Simulation techniques for an Efficient Use of Resources: An overview for the steelmaking field

Matino, Ismael; Colla, Valentina; Romaniello, Lea; Rosito, Felice; Portulano, Luigi

doi:10.1109/wcst.2015.7415115

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Often, the most suitable modelling approach consists of combining these approaches. Some examples of advanced simulation tools applied to improve the environmental performance of steelmaking processes concern advanced flow sheeting models developed through the Aspen Plus ® suite, which are exploited to assess the viability of process integration solutions to improve by-products handling and water efficiency in integrated steelworks [61][62][63]. In a further application, an Aspen Plus ® -based model was tuned through genetic algorithms by exploiting experimental data to test and validate innovative treatments for BF gas washing waters [64].…”

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

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“…Recovery and reuse of treated wastewater are ideal options from both environmental and economic perspectives. How-ever, the hazardous sludge generated from the treatment process has to be handled according to regulatory norms [1,7,[9][10][11].…”

Section: Water Cyclementioning

confidence: 99%

Ascertaining and Optimizing the Water Footprint and Sludge Management Practice in Steel Industries

Choudhury

Singh²,

Veeraraghavan

et al. 2023

Water

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Steelmaking is a water-intensive process. The mean water intake against each ton of steel manufactured is ascertained as between 2 and 20 m3. Primarily, the stated requirement is in the form of make-up water to compensate for evaporation and mechanical losses and does not contribute to wastewater generation. Conversely, unit operations, such as rolling, continuous casting, pickling, etc., generate highly complex wastewater rich in polycyclic aromatic hydrocarbons (PAH), cyanide, ammonia, non-consumed acids, benzene, toluene, xylene, oil, grease, etc. Further, the conjugative wastewater contains a high concentration of metallic oxides, toxic elements, oil, nitrogen, and heavy metals such as zinc, nickel, chromium, etc. These contaminants are generally treated and neutralized using physicochemical and membrane-based systems. This also yields hazardous sludge, which is landfilled, thereby incurring an ancillary financial burden. However, sludge can be a frugal source of extracting multi-dimensional benefits. The present review investigated and identified the most water-intensive and wastewater/sludge-contributing unit operations and proposed a preferential combination of treatments to balance efficacy and economy. Further, the various global practices for sludge recycling and management documented in the existing literature are summarized and ranked with the help of the analytic hierarchy process (AHP). The findings revealed concrete making and nutrient recovery as the most- and least-preferred recycling alternatives.

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“…The importance of advanced simulation tools in order to improve the environmental footprint of the steel production processes was showed by several research projects. For instance, in the EU-funded project entitled "Efficient use of resources in steel plants through Process Integration" (ReffiPlant), advanced flowsheeting models were exploited in order to assess the viability of Process Integration solutions capable to improve by-products handling and water efficiency in integrated steelworks [40,47,48]. Such models were developed by means of the Aspen Plus ® modelling and simulation suite and were tuned through process data, by minimizing the difference among the values of the monitored variables of the real and "virtual" processes.…”

Section: Exemplar Applications Of Advanced Simulation and Machine Learning For Monitoring And Control Of The Environmental Footprintmentioning

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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Simulation techniques for an Efficient Use of Resources: An overview for the steelmaking field

Cited by 9 publications

References 19 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

Ascertaining and Optimizing the Water Footprint and Sludge Management Practice in Steel Industries

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

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