimisation concept in integration with plant’s automation systems. This ambitious approach will be developed and verified in co-operation of European universities, research institutes and industry. The Consortium comprises two universities, three research organisations, the leading copper-plant technology provider, two large companies from the process industry (steel and special chemicals) and four SMEs providing automation solutions.
Technical objective is to define, design and implement a concept that integrates existing industrial control systems with efficient data management and optimisation methods and provides means to monitor and control large industrial production processes. The plant-wide monitoring and control comprehend computationally intensive data analysis and large scale optimisation. The social objective is to improve operator plant-wide awareness and reduce mental workload.
COCOP will liaise with standardisation bodies (automation) to ensure a sustained impact of the project’s results. Commercialisation of the solution by its process-automation industry partners will allow plant operators to approach optimal production and result in reduced energy and resource consumption, and decreased on-site material handling time and greenhouse gas emissions.
| Acronym | COCOP |
|---|---|
| Status | Finished |
| Effective start/end date | 1/10/16 → 31/03/20 |
Collaborative partners
- VTT Technical Research Centre of Finland
- Tampere University (lead)
- Outokumpu Oyj
- Tecnalia Research & Innovation (TRI)
- TU Dortmund University
- VDEh-Betriebsforschungsinstitut GmbH
- 2-control ApS
- Royal DSM N.V.
- Sidenor
- Optimización orientada a la sostenibilidad S.L. (IDENER)
- MSi Grupo
- Optimation AB
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):
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SDG 9 Industry, Innovation, and Infrastructure
Funding category
- EU-H2020
Keywords
- H2020-EU.2.1.5.3.
Research output
- 2 Conference article in proceedings
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Human factors in software projects for complex industrial processes
Liinasuo, M., Lastusilta, T., Savolainen, J. & Kuula, T., Feb 2019, Intelligent Human Systems Integration 2019: IHSI 2019. Karwowski, W. & Ahram, T. (eds.). Cham: Springer, p. 517-523 (Advances in Intelligent Systems and Computing, Vol. 903).Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
Open AccessFile158 Downloads (Pure) -
Business view on the development of industrial plant-wide optimization tool
Hemilä, J. & Jansson, J., 2 Jul 2018, 2018 International Conference on Production and Operations Management Society: POMS 2018. IEEE Institute of Electrical and Electronic Engineers, 8629458Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus)174 Downloads (Pure)