Jawahery, S.; Visuri, V.-V.; Wasbø, S.O.; Hammervold, A.; Hyttinen, N.; Schlautmann, M. Thermophysical Model for Online Optimization and Control of the Electric Arc Furnace. Metals 2021, 11, 1587. https://doi.org/10.3390/met11101587
Thermophysical model for online optimization and control of the electric arc furnace
|Author:||Jawahery, Sudi1; Visuri, Ville-Valtteri2,3; Wasbø, Stein O.1;|
1Cybernetica AS, Leirfossvegen 27, 7038 Trondheim, Norway
2Research and Development, Outokumpu Stainless Oy, Terästie, 95490 Tornio, Finland
3Process Metallurgy Research Unit, University of Oulu, 90014 Oulu, Finland
4VDEh-Betriebsforschungsinstitut GmbH, Sohnstraße 69, 40237 Düsseldorf, Germany
|Online Access:||PDF Full Text (PDF, 4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021112456708
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2021-11-24
A dynamic, first-principles process model for a steelmaking electric arc furnace has been developed. The model is an integrated part of an application designed for optimization during operation of the furnace. Special care has been taken to ensure that the non-linear model is robust and accurate enough for real-time optimization. The model is formulated in terms of state variables and ordinary differential equations and is adapted to process data using recursive parameter estimation. Compared to other models available in the literature, a focus of this model is to integrate auxiliary process data in order to best predict energy efficiency and heat transfer limitations in the furnace. Model predictions are in reasonable agreement with steel temperature and weight measurements. Simulations indicate that industrial deployment of Model Predictive Control applications derived from this process model can result in electrical energy consumption savings of 1–2%.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
215 Chemical engineering
213 Electronic, automation and communications engineering, electronics
This research was funded by the European Commission, Directorate-General for Research and Innovation, through the project “Model-based optimisation for efficient use of resources and energy” (MORSE), grant number 768652. https://www.spire2030.eu/morse (accessed on 26 September 2021).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).