Process modelling and simulation of electric arc furnace steelmaking to allow prognostic evaluations of process environmental and energy impacts

Matino, Ismael; Alcamisi, E.; Colla, Valentina; Baragiola, S.; Moni, Paolo

doi:10.1051/mattech/2016004

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…Different modelling tools can be exploited after tuning through process data by enforcing resource and energy efficiency [70][71][72]. Furthermore, such modelling tools can also be embedded into processes of digital twins and advanced monitoring systems to improve energy and resource efficiency in steelworks [73,74].…”

Section: Modelling/simulation For By-productsmentioning

confidence: 99%

Future Research and Developments on Reuse and Recycling of Steelmaking By-Products

Colla

Branca

Pietruck

et al. 2023

Metals

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In the steel sector, sustainable management of by-products is a key challenge to preserve natural resources and achieve the zero waste goal. In this paper, the main trends of future research and development on reuse and recycling of by-products of the steel industry are presented in the form of a roadmap, which is the outcome of a dissemination project funded by the European Union based on the analysis of the most relevant and recent European projects concerning reuse and recycling of by-products from the steel production cycle. In particular, the developed roadmap highlights the most important topics of future research activities and challenges related to reuse and recycling of by-products from the existing or alternative steelmaking routes. A time horizon of 10 years has been considered, taking into account the European Commission targets to achieve carbon neutrality in a circular economy context. In addition, current technological trends derived from past and ongoing research projects are analysed. Research needs are based on the main categories of by-products and residual materials. Due to the different pathways to reduce CO2 emissions, each category is divided into subcategories considering both current and novel process routes targeting decarbonization of steel production. This work identifies the most urgent and demanding research directions for the coming years based on a survey targeting the steel companies, services providers of the steel industry and research organizations active in the field.

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Section: Modelling/simulation For By-productsmentioning

confidence: 99%

Future Research and Developments on Reuse and Recycling of Steelmaking By-Products

Colla

Branca

Pietruck

et al. 2023

Metals

Self Cite

View full text Add to dashboard Cite

show abstract

“…A number of examples can be found in literature, where different kind of modelling tools are exploited after being tuned through process data in order to improve environmental sustainability of steelmaking processes, by enforcing resource and energy efficiency [15][16][17][18]. Such modelling tools can also be embedded into advanced monitoring systems for improving energy and resource efficiency at plant level [19,20]. The greater the capability to collect data which exhaustively represent the processes behaviors, the higher the possibility to exploit computationally efficient and data-driven models, which are often based on ML tools and techniques, such as Artificial Neural Networks (ANN) [21,22].…”

Section: Enabling Circular Economy and Industrial Symbiosis Through Digital Transformationmentioning

confidence: 99%

“…® was exploited for the first time to model and simulate the entire electric steelmaking route up to the continuous caster and including water and gas networks. The different sub-models were validated by exploiting industrial data, and the overall simulation tool was linked to a metrics evaluation tool and a Life Cycle Analysis tool, which both output KPIs related to different aspects of the environmental impact [20]. The simulation models output the main energy and water consumptions, off-gases parameters, slag and steel chemical compositions, by facilitating joint consideration of product quality, process yield and efficiency, as well as environmental footprint-related variables.…”

Section: Plusmentioning

confidence: 99%

Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes

Colla

Pietrosanti

Malfa³

et al. 2020

Matériaux & Techniques

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The concepts of Circular Economy and Industrial Symbiosis are nowadays considered by policy makers a key for the sustainability of the whole European Industry. However, in the era of Industry4.0, this results into an extremely complex scenario requiring new business models and involve the whole value chain, and representing an opportunity as well. Moreover, in order to properly consider the environmental pillar of sustainability, the quality of available information represents a challenge in taking appropriate decisions, considering inhomogeneity of data sources, asynchronous nature of data sampling in terms of clock time and frequency, and different available volumes. In this sense, Big Data techniques and tools are fundamental in order to handle, analyze and process such heterogeneity, to provide a timely and meaningful data and information interpretation for making exploitation of Machine Learning and Artificial Intelligence possible. Handling and fully exploiting the complexity of the current monitoring and automation systems calls for deep exploitation of advanced modelling and simulation techniques to define and develop proper Environmental Decision Support Systems. Such systems are expected to extensively support plant managers and operators in taking better, faster and more focused decisions for improving the environmental footprint of production processes, while preserving optimal product quality and smooth process operation. The paper describes a vision from the steel industry on the way in which the above concepts can be implemented in the steel sector through some application examples aimed at improving socio-economic and environmental sustainability of production cycles.

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“…Milford et al combined mass flow analysis and process emissions intensities to predict emissions of the global IS industry based on different CO 2 abatement scenarios [44]. Matino et al constructed a simulation model to evaluate the energy consumptions and emissions during the steel production process [45]. Song et al posited a comprehensive decomposition framework to observe the determinants of CO 2 emissions of China's IS industry [46].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Driving Forces of Green Growth: Empirical Analysis on China’s Iron and Steel Industry

Qin

Wang

et al. 2019

Sustainability

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Green growth of sectors with extensive energy and environmental impacts represented by the iron and steel (IS) industry plays a critical role in fostering sustainable and inclusive development. This paper constructs an updated evaluation model for green growth estimation by combining a global Malmquist–Luenberger index and an epsilon-based measure. A comprehensive decomposition framework is further posited to reveal underlying determinants of green growth. Research findings based on empirical tests from the panel data of Chinese IS firms spanning 2010–2017 demonstrate that the green growth level increased by 0.80% annually and followed the east–central–west gradient distribution. Technology progress and scale efficiency are two main contributors to China’s IS industrial green growth, whereas managerial inefficiency and scale bias of technical change hinder the productivity gains. The aforementioned factors also play different roles in enhancing the green growth of divergent regions. In accordance, context-specific practical implications and suggestions are put forward based on the research findings to facilitate the green growth of China’s IS industry.

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Process modelling and simulation of electric arc furnace steelmaking to allow prognostic evaluations of process environmental and energy impacts

Cited by 12 publications

References 10 publications

Future Research and Developments on Reuse and Recycling of Steelmaking By-Products

Future Research and Developments on Reuse and Recycling of Steelmaking By-Products

Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes

Exploring Driving Forces of Green Growth: Empirical Analysis on China’s Iron and Steel Industry

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