Comparison of data-based models for prediction and optimization of energy consumption in electric arc furnace (EAF)

Andonovski, Goran; Tomažič, Simon

doi:10.1016/j.ifacol.2022.09.123

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…(2) Intelligent control: utilizing big data and IoT technology to remotely monitor and intelligently schedule electric heaters to reduce emissions and enhance production efficiency Using several data models for analysis and comparison, Goran Andonovski and his colleagues collected EAF batch data to address the issue of streamlining production procedures in the steel sector to conserve resources. Due to this research, more energy will be saved in the future [12].…”

Section: Development Trendsmentioning

confidence: 96%

Temperature control in electric furnaces: Methods, applications, and challenges

Chen

2023

J. Phys.: Conf. Ser.

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Temperature control of electric heating furnaces, a common piece of equipment in industrial production, is essential for assuring product quality and enhancing production effectiveness. In order to better understand the similarities and differences in temperature management between various types of electric heating furnaces, this paper first discusses the fundamental concepts and classifications of electric heating furnaces. Following that, it systematically describes the applications of the various temperature control techniques now used for electric heating furnaces, such as PID control, fuzzy logic control, genetic algorithm control, and model predictive control. This paper further analyzes the difficulties in controlling the temperature of electric heating furnaces and identifies potential future development trends in light of the issues that currently used control methods must deal with in practical applications, such as insufficient control accuracy, slow response speed, and poor stability. Finally, it summarizes the information in the entire paper and considers the direction of temperature control research for electric heating furnaces. This research will focus on new control theories and algorithms, furnace body design optimization, control system integration, and intelligent adaptive temperature control. In order to advance the development and advancement of technology in this area, the goal of this article is to provide a thorough theoretical reference and practical advice for the temperature management of electric heating furnaces.

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Section: Development Trendsmentioning

confidence: 96%

Temperature control in electric furnaces: Methods, applications, and challenges

Chen

2023

J. Phys.: Conf. Ser.

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“…Data-driven models utilize empirical data to develop predictive algorithms for EAF operation. Andonnovski and Tomazic [ 53 ] conducted a comparative analysis of data-driven models for forecasting and optimizing energy usage in EAF. During the modeling phase, they emphasized the advancement of fuzzy modeling techniques in contrast to several established machine learning approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

A review of simulation and numerical modeling of electric arc furnace (EAF) and its processes

Abadi,

Tang,

Rashidi

2024

Heliyon

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“…Machine learning approaches have recently gained popularity in EAF energy consumption modelling. Andonovski and Tomažič (2022) [16] Commonly understood valuable input parameters for EAF energy consumption modelling and simulation include scrap mass, tap-to-tap time, scrap quality, produced steel quality, steel temperature, additives such as lime or ferrous alloys, and the amount of injected oxygen. However, it is important to note regarding Carlsson et al's work [18] that power-on-time (referring to the duration for which the electric arc is active and the furnace is powered on) during a heat, while impactful, is not suitable due to its lack of independence.…”

Section: Energy Forecast Modelling Of Eafsmentioning

confidence: 99%

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

Zawodnik,

Schwaiger,

Sorger

et al. 2024

Energies

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The iron and steel industry significantly contributes to global energy use and greenhouse gas emissions. The rising deployment of volatile renewables and the resultant need for flexibility, coupled with specific challenges in electric steelmaking (e.g., operation optimization, optimized power purchasing, effective grid capacity monitoring), require accurate energy consumption and demand forecasts for electric steel mills to align with the energy transition. This study investigates diverse approaches to forecast the energy consumption and demand of an electric arc furnace—one of the largest consumers on the grid—considering various forecast horizons and objectives with limited knowledge on process parameters. The results are evaluated for accuracy, robustness, and costs. Two grid connection capacity monitoring approaches—a one-step and a multi-step Long Short-Term Memory neural network—are assessed for intra-hour energy demand forecasts. The one-step approach effectively models energy demand, while the multi-step approach encounters challenges in representing different operational phases of the furnace. By employing a combined statistic–stochastic model integrating a Seasonal Auto-Regressive Moving Average model and Markov chains, the study extends the forecast horizon for optimized day-ahead electricity procurement. However, the accuracy decreases as the forecast horizon lengthens. Nevertheless, the day-ahead forecast provides substantial benefits, including reduced energy balancing needs and potential cost savings.

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Comparison of data-based models for prediction and optimization of energy consumption in electric arc furnace (EAF)

Cited by 10 publications

References 23 publications

Temperature control in electric furnaces: Methods, applications, and challenges

Temperature control in electric furnaces: Methods, applications, and challenges

A review of simulation and numerical modeling of electric arc furnace (EAF) and its processes

Tackling Uncertainty: Forecasting the Energy Consumption and Demand of an Electric Arc Furnace with Limited Knowledge on Process Parameters

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