Model-based optimisation for efficient use of resources and energy

Livrables

Evaluation and reporting of achieved process improvements

Evaluation and reporting of achieved process improvementsThis report will represent the evaluation results of the developed system in different demonstrators This includes evaluation of system performance achieved improvements and benefits

Guidelines for model set-up, validation and auto-calibration

Guidelines for model setup validation and autocalibrationCrosssectorial guidelines for model setup validation and autocalibration

Communication plan

Communication plan Plan for implementation of different communication actions of the project

Exploitation plan

Exploitation planFinal version of the exploitation plan Document will identify the exploitable results and how these should be exploited

Initial exploitation plan

Initial exploitation plan Document for the exploitation opportunities and plans for project result exploitation. Document will be updated throughout the project to keep track of potential opportunities as they are identified.

Dissemination plan

Dissemination plan Plan for dissemination activities to make the project outcomes visible and accessible to the different target stakeholders

Validation of process models

Validation of process modelsValidation and tuning of the enhanced models

List of generic KPIs to assess the system functionality and benchmarks for system validation on use cases

List of generic KPIs to assess the system functionality and benchmarks for system validation on use cases Deliverable D1.3 will list the generic KPIs to assess the system functionality and the criteria by which the project’s impact will be evaluated in WP5.

Layout of process models

Specification of application specific models (energy and mass balance, thermodynamics, heat transfer, product quality etc.) to be integrated into through-process monitoring, optimisation and control system

System requirements specification (SRS)

System requirements specification (SRS) Deliverable D1.2 will list the requirements derived from the use case for the developed system.

Setting up project content management system

Setting up project content management system Content management system will be used as a tool for coordination, document delivery and storage and internal communication.

Project website

Project website Creation of a project website for dissemination and communication purpose.

Publications

Methods and Tools of Improving Steel Manufacturing Processes: Current State and Future Methods

Auteurs: Jere Backman, Vesa Kyllönen, Heli Helaakoski
Publié dans: IFAC-PapersOnLine, Numéro 52/13, 2019, Page(s) 1174-1179, ISSN 2405-8963
Éditeur: Elsevier
DOI: 10.1016/j.ifacol.2019.11.355

Steelmaking Process Optimised through a Decision Support System Aided by Self-Learning Machine Learning

Auteurs: Andreiana, Doru S., Luis E. Acevedo Galicia, Seppo Ollila, Carlos Leyva Guerrero, Álvaro Ojeda Roldán, Fernando Dorado Navas, and Alejandro del Real Torres
Publié dans: Processes, Numéro 10, 2022, ISSN 2227-9717
Éditeur: MDPI
DOI: 10.3390/pr10030434

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

Auteurs: Ojeda Roldán, Álvaro, Gert Gassner, Martin Schlautmann, Luis E. Acevedo Galicia, Doru S. Andreiana, Mikko Heiskanen, Carlos Leyva Guerrero, Fernando Dorado Navas, and Alejandro del Real Torres
Publié dans: Journal of Manufacturing and Materials Processing, Numéro 6, 2022, ISSN 2504-4494
Éditeur: MDPI
DOI: 10.3390/jmmp6020034

Adaptive First Principles Model for the CAS-OB Process for Real-Time Applications

Auteurs: Kasper Linnestad; Seppo Ollila; Stein O. Wasbø; Agne Bogdanoff; Torstein Rotevatn
Publié dans: Metals, Numéro 11, 2021, ISSN 2075-4701
Éditeur: MDPI
DOI: 10.3390/met11101554

Thermophysical Model for Online Optimization and Control of the Electric Arc Furnace

Auteurs: Sudi Jawahery; Ville-Valtteri Visuri; Stein O. Wasbø; Andreas Hammervold; Niko Hyttinen; Martin Schlautmann
Publié dans: Metals, Numéro 11, 2021, ISSN 2075-4701
Éditeur: MDPI
DOI: 10.3390/met11101587

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

Auteurs: Janne Takalo-Mattila, Mikko Heiskanen, Vesa Kyllönen, Leena Määttä, Agne Bogdanoff
Publié dans: IEEE Access, Numéro 10, 2022, Page(s) 68099 - 68110, ISSN 2169-3536
Éditeur: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/access.2022.3185607

Model-Based Optimisation for Efficient Use of Resources and Energy

Auteurs: Heli Helaakoski, Seppo Ollila, Stein O. Wasbø, Torstein Rotevatn, Santiago Moreira, Martin Schlautmann, Jere Backman
Publié dans: Proceedings of the METEC & 4th ESTAD 2019, European Steel Technology and Application Days, 2019
Éditeur: -

Model predictive control of the AOD process for material and energy optimisation

Auteurs: Ville-Valtteri Visuri, Pentti Kupari, Andreas Hammervold, Stein O. Wasbø, Vera Peiss
Publié dans: Proceedings of the 9th International Conference on Modeling and Simulation of Metallurgical Processes in Steelmaking, 2021, Page(s) 128–135, ISBN 978-3-200-07994-6
Éditeur: The Austrian Society for Metallurgy and Materials

Development and application of model-based software tools for raw material and energy optimisation at the Cast Steel production route – Results from MORSE project

Auteurs: Gassner, G., Fuchs, P., Schlautmann, M., Stubbe G., Jendryssek, U., Niehues, P., Leyva, C., Ojeda, A
Publié dans: Proceedings of the 5th ESTAD 2021, European Steel Technology and Application Days, 2021
Éditeur: -

Preliminary experiences from the application of model predictive control for the EAF process in stainless steelmaking

Auteurs: Ville-Valtteri Visuri, Sudi Jawahery, Niko Hyttinen, Stein O. Wasbø, Martin Schlautmann
Publié dans: Proceedings of the 4th European Academic Symposium on EAF Steelmaking, 2021, Page(s) 42-43
Éditeur: Department for Industrial Furnaces and Heat Engineering, RWTH Aachen University

Livrables

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