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

被引：9

作者：

Jawahery, Sudi ^{[1
]}

Visuri, Ville-Valtteri ^{[2
,3
]}

Wasbo, Stein O. ^{[1
]}

Hammervold, Andreas ^{[1
]}

Hyttinen, Niko ^{[2
]}

Schlautmann, Martin ^{[4
]}

机构：

[1] Cybernetica AS, Leirfossvegen 27, N-7038 Trondheim, Norway

[2] Outokumpu Stainless Oy, Res & Dev, Terastie 95490, Tornio, Finland

[3] Univ Oulu, Proc Met Res Unit, Oulu 90014, Finland

[4] VDEh Betriebsforschungsinst GmbH, Sohnstr 69, D-40237 Dusseldorf, Germany

来源：

METALS | 2021年 / 11卷 / 10期

基金：

欧盟地平线“2020”;

关键词：

electric arc furnace; mathematical modeling; model predictive control; VALIDATION;

D O I：

10.3390/met11101587

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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%.</p>

引用

页数：26

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