Estimation of bath temperature in electric arc furnace using fuzzy modelling approach

Elektroobločne peči so namenjene reciklaži jeklenega odpadka. Pri procesu reciklaže predstavlja končna temperatura taline eno od pomembnejših veličin. Narava procesa taljenja onemogoča sprotno merjenje temperature taline, zato se meritve izvaja le pred odlivanjem taline, s čimer se preveri ali se temperatura taline nahaja v predpisanemu intervalu. Za opravljanje meritev se uporabljajo merilne sonde za enkratno uporabo. Med potekom merjenja temperature je potrebno peč ugasniti, kar prispeva k daljšemu trajanju reciklaže, nepotrebnih izgubam energije ter posledično nižji učinkovitosti. Delo predstavlja razvoj modela temperature taline v elektroobločni peči z uporabo mehkega pristopa. Model vključuje vse vplivne dejavnike, ki se merijo na elektroobločni peči. Predlagani model temperature taline je namenjen implementaciji na elektroobločni peči, kjer bo deloval sočasno s procesom reciklaže ter nudil podporo operaterjem elektroobločne peči o vrednosti temperature jekla. Tako bo zmanjšano potrebno število meritev temperature taline, kar skrajša čas reciklaže jekla in posledično poveča produktivnost obrata. Mehki model temperature se je izkazal kot zanesljiv in primeren za uporabo na elektroobločni peči pod dvema pogojema, in sicer da je prva meritev temperature pravilna in da je do prve meritve temperature taline vložek jekla v celoti staljen. Predpostavljeni model temperature ni vezan na specifične lastnosti elektroobločne peči, ampak zgolj na meritve, ki se zajemajo na peči. To mu omogoča hitro in enostavno prenosljivost tudi na druge dizajne peči. Pristop k mehkemu modeliranju, nakazan v delu, ni omejen zgolj na ocenjevanje temperature taline, saj ga je mogoče enostavno razširiti tudi na druga področja.Electric arc furnaces are intended for the recycling of steel scrap. In the recycling process, the final melt temperature is one of the most important quantities. Due to the nature of the melting process, continuous measurement of the melt temperature is impossible and is performed only before the melt is tapped, in order to check whether the melt temperature is within the prescribed interval. Disposable measuring probes are used to perform the measurements. During the temperature measurement, the furnace must be switched off, which contributes to a longer recycling time, unnecessary energy losses and consequently lower efficiency. The thesis presents the development of a melt temperature model in an electric arc furnace using a fuzzy approach. The model includes all influencing factors measured on an electric arc furnace. The proposed melt temperature model is intended for implementation on an electric arc furnace, where it will operate in parallel with the recycling process and offer support to electric arc furnace operators on steel temperature. This will reduce the required number of melt temperature measurements, which shortens the recycling time of steel and consequently increases plant productivity. The fuzzy temperature model has proven to be reliable and suitable for use on an electric arc furnace provided that the first temperature measurement is correct and that the steel scrap is completely melted by the first melt temperature measurement. The fuzzy temperature model is not tied to the specific properties of the electric arc furnace, but only to the measurements performed on the furnace. This allows it to be quickly and easily transferred to other furnace designs. The approaches to fuzzy modelling indicated in the thesis are not limited to estimating melt temperature, as it can be easily extended to other areas

Soft sensor of bath temperature in an electric arc furnace based on a data-driven Takagi–Sugeno fuzzy model

Electric arc furnaces (EAFs) are intended for the recycling of steel scrap. One of the more important variables in the recycling process is the tapping temperature of the steel. Due to the nature of the process, continuous measurement of the melt temperature is complicated and requires sophisticated measuring equipmenttherefore, for most EAFs, separate temperature samples are taken several times before the melt is tapped, to verify whether the melt temperature is within the prescribed range. The measurements are obtained using disposable probeswhen measurement is performed, the furnace must be switched off, leading to increased tap-to-tap time, unnecessary energy losses, and consequently, lower efficiency. The following paper presents a novel approach to EAF bath temperature estimation using a fuzzy model soft sensor obtained using Gustafson–Kessel input data clustering and particle swarm optimization of model parameters. The model uses the first temperature measurement as an initial condition, and measurements of the necessary EAF inputs to estimate continuously the bath temperature throughout the refining stage of the recycling process. The results have shown that the prediction accuracy of the proposed model is very high and that it fulfils the required tolerance band. The model is intended for parallel implementation in the EAF process, with the aim of achieving fewer temperature measurements, shorter tap-to-tap times, and decreased energy losses. Furthermore, if information about bath temperature is accessible in a continuous manner, operators can adjust the control of the EAF to achieve optimal tapping temperature and thus higher EAF efficiency

Active filter reference calculations based on customers\u27 current harmonic emissions

This paper deals with harmonics compensation in industrial and distribution networks using an active filter (AcF). When defining the AcF’s reference current, it is important to properly consider the network background harmonic distortion. Within this paper, we propose an AcF reference current calculation method, based on customers’ current harmonic emissions. The main novelty of the paper is the AcF reference current calculation method that considers only the customer’s contributions to the harmonic distortion at the point of common coupling (PCC). By separating the harmonic current at the PCC into components that can be attributed to the customer and to the network, it is possible to limit the required AcF power. To determine the customer’s emission, the customer’s harmonic impedance must be known. As the actual harmonic impedance cannot be determined in a real environment, a reference harmonic impedance can be used instead. To test the proposed AcF reference current calculation method, we developed a control algorithm of an AcF in the PSCAD software and tested this on a medium-voltage benchmark simulation model