Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters

Manojlović, Vaso; Kamberović, Željko; Korać, Marija; Dotlić, Milan

doi:10.1016/j.apenergy.2021.118209

Cited by 32 publications

(12 citation statements)

References 38 publications

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“…For instance, the use of computational fluid dynamics (CFD) and finite element analysis (FEA) have been used in several studies to optimize the design of melting and casting operations and to study the heat transfer processes within the furnace and mold, allowing for the identification of potential energy savings [16,17]. Optimization techniques such as mathematical programming, artificial intelligence, and machine learning have also been reported to reduce energy consumption in the foundry process [18][19][20][21].…”

Section: Energy Efficiency In Foundry Productionmentioning

confidence: 99%

Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-criteria Decision Making

Howard,

Jørgensen,

2023

Lecture Notes in Computer Science

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Improving energy efficiency in industrial production processes is crucial for competitiveness, and compliance with climate policies. This paper introduces a data-driven approach to identify optimal melting patterns in induction furnaces. Through time-series K-means clustering the melting patterns could be classified into distinct clusters based on temperature profiles. Using the elbow method, 12 clusters were identified, representing the range of melting patterns. Performance parameters such as melting time, energy-specific performance, and carbon cost were established for each cluster, indicating furnace efficiency and environmental impact. Multiple criteria decision-making methods including Simple Additive Weighting, Multiplicative Exponential Weighting, Technique for Order of Preference by Similarity to Ideal Solution, modified TOPSIS, and VlseKriterijumska Optimizacija I Kompromisno Resenje were utilized to determine the best-practice cluster. The study successfully identified the cluster with the best performance. Implementing the best practice operation resulted in an 8.6% reduction in electricity costs, highlighting the potential energy savings in the foundry.

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Section: Energy Efficiency In Foundry Productionmentioning

confidence: 99%

Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-criteria Decision Making

Howard,

Jørgensen,

2023

Lecture Notes in Computer Science

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“…A low value of means on the contrary that the technology can be improved, at least from a theoretical point of view. Important kinetic limitations such as anodic and cathodic overpotentials in electrolysis technologies 233 as well as the unavoidable presence of temperature gradients and high temperature output streams 234 may prevent reaching a value of close to one in most pyrometallurgical processes. Strategies to harness the residual energy of these processes to increase will be presented in “ Valorization of residual energy in pyrometallurgical processes ” section.…”

Section: Renewable Energies In Pyrometallurgymentioning

confidence: 99%

“…One mature technology already fully integrated in pyrometallurgy are electric arc furnaces 33 as highlighted in “ Electric arc furnaces ” section. The operating parameters of EAF furnaces are now being optimized via AI and machine learning, 234 which are other tools to reduce CO emissions and maximize energy efficiency. Here are some other electric-based heating strategies.…”

Section: Renewable Energies In Pyrometallurgymentioning

confidence: 99%

Greener reactants, renewable energies and environmental impact mitigation strategies in pyrometallurgical processes: A review

Harvey

Courchesne

et al. 2022

MRS Energy & Sustainability

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Metals and alloys are among the most technologically important materials for our industrialized societies. They are the most common structural materials used in cars, airplanes and buildings, and constitute the technological core of most electronic devices. They allow the transportation of energy over great distances and are exploited in critical parts of renewable energy technologies. Even though primary metal production industries are mature and operate optimized pyrometallurgical processes, they extensively rely on cheap and abundant carbonaceous reactants (fossil fuels, coke), require high power heating units (which are also typically powered by fossil fuels) to calcine, roast, smelt and refine, and they generate many output streams with high residual energy content. Many unit operations also generate hazardous gaseous species on top of large CO2 emissions which require gas-scrubbing and capture strategies for the future. Therefore, there are still many opportunities to lower the environmental footprint of key pyrometallurgical operations. This paper explores the possibility to use greener reactants such as bio-fuels, bio-char, hydrogen and ammonia in different pyrometallurgical units. It also identifies all recycled streams that are available (such as steel and aluminum scraps, electronic waste and Li-ion batteries) as well as the technological challenges associated with their integration in primary metal processes. A complete discussion about the alternatives to carbon-based reduction is constructed around the use of hydrogen, metallo-reduction as well as inert anode electrometallurgy. The review work is completed with an overview of the different approaches to use renewable energies and valorize residual heat in pyrometallurgical units. Finally, strategies to mitigate environmental impacts of pyrometallurgical operations such as CO2 capture utilization and storage as well as gas scrubbing technologies are detailed. This original review paper brings together for the first time all potential strategies and efforts that could be deployed in the future to decrease the environmental footprint of the pyrometallurgical industry. It is primarily intended to favour collaborative work and establish synergies between academia, the pyrometallurgical industry, decision-makers and equipment providers. Graphical abstract Highlights A more sustainable production of metals using greener reactants, green electricity or carbon capture is possible and sometimes already underway. More investments and pressure are required to hasten change. Discussion Is there enough pressure on the aluminum and steel industries to meet the set climate targets? The greenhouse gas emissions of existing facilities can often be partly mitigated by retrofitting them with green technologies, should we close plants prematurely to build new plants using greener technologies? Since green or renewable resources presently have limited availability, in which sector should we use them to maximize their benefits?

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“…e deployment of electric arc furnaces (EAFs) is one of the well-known and efficient approaches in steel industries [1,2]. e EAF-based technologies have ranked as the second steelmaking process [3]. Due to the stochastic changes in the arc length and other behaviors of electric arcs during the fusion and refining process, EAF's characteristics would be dynamic, nonlinear, and time-variant [4,5].…”

Section: Motivation and Incitementmentioning

confidence: 99%

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

Nooshabadi

Sadeghi

Hashemi‐Dezaki

2022

International Transactions on Electrical Energy Systems

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The stochastic and nonlinear characteristics of electric arc furnaces (EAFs) lead to power quality challenges in the power system. In studying EAF behaviors, having optimized characteristics/models, selecting a suitable and optimum model that adapts to the actual characteristics of EAFs, and investigating simulation software’s capability for implementing EAF models are essential. However, the literature shows a research gap in investigating EAF simulations in various software products based on different models. This paper studies several time-domain models, such as piece-wise linear, modified piece-wise linear, hyperbolic, exponential, and exponential-hyperbolic models, for EAF modeling and simulation. The optimal estimation of parameters for the introduced models is necessary to adapt actual EAF characteristics. Thus, one of the studies taken in this paper is optimizing the EAF model’s characteristics. The proposed optimization problem is solved using the genetic algorithm (GA) and particle swarm optimization (PSO). Moreover, the optimized models are simulated in DIgSILENT and EMTP-RV to investigate different EAF models from the viewpoint of accuracy and efficiency. The optimization of different EAF models’ characteristics and comparison of EMTP-RV and DIgSILENT in simulating EAF behavior are the contributions of this paper. The proposed method is validated based on the actual data of a realistic EAF-based steel company in Iran. The obtained results show that the modified piece-wise linear model has the most accuracy in identifying the EAF behavior. The test results based on DIgSILENT and EMTP-RV simulations imply that the EAF could be simulated with high accuracy using modified piece-wise linear and piece-wise linear models. In general, EMTP-RV has expressed more accuracy in simulating different EAF models, and the simulation execution speed of EMTP-RV is around 2.5 times faster than DIgSILENT. In contrast, DIgSILENT is more suitable to facilitate the power system studies of EAF according to its extensive study tools and library.

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Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters

Cited by 32 publications

References 38 publications

Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-criteria Decision Making

Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-criteria Decision Making

Greener reactants, renewable energies and environmental impact mitigation strategies in pyrometallurgical processes: A review

Optimal Electric Arc Furnace Model’s Characteristics Using Genetic Algorithm and Particle Swarm Optimization and Comparison of Various Optimal Characteristics in DIgSILENT and EMTP-RV

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