MHD instability at the cathode spot as the origin of the vortex formation in high-intensity plasma arcs

Magnetohydrodynamic instability in a high-intensity arc, similar to typical arcs in DC electric arc furnaces, is simulated using an induction based model under 2D axisymmetric conditions. Time-averaged results show a good agreement with steady-state calculated results expected for a stable arc. The transient results declare that z-pinch close to the cathode, occurring due to the high electrical current density, is responsible for arc instability in this region. The unstable behavior of the arc can be evaluated in a periodic procedure. Moreover, correlations between the fluctuations in total voltage drop curve and the arc shape are investigated: when the arc is in form of column (or bell) the total voltage drop is on a minimum peak; if there is an irregular expansion of the arc in form of arms, the total voltage drop shows a maximum peak.

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

<div> <div> <div> <p>In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low- pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low- pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics. </p> </div> </div> </div>

Deep Learning Based Predictive Compensation of Flicker, Voltage Dips, Harmonics and Interharmonics in Electric Arc Furnaces

<div> <div> <div> <p>In this research work, deep machine learning based methods together with a novel data augmentation are developed for predicting flicker, voltage dip, harmonics and interharmonics originating from highly time-varying electric arc furnace (EAF) currents and voltage. The aim with the prediction is to counteract both the response and reaction time delays of active power filters (APFs) specifically designed for electric arc furnaces (EAF). Multiple synchronous Reference frame (MSRF) analysis is used to decompose the frequency components of the EAF current and voltage waveforms into dqo components. Then using low- pass filters and prediction of the future values of these dqo components, reference signals for APFs are generated. Three different methods have been developed. In two of them, a low- pass Butterworth filter is used together with a linear FIR based prediction or long short-term memory network (LSTM) for prediction. In the third method, a deep convolutional neural network (CNN) combined with a LSTM network is used to filter and predict at the same time. For a 40 ms prediction horizon, the proposed methods provide 2.06%, 0.31%, 0.99% prediction errors of the dqo components for the Butterworth and linear prediction, Butterworth and LSTM and CNN with LSTM, respectively. The error of the predicted reconstructed waveforms of flicker, harmonics, and interharmonics resulted in 8.5%, 1.90%, and 3.2% reconstruction errors for the above-mentioned methods. Finally, a Simulink and GPU based implementation of predictive APF using Butterworth filter + LSTM and a trivial APF resulted 96% and 60% efficiency on compensation of EAF current interharmonics. </p> </div> </div> </div>

A Comprehensive Analysis of DSTATCOM-Based Power Improvement

Power quality became one of the most critical challenges in today’s modern power system. Utmost primary factors of power quality issues are non-linear fluctuating loads, system interruption, load variances, infrequent loads, and arc furnaces. As a matter of fact, there is occurrence of various electrical distortions, along with voltage spikes, voltage sags, and so forth. Several innovative techniques were employed for the development of circuits which resulted in minimised voltage stability as well as reliability. Utility companies which are electrical are receiving a large quantity of complaints because of these challenges. An approach is required now to explore the power quality challenges in industrial sector, corporate sector, municipal, or domestic sites. Therefore, a few techniques known as Flexible AC transmission system (FACTs) are used to enhance power quality. It is divided into two categories: namely series-FACTs and shunt-FACTS. As name itself define its architecture that in series-FACTS comprised of transmission line which is connected in series with module and connected in parallel in shun-FACTs. We can alleviate the problems related to power quality with the help of these FACTS devices. D-STATCOM is one of the better devices available. D-STATCOM is a shunt-connected solid-state device employed at the distribution network to maintain load-side disturbances. It has exceeded the traditional capacitor being used improve power quality with it's lower upfront investment, outstanding dynamics, absence of static, and lower operational expenses. The sorts, architectural style, operating, control systems, and AI are all described in this article.

Toward On-Line Slag Composition Analysis: Optical Emissions from Laboratory Electric Arc

Electric arc furnaces and ladle furnaces have an important role in the future of steelmaking where $$\hbox {CO}_2$$ CO 2 emissions have to be mitigated to an acceptable level. One way to address this goal is to optimize and improve the current practices by adjusting the chemistry and reactions with material additions or gas injections. These procedures would greatly benefit from on-line slag composition analysis. Since the electric arcs radiate throughout the melting, optical emission spectroscopy is a potential method for such analysis. In this study, optical emissions from the electric arc are measured in a laboratory environment. Dozens of atomic emission lines were correlated with $$\hbox {Cr}_2\hbox {O}_3$$ Cr 2 O 3 , $$\hbox {Fe}_2\hbox {O}_3$$ Fe 2 O 3 , $$\hbox {Al}_2\hbox {O}_3$$ Al 2 O 3 , $$\hbox {SiO}_2$$ SiO 2 , MnO, MgO, CaO, $$\hbox {CaF}_2$$ CaF 2 , $$\hbox {V}_2\hbox {O}_5$$ V 2 O 5 , and Ni content of the slag together with correlation between $$\hbox {CaF}_2$$ CaF 2 and molecular optical emission bands of CaF. Optimal spectral resolution for industrial applications was deducted to be between 0.022 and 0.179 nm.

Investigation of dielectric heating method of zinc-containing materials for chlorides removal

The technological development of the world community makes it necessary to resolve the issues of the produced industrial residues utilization. Worn-out galvanized metal cars, ceilings and other metal structures require orderly utilization. Utilization of galvanized scrap may be carried out in Electric Arc Furnaces (EAF) to obtain intermediate steel and EAF dust. Electric Arc Furnaces dus contains zinc, so it is used as a secondary raw material for the production of metallic zinc. Waelz oxides are formed in Waelz kilns, after the primary processing of EAF dust. Waelz oxide contains halides that must be removed first. Halides may be removed by heating to temperatures of about 1000°C. Dielectric (microwave) heating is a promising and environmentally friendly method for material processing. Microwave heating is carried out without burning natural gas, which leads to decrease of waste gases volume. The work experimentally confirmed the possibility of zinc-containing materials heating at electromagnetic emission exposure. The duration of products heating up to the temperature of 1000°C was 128 - 188 s. The residual content of chloride ions in the calcined products is less than 0.05 wt%.

Replacing Fossil Carbon in the Production of Ferroalloys with a Focus on Bio-Based Carbon: A Review

The production of ferroalloys and alloys like ferronickel, ferrochromium, ferromanganese, silicomanganese, ferrosilicon and silicon is commonly carried out in submerged arc furnaces. Submerged arc furnaces are also used to upgrade ilmenite by producing pig iron and a titania-rich slag. Metal containing resources are smelted in this furnace type using fossil carbon as a reducing agent, which is responsible for a large amount of direct CO2 emissions in those processes. Instead, renewable bio-based carbon could be a viable direct replacement of fossil carbon currently investigated by research institutions and companies to lower the CO2 footprint of produced alloys. A second option could be the usage of hydrogen. However, hydrogen has the disadvantages that current production facilities relying on solid reducing agents need to be adjusted. Furthermore, hydrogen reduction of ignoble metals like chromium, manganese and silicon is only possible at very low H2O/H2 partial pressure ratios. The present article is a comprehensive review of the research carried out regarding the utilization of bio-based carbon for the processing of the mentioned products. Starting with the potential impact of the ferroalloy industry on greenhouse gas emissions, followed by a general description of bio-based reducing agents and unit operations covered by this review, each following chapter presents current research carried out to produce each metal. Most studies focused on pre-reduction or solid-state reduction except the silicon industry, which instead had a strong focus on smelting up to an industrial-scale and the design of bio-based carbon for submerged arc furnace processes. Those results might be transferable to other submerged arc furnace processes as well and could help to accelerate research to produce other metals. Deviations between the amount of research and scale of tests for the same unit operation but different metal resources were identified and closer cooperation could be helpful to transfer knowledge from one area to another. Life cycle assessment to produce ferronickel and silicon already revealed the potential of bio-based reducing agents in terms of greenhouse gas emissions, but was not carried out for other metals until now.