Model predictive control of an electric arc furnace off-gas process

The following paper presents an approach to the mathematical modeling of heat and mass transfer processes in a 3-phase, 80 MVA AC, electric arc furnace (EAF) and represents a continuation of our work on modeling the electric and hydraulic EAF processes. This paper represents part 1 of the complete model and addresses issues on modeling the mass, temperature and energy processes in the EAF, while part 2 of the paper focuses solely on the issues related to the thermo-chemical relations and reactions in the EAF. As is generally known, the chemical, thermal and mass processes in an EAF are related to each other and cannot be studied completely separately; therefore, the work presented in part 1 and part 2 is related to each other accordingly and should be considered as a whole. The presented sub-models were obtained in accordance with different mathematical and thermo-dynamic laws, with the parameters fitted both experimentally, using the measured operational data of an EAF during different periods of the melting process, and theoretically, using the conclusions of different studies involved in EAF modeling. In conjunction with the already presented electrical and hydraulic models of the EAF, the heat-, mass-and energy-transfer models proposed in this work represent a complete EAF model, which can be further used for the initial aims of our study, i.e., optimization of the energy consumption and development of the operator-training simulator. The presented results show high levels of similarity with both the measured operational data and the theoretical data available in different EAF studies, from which we can conclude that the presented EAF model is developed in accordance with both fundamental laws of thermodynamics and the practical aspects regarding EAF operation.

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“…Similar expressions can also be found in Eqs. (13), (15) and (18), where their interpretation is similar as here.…”

Section: Solid Scrap Zone (Ssc)mentioning

confidence: 53%

Modeling and Validation of an Electric Arc Furnace: Part 1, Heat and Mass Transfer

2012

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“…Further, CO gas from furnace operations has been used successfully with significant economic and environmental benefits in a number of different industries around the world, including some applications in South Africa. In the steel industry particularly, CO gas from furnace operations (furnace CO gas) is used for the pre-heating of scrap, for the continuous charging of furnace feed material ( [8]), and for model predictive control in the automation of manually-controlled variables such as the forced-draught fan power and the airentrainment slip-gap width for the furnace off-gas process [9]. Another industry application that uses furnace CO gas is the co-generation of electricity.…”

Section: Opportunity For Improvement and Potential Benefitsmentioning

confidence: 99%

Multi-Objective Optimisation in Carbon Monoxide Gas Management at Tronox KZN Sands

Stadler

Bekker

2014

The South African Journal of Industrial Engineering

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Carbon monoxide (CO) is a by-product of the ilmenite smelting process from which titania slag and pig iron are produced. Prior to this project, the CO at Tronox KZN Sands in South Africa was burnt to get rid of it, producing carbon dioxide (CO 2 ). At this plant, unprocessed materials are pre-heated using methane gas from an external supplier. The price of methane gas has increased significantly; and so this research considers the possibility of recycling CO gas and using it as an energy source to reduce methane gas demand. It is not possible to eliminate the methane gas consumption completely due to the energy demand fluctuation, and sub-plants have been assigned either CO gas or methane gas over time. Switching the gas supply between CO and methane gas involves production downtime to purge supply lines. Minimising the loss of production time while maximising the use of CO arose as a multi-objective optimisation problem (MOP) with seven decision variables, and computer simulation was used to evaluate scenarios. We applied computer simulation and the multi-objective optimisation cross-entropy method (MOO CEM) to find good solutions while evaluating the minimum number of scenarios. The proposals in this paper, which are in the process of being implemented, could save the company operational expenditure while reducing the carbon footprint of the smelter. OPSOMMINGKoolstofmonoksied (CO) is 'n newe-produk van die ilmenietsmeltproses waardeur titaniumslak en ru-yster geproduseer word. Voor die uitvoering van hierdie projek is die CO gas wat deur Tronox KZN Sands, Suid-Afrika produseer is, verbrand om daarvan ontslae te raak. Die verbranding het weer koolstofdioksied produseer. By hierdie aanleg word ongeprosesseerde materiale voorverhit met metaangas wat van 'n eksterne verskaffer aangekoop word. Die prys van metaangas het beduidend toegeneem, en die moontlikheid om CO te herbruik in die plek van metaangas is in hierdie studie ondersoek. Weens die variasie in vraag na metaangas, kan die gebruik daarvan nie volledig uitgeskakel word nie; dus moet of CO of metaangas aan sub-aanlegte oor tyd toegeken word. Sodra daar 'n oorskakeling tussen die twee gassoorte plaasvind, moet die toevoerlyne eers gespoel word; en dit veroorsaak verlies van produksietyd. 'n Multidoelwit-optimeringprobleem het dus ontstaan waarin verlies aan produksietyd minimeer word terwyl CO gasgebruik maksimeer word. Hierdie probleem bevat sewe besluitveranderlikes, en scenario's is met rekenaarsimulasie ge-evalueer. Rekenaarsimulasie en die kruis-entropie metode vir multidoelwit-optimering is toegepas om goeie oplossings te verkry terwyl die minimum aantal scenario's evalueer is. Die voorstelle, wat tans in die proses is om implementeer te word, sal die onderneming operasionele koste bespaar en die koolstofspoor verklein.AA

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“…A literature review of the existent EAF models [8][9][10][11][12][13][14][15][16][17][18][19][20] indicates a lack of accurate arc models that could be applied for smart sensing, control or optimization of the energy flows. It is assumed that such model should present specific features that support their industrial application, including minimum input requirement, sufficient accuracy, simple mathematics and short calculation times.…”

Section: Introductionmentioning

confidence: 99%

“…The share of convective heat transfer when air is employed to form plasma in an EAF was predicted at 72.5%, which is more than the expected value. 20,21) The second group involves models, which describe all significant parts of the EAF process; [13][14][15][16][17][18][19][20] however, those models hardly consider arc energy dissipation, except Logar et al, 18,19) who assumed constant values for arc energy distribution (i.e. 20% convective heat, 75% radiation heat, 2.5% of the energy transferred to gas zone and 2.5% of the energy lost to electrodes) and Ghobara 20) who as well as Logar et al 18,19) considered constant coefficients for the heat transferred from the arc.…”

Section: Introductionmentioning

confidence: 99%

Low Computational-complexity Model of EAF Arc-heat Distribution

et al. 2015

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The paper presents a computational model and the corresponding algorithm for estimating the arc energy distribution to conductive, convective and radiative heat transfer in an electric arc furnace (EAF). The proposed algorithm uses channel arc model (CAM) in order to compute the distribution of the arc energy through empirical equations (to approximate arc radius), ideal gas law (to approximate arc density) and results of magneto-hydro-dynamic (MHD) models (to approximate arc pressure, temperature and velocity). Results obtained using the proposed algorithm are comparable with other similar studies; however, in contrast to other arc-energy distribution models, this model requires only two input variables (arc length and arc current) in order to calculate the energy distribution. Furthermore, simple algebraic equations used in the algorithm ensure minimal computational load and consequently lead to short calculation times which are approximately one hundred thousand (100 000) times smaller than solving the MHD model equations, making the algorithm suitable for real-time applications, such as smart monitoring and model-based control. The algorithm has been validated by two different approaches. First, the simulation results have been compared to a study dealing with arc-heat distribution in plasma arc furnace; and second, the proposed arc module has been integrated into the frame of a comprehensive EAF model in order to estimate the EAF temperature levels and compare them with operational EAF measurements. Both validations show high levels of similarity with the comparing data.KEY WORDS: arc current; arc heat distribution; arc length; channel arc model; EAF.approximately 75-85% of the total energy in low to medium power furnaces and approximately 50-60% of the total energy in ultra-high power furnaces (UHP).3) Implementing an accurate arc module in a comprehensive model-based EAF control, which ensures optimal control of arc length and slag height can lead to substantial energy saving and associated cost reduction. If a total energy reduction in a 200 ton UHP EAF with approximate annual production of 675 000 tones is 50 kWh/ton, with an assumption of 25 kWh/ton being the electrical energy, with the price of 10 $Cent/kWh, total annual energy savings will be equivalent to 1.7 million $ per year.It is known that the heat generated by the arcs is dissipated into the furnace by all three mechanisms of heat transfer (convection, conduction and radiation); however, the amount of the heat, transferred by each mechanism varies according to several factors, such as arc length, arc current, slag height, stage of melting etc. Development of a computational model of the arc, which allows estimation of the arc-heat distribution should therefore include all three types of heat transfer mechanisms, which can also be used to obtain the overall energy balance of the EAF. Application of such models may provide appropriate tools for optimizing the energy flow or use of model-based control systems in the EAFs.

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Model predictive control of an electric arc furnace off-gas process

Cited by 63 publications

References 5 publications

Modeling and Validation of an Electric Arc Furnace: Part 1, Heat and Mass Transfer

Modeling and Validation of an Electric Arc Furnace: Part 1, Heat and Mass Transfer

Multi-Objective Optimisation in Carbon Monoxide Gas Management at Tronox KZN Sands

Low Computational-complexity Model of EAF Arc-heat Distribution

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