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Dr. Ismael Matino
ICT-COISP Center of Institute of Communication, lnformation and Perception Technologies TECIP, Scuola Superiore Sant'Anna, 56124 Pisa, Italy

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0 Artificial Intelligence
0 Machine Learning
0 Metallurgy
0 environmental impact
0 circular economy

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environmental impact
Modelling and Simulation
Process integration and optimization
Machine Learning
circular economy
Valorization of secondary material and energy resources

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Review
Published: 28 July 2021 in Metals
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The steel industry is an important engine for sustainable growth, added value, and high-quality employment within the European Union. It is committed to reducing its CO2 emissions due to production by up to 50% by 2030 compared to 1990′s level by developing and upscaling the technologies required to contribute to European initiatives, such as the Circular Economy Action Plan (CEAP) and the European Green Deal (EGD). The Clean Steel Partnership (CSP, a public–private partnership), which is led by the European Steel Association (EUROFER) and the European Steel Technology Platform (ESTEP), defined technological CO2 mitigation pathways comprising carbon direct avoidance (CDA), smart carbon usage SCU), and a circular economy (CE). CE approaches ensure competitiveness through increased resource efficiency and sustainability and consist of different issues, such as the valorization of steelmaking residues (dusts, slags, sludge) for internal recycling in the steelmaking process, enhanced steel recycling (scrap use), the use of secondary carbon carriers from non-steel sectors as a reducing agent and energy source in the steelmaking process chain, and CE business models (supply chain analyses). The current paper gives an overview of different technological CE approaches as obtained in a dedicated workshop called “Resi4Future—Residue valorization in iron and steel industry: sustainable solutions for a cleaner and more competitive future Europe” that was organized by ESTEP to focus on future challenges toward the final goal of industrial deployment.

ACS Style

Johannes Rieger; Valentina Colla; Ismael Matino; Teresa Branca; Gerald Stubbe; Andrea Panizza; Carlo Brondi; Mohammadtaghi Falsafi; Johannes Hage; Xuan Wang; Bernhard Voraberger; Thomas Fenzl; Victoria Masaguer; Eros Faraci; Loredana di Sante; Filippo Cirilli; Florian Loose; Christoph Thaler; Aintzane Soto; Piero Frittella; Gianpaolo Foglio; Cosmo di Cecca; Mattia Tellaroli; Marco Corbella; Marta Guzzon; Enrico Malfa; Agnieszka Morillon; David Algermissen; Klaus Peters; Delphine Snaet. Residue Valorization in the Iron and Steel Industries: Sustainable Solutions for a Cleaner and More Competitive Future Europe. Metals 2021, 11, 1202 .

AMA Style

Johannes Rieger, Valentina Colla, Ismael Matino, Teresa Branca, Gerald Stubbe, Andrea Panizza, Carlo Brondi, Mohammadtaghi Falsafi, Johannes Hage, Xuan Wang, Bernhard Voraberger, Thomas Fenzl, Victoria Masaguer, Eros Faraci, Loredana di Sante, Filippo Cirilli, Florian Loose, Christoph Thaler, Aintzane Soto, Piero Frittella, Gianpaolo Foglio, Cosmo di Cecca, Mattia Tellaroli, Marco Corbella, Marta Guzzon, Enrico Malfa, Agnieszka Morillon, David Algermissen, Klaus Peters, Delphine Snaet. Residue Valorization in the Iron and Steel Industries: Sustainable Solutions for a Cleaner and More Competitive Future Europe. Metals. 2021; 11 (8):1202.

Chicago/Turabian Style

Johannes Rieger; Valentina Colla; Ismael Matino; Teresa Branca; Gerald Stubbe; Andrea Panizza; Carlo Brondi; Mohammadtaghi Falsafi; Johannes Hage; Xuan Wang; Bernhard Voraberger; Thomas Fenzl; Victoria Masaguer; Eros Faraci; Loredana di Sante; Filippo Cirilli; Florian Loose; Christoph Thaler; Aintzane Soto; Piero Frittella; Gianpaolo Foglio; Cosmo di Cecca; Mattia Tellaroli; Marco Corbella; Marta Guzzon; Enrico Malfa; Agnieszka Morillon; David Algermissen; Klaus Peters; Delphine Snaet. 2021. "Residue Valorization in the Iron and Steel Industries: Sustainable Solutions for a Cleaner and More Competitive Future Europe." Metals 11, no. 8: 1202.

Journal article
Published: 18 May 2021 in Energies
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The steel industry is among the highest carbon-emitting industrial sectors. Since the steel production process is already exhaustively optimized, alternative routes are sought in order to increase carbon efficiency and reduce these emissions. During steel production, three main carbon-containing off-gases are generated: blast furnace gas, coke oven gas and basic oxygen furnace gas. In the present work, the addition of renewable hydrogen by electrolysis to those steelworks off-gases is studied for the production of methane and methanol. Different case scenarios are investigated using AspenPlusTM flowsheet simulations, which differ on the end-product, the feedstock flowrates and on the production of power. Each case study is evaluated in terms of hydrogen and electrolysis requirements, carbon conversion, hydrogen consumption, and product yields. The findings of this study showed that the electrolysis requirements surpass the energy content of the steelwork’s feedstock. However, for the methanol synthesis cases, substantial improvements can be achieved if recycling a significant amount of the residual hydrogen.

ACS Style

Michael Bampaou; Kyriakos Panopoulos; Panos Seferlis; Spyridon Voutetakis; Ismael Matino; Alice Petrucciani; Antonella Zaccara; Valentina Colla; Stefano Dettori; Teresa Annunziata Branca; Vincenzo Iannino. Integration of Renewable Hydrogen Production in Steelworks Off-Gases for the Synthesis of Methanol and Methane. Energies 2021, 14, 2904 .

AMA Style

Michael Bampaou, Kyriakos Panopoulos, Panos Seferlis, Spyridon Voutetakis, Ismael Matino, Alice Petrucciani, Antonella Zaccara, Valentina Colla, Stefano Dettori, Teresa Annunziata Branca, Vincenzo Iannino. Integration of Renewable Hydrogen Production in Steelworks Off-Gases for the Synthesis of Methanol and Methane. Energies. 2021; 14 (10):2904.

Chicago/Turabian Style

Michael Bampaou; Kyriakos Panopoulos; Panos Seferlis; Spyridon Voutetakis; Ismael Matino; Alice Petrucciani; Antonella Zaccara; Valentina Colla; Stefano Dettori; Teresa Annunziata Branca; Vincenzo Iannino. 2021. "Integration of Renewable Hydrogen Production in Steelworks Off-Gases for the Synthesis of Methanol and Methane." Energies 14, no. 10: 2904.

Journal article
Published: 16 April 2021 in Neural Computing and Applications
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This article presents the application of a recent neural network topology known as the deep echo state network to the prediction and modeling of strongly nonlinear systems typical of the process industry. The article analyzes the results by introducing a comparison with one of the most common and efficient topologies, the long short-term memories, in order to highlight the strengths and weaknesses of a reservoir computing approach compared to one currently considered as a standard of recurrent neural network. As benchmark application, two specific processes common in the integrated steelworks are selected, with the purpose of forecasting the future energy exchanges and transformations. The procedures of training, validation and test are based on data analysis, outlier detection and reconciliation and variable selection starting from real field industrial data. The analysis of results shows the effectiveness of deep echo state networks and their strong forecasting capabilities with respect to standard recurrent methodologies both in terms of training procedures and accuracy.

ACS Style

Stefano Dettori; Ismael Matino; Valentina Colla; Ramon Speets. A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace. Neural Computing and Applications 2021, 1 -13.

AMA Style

Stefano Dettori, Ismael Matino, Valentina Colla, Ramon Speets. A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace. Neural Computing and Applications. 2021; ():1-13.

Chicago/Turabian Style

Stefano Dettori; Ismael Matino; Valentina Colla; Ramon Speets. 2021. "A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace." Neural Computing and Applications , no. : 1-13.

Journal article
Published: 16 April 2021
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This article presents the application of a recent neural network topology known as the deep echo state network to the prediction and modeling of strongly nonlinear systems typical of the process industry. The article analyzes the results by introducing a comparison with one of the most common and efficient topologies, the long short-term memories, in order to highlight the strengths and weaknesses of a reservoir computing approach compared to one currently considered as a standard of recurrent neural network. As benchmark application, two specific processes common in the integrated steelworks are selected, with the purpose of forecasting the future energy exchanges and transformations. The procedures of training, validation and test are based on data analysis, outlier detection and reconciliation and variable selection starting from real field industrial data. The analysis of results shows the effectiveness of deep echo state networks and their strong forecasting capabilities with respect to standard recurrent methodologies both in terms of training procedures and accuracy. The online version contains supplementary material available at 10.1007/s00521-021-05984-x.

ACS Style

Stefano Dettori; Ismael Matino; Valentina Colla; Ramon Speets. A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace. 2021, 1 -13.

AMA Style

Stefano Dettori, Ismael Matino, Valentina Colla, Ramon Speets. A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace. . 2021; ():1-13.

Chicago/Turabian Style

Stefano Dettori; Ismael Matino; Valentina Colla; Ramon Speets. 2021. "A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace." , no. : 1-13.

Conference paper
Published: 05 February 2021 in Advances in Intelligent Systems and Computing
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Within integrated steelworks, several sub-processes produce off-gases, which are suitable for reuse as energy sources for other internal processes as well as for the production of energy. An adequate and optimal distribution of these gases among their users allows valorizing at best their energy content by minimizing the need to both burn them through torches due to storage issues and to acquire natural gas to satisfy the internal energetic demand. To this purpose, the volume and energetic value of produced gases as well as the demands from internal users must be known in advance, in order to implement model-predictive control strategies aimed at satisfying the demands on the short-medium term based on the production scheduling. Such forecasting knowledge also enhances the capability to react to the variability of the process scheduling as well as to other unforeseen events. The paper depicts an application of Machine Learning-based models to forecast off-gases and further energy carriers productions and demands within integrated steelworks. The forecasting models are integrated into a complex hierarchical control strategy aimed at optimizing the distribution of such gases.

ACS Style

Ismael Matino; Stefano Dettori; Angelo Castellano; Ruben Matino; Claudio Mocci; Marco Vannocci; Alessandro Maddaloni; Valentina Colla; Andreas Wolff. Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks. Advances in Intelligent Systems and Computing 2021, 104 -118.

AMA Style

Ismael Matino, Stefano Dettori, Angelo Castellano, Ruben Matino, Claudio Mocci, Marco Vannocci, Alessandro Maddaloni, Valentina Colla, Andreas Wolff. Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks. Advances in Intelligent Systems and Computing. 2021; ():104-118.

Chicago/Turabian Style

Ismael Matino; Stefano Dettori; Angelo Castellano; Ruben Matino; Claudio Mocci; Marco Vannocci; Alessandro Maddaloni; Valentina Colla; Andreas Wolff. 2021. "Machine Learning-Based Models for Supporting Optimal Exploitation of Process Off-Gases in Integrated Steelworks." Advances in Intelligent Systems and Computing , no. : 104-118.

Journal article
Published: 18 November 2020 in Metals
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Within integrated steelmaking industries significant research efforts are devoted to the efficient use of resources and the reduction of CO2 emissions. Integrated steelworks consume a considerable quantity of raw materials and produce a high amount of by-products, such as off-gases, currently used for the internal production of heat, steam or electricity. These off-gases can be further valorized as feedstock for methane and methanol syntheses, but their hydrogen content is often inadequate to reach high conversions in synthesis processes. The addition of hydrogen is fundamental and a suitable hydrogen production process must be selected to obtain advantages in process economy and sustainability. This paper presents a comparative analysis of different hydrogen production processes from renewable energy, namely polymer electrolyte membrane electrolysis, solid oxide electrolyze cell electrolysis, and biomass gasification. Aspen Plus® V11-based models were developed, and simulations were conducted for sensitivity analyses to acquire useful information related to the process behavior. Advantages and disadvantages for each considered process were highlighted. In addition, the integration of the analyzed hydrogen production methods with methane and methanol syntheses is analyzed through further Aspen Plus®-based simulations. The pros and cons of the different hydrogen production options coupled with methane and methanol syntheses included in steelmaking industries are analyzed.

ACS Style

Antonella Zaccara; Alice Petrucciani; Ismael Matino; Teresa Annunziata Branca; Stefano Dettori; Vincenzo Iannino; Valentina Colla; Michael Bampaou; Kyriakos Panopoulos. Renewable Hydrogen Production Processes for the Off-Gas Valorization in Integrated Steelworks through Hydrogen Intensified Methane and Methanol Syntheses. Metals 2020, 10, 1 .

AMA Style

Antonella Zaccara, Alice Petrucciani, Ismael Matino, Teresa Annunziata Branca, Stefano Dettori, Vincenzo Iannino, Valentina Colla, Michael Bampaou, Kyriakos Panopoulos. Renewable Hydrogen Production Processes for the Off-Gas Valorization in Integrated Steelworks through Hydrogen Intensified Methane and Methanol Syntheses. Metals. 2020; 10 (11):1.

Chicago/Turabian Style

Antonella Zaccara; Alice Petrucciani; Ismael Matino; Teresa Annunziata Branca; Stefano Dettori; Vincenzo Iannino; Valentina Colla; Michael Bampaou; Kyriakos Panopoulos. 2020. "Renewable Hydrogen Production Processes for the Off-Gas Valorization in Integrated Steelworks through Hydrogen Intensified Methane and Methanol Syntheses." Metals 10, no. 11: 1.

Conference paper
Published: 29 May 2020 in IFIP Advances in Information and Communication Technology
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ACS Style

Stefano Dettori; Ismael Matino; Valentina Colla; Ramon Speets. Deep Echo State Networks in Industrial Applications. IFIP Advances in Information and Communication Technology 2020, 53 -63.

AMA Style

Stefano Dettori, Ismael Matino, Valentina Colla, Ramon Speets. Deep Echo State Networks in Industrial Applications. IFIP Advances in Information and Communication Technology. 2020; ():53-63.

Chicago/Turabian Style

Stefano Dettori; Ismael Matino; Valentina Colla; Ramon Speets. 2020. "Deep Echo State Networks in Industrial Applications." IFIP Advances in Information and Communication Technology , no. : 53-63.

Journal article
Published: 27 February 2020 in International journal of simulation: systems, science & technology
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ACS Style

Ismael Matino; Erika Alcamisi; Giacomo Filippo Porzio; Valentina Colla. Application of Unconventional Techniques for Evaluation and Monitoring of Physico-Chemical Properties of Water Streams. International journal of simulation: systems, science & technology 2020, 1 .

AMA Style

Ismael Matino, Erika Alcamisi, Giacomo Filippo Porzio, Valentina Colla. Application of Unconventional Techniques for Evaluation and Monitoring of Physico-Chemical Properties of Water Streams. International journal of simulation: systems, science & technology. 2020; ():1.

Chicago/Turabian Style

Ismael Matino; Erika Alcamisi; Giacomo Filippo Porzio; Valentina Colla. 2020. "Application of Unconventional Techniques for Evaluation and Monitoring of Physico-Chemical Properties of Water Streams." International journal of simulation: systems, science & technology , no. : 1.

Journal article
Published: 01 January 2020 in Matériaux & Techniques
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The efficient use of water resources is one of the main challenges of the steel sector, according to the European Union water policy. On this subject, monitoring and optimization systems, linked to the innovative water treatments, represent important tools to improve water management and the related energy use. The present paper describes a part of the work developed in the early stage of the project entitled “Water and related energy Hub Advanced Management system in steelworks – WHAM”, which is co-funded by the Research Fund for Coal and Steel. The project aims at optimizing water consumption in the steelworks through a holistic combination of on-line monitoring and optimisation and innovative water treatment technologies. As different aspects affect water use in the steelmaking processes, in the first part of the paper, the main technical barriers and factors, that can impact on reuse and recirculation of wastewater and energy efficiency, are analysed. The main constraints on water management in the steel sector, such as fresh water availability, its quality and local legal requirements, were considered in order to maximise the water reuse and recycling. Furthermore, the main barriers, such as environmental issues and several costs, were investigated. In the second part of the paper, a set of Key Performance Indicators are listed. They aim at assessing and monitoring the water management sustainability in a holistic way, both in terms of environmental and economic performances, as well as of new water treatments efficiency and their economic viability. Key Performance Indicators will be used to monitor the efficiency of water management, aiming at achieving significant increase of performances. On the other hand, some of these indicators will be used as objective functions for problems optimization. The computation of the selected Key Performance Indicators will take into account both industrial data and results from simulations that will be carried out after the development of suitable tools in order to assess the feasibility of some relevant process modifications or the applications of new technologies.

ACS Style

Teresa Annunziata Branca; Ismael Matino; Valentina Colla; Alice Petrucciani; Amarjit Kuor Maria Singh; Antonella Zaccara; Teresa Beone; Luca De Cecco; Ville Hakala; Davide Lorito; Santiago Moreira; Elisa Piras. Paving the way for the optimization of water consumption in the steelmaking processes: barriers, analysis and KPIs definition. Matériaux & Techniques 2020, 108, 510 .

AMA Style

Teresa Annunziata Branca, Ismael Matino, Valentina Colla, Alice Petrucciani, Amarjit Kuor Maria Singh, Antonella Zaccara, Teresa Beone, Luca De Cecco, Ville Hakala, Davide Lorito, Santiago Moreira, Elisa Piras. Paving the way for the optimization of water consumption in the steelmaking processes: barriers, analysis and KPIs definition. Matériaux & Techniques. 2020; 108 (5-6):510.

Chicago/Turabian Style

Teresa Annunziata Branca; Ismael Matino; Valentina Colla; Alice Petrucciani; Amarjit Kuor Maria Singh; Antonella Zaccara; Teresa Beone; Luca De Cecco; Ville Hakala; Davide Lorito; Santiago Moreira; Elisa Piras. 2020. "Paving the way for the optimization of water consumption in the steelmaking processes: barriers, analysis and KPIs definition." Matériaux & Techniques 108, no. 5-6: 510.

Journal article
Published: 01 November 2019 in Applied Energy
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ACS Style

Ismael Matino; Stefano Dettori; Valentina Colla; Valentine Weber; Sahar Salame. Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management. Applied Energy 2019, 253, 1 .

AMA Style

Ismael Matino, Stefano Dettori, Valentina Colla, Valentine Weber, Sahar Salame. Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management. Applied Energy. 2019; 253 ():1.

Chicago/Turabian Style

Ismael Matino; Stefano Dettori; Valentina Colla; Valentine Weber; Sahar Salame. 2019. "Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management." Applied Energy 253, no. : 1.

Full paper
Published: 25 July 2019 in steel research international
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Steel industry consumes resources and produces important amounts of by‐products. Process modelling, simulation and optimization can address field tests for maximizing by‐products reuse. This paper presents improvements of a long‐lasting research activity on slag reuse by deepening aspects, only marginally considered in the past. An Aspen Plus® based model is developed to assess different Basic Oxygen Furnace slag pre‐treatment configurations or involved techniques. A previously developed reMIND‐based superstructure is upgraded, considering more slag pre‐treatments and qualities. A simplified Aspen Plus®‐based model of mixing of the different pre‐treated by‐products is applied to evaluate the chemical composition of pellets in each scenario, starting from the computed mixtures. The developed tools are exploited to get a holistic view of steelmaking by‐products reuse and to make multi‐objective optimization studies providing different results. For instance, the mixture to be pelletized completely changes if final products quality is (or not) considered in the optimization. Regarding the optimization combining all the considered objective functions, the best slag treatment includes a wet high intensity magnetic separation stage and only some slags qualities are considered in the pellet mixture. A preliminary economic analysis confirms that an optimal combination of internal and external recoveries of by‐products can produce noteworthy revenues. This article is protected by copyright. All rights reserved.

ACS Style

Ismael Matino; Teresa Annunziata Branca; Barbara Fornai; Valentina Colla; Lea Romaniello. Scenario Analyses for By‐Products Reuse in Integrated Steelmaking Plants by Combining Process Modeling, Simulation, and Optimization Techniques. steel research international 2019, 90, 1 .

AMA Style

Ismael Matino, Teresa Annunziata Branca, Barbara Fornai, Valentina Colla, Lea Romaniello. Scenario Analyses for By‐Products Reuse in Integrated Steelmaking Plants by Combining Process Modeling, Simulation, and Optimization Techniques. steel research international. 2019; 90 (10):1.

Chicago/Turabian Style

Ismael Matino; Teresa Annunziata Branca; Barbara Fornai; Valentina Colla; Lea Romaniello. 2019. "Scenario Analyses for By‐Products Reuse in Integrated Steelmaking Plants by Combining Process Modeling, Simulation, and Optimization Techniques." steel research international 90, no. 10: 1.

Conference paper
Published: 15 May 2019 in Engineering Applications of Neural Networks
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Echo-State Neural Networks represent a very efficient solution for modelling of dynamic systems, thanks to their particular structure, which allows faithful reproduction of the behavior of the system to model with a usually limited computational burden for a training phase. This aspect favors the deployment of Echo-State Neural networks in the industrial field. In this paper, a novel application of such approach is proposed for the modelling of industrial processes. The developed models are part of a complex system for optimizing the exploitation of process off-gases in an integrated steelwork. Two models are presented and discussed, where both shallow Echo-State Neural Networks and Deep Echo State Neural networks are applied. The achieved results are presented and discussed, by comparing advantages and drawbacks of both approaches.

ACS Style

Valentina Colla; Ismael Matino; Stefano Dettori; Silvia Cateni; Ruben Matino. Reservoir Computing Approaches Applied to Energy Management in Industry. Engineering Applications of Neural Networks 2019, 66 -79.

AMA Style

Valentina Colla, Ismael Matino, Stefano Dettori, Silvia Cateni, Ruben Matino. Reservoir Computing Approaches Applied to Energy Management in Industry. Engineering Applications of Neural Networks. 2019; ():66-79.

Chicago/Turabian Style

Valentina Colla; Ismael Matino; Stefano Dettori; Silvia Cateni; Ruben Matino. 2019. "Reservoir Computing Approaches Applied to Energy Management in Industry." Engineering Applications of Neural Networks , no. : 66-79.

Journal article
Published: 21 March 2019 in Matériaux & Techniques
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The European Steel industry is spending considerable efforts in order to improve the socio-economic and environmental sustainability of its processes by promoting any development, which can increase efficiency and lower the environmental impact of the steel production processes. In particular, the European iron and steel sector is strongly committed toward the reduction of energy consumptions and CO2 emissions. Process gases are a very valuable resource: possibilities exist to consider these gases as an intermediate by-product for the production of other valuable energy carriers or products with an associated environmental benefit. Therefore, the process gas networks, especially inside the integrated steelworks, have a fundamental function, as they allow meeting the demand of many processes and producing energy through dedicated facilities. They can also support the production processes by internal electric energy generation and often by supplying energy outside the plant boundaries. On the other hand, such networks are very complex systems interacting with many different production steps and the management of such complex systems is a very difficult task, where many often-counteracting factors need to be jointly taken into account. This paper presents the first outcomes of the research project entitled “Optimization of the management of the process gas network within the integrated steelworks (GASNET)”, which aims at developing a Decision Support System helping the energy managers and other concerned technical personnel to implement an optimized off-gases management and exploitation considering environmental and economic objectives. A series of Key Performance Indicators has been elaborated, in order to monitor the efficiency of the gas management and the objectives of the optimization have been defined. The overall structure of the project and the ongoing work will also be outlined in the paper.

ACS Style

Valentina Colla; Ismael Matino; Stefano Dettori; Alice Petrucciani; Antonella Zaccara; Valentine Weber; Sahar Salame; Natalia Zapata; Santiago Bastida; Andreas Wolff; Ramon Speets; Lea Romaniello. Assessing the efficiency of the off-gas network management in integrated steelworks. Matériaux & Techniques 2019, 107, 104 .

AMA Style

Valentina Colla, Ismael Matino, Stefano Dettori, Alice Petrucciani, Antonella Zaccara, Valentine Weber, Sahar Salame, Natalia Zapata, Santiago Bastida, Andreas Wolff, Ramon Speets, Lea Romaniello. Assessing the efficiency of the off-gas network management in integrated steelworks. Matériaux & Techniques. 2019; 107 (1):104.

Chicago/Turabian Style

Valentina Colla; Ismael Matino; Stefano Dettori; Alice Petrucciani; Antonella Zaccara; Valentine Weber; Sahar Salame; Natalia Zapata; Santiago Bastida; Andreas Wolff; Ramon Speets; Lea Romaniello. 2019. "Assessing the efficiency of the off-gas network management in integrated steelworks." Matériaux & Techniques 107, no. 1: 104.

Journal article
Published: 01 February 2019 in Energy Procedia
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The paper proposes a methodology for modeling of energy transformation equipment which are commonly found in integrated steelworks, mainly focusing on steam production in the Basic Oxygen Furnace and auxiliary boilers, the electric power production in off-gas expansion turbines and some relevant steam and electricity consumers. The modeling approach is based on standard neural networks and Echo State Networks (ESN) for forecasting the variables of interest. All the models are intended as processes predictors to be used in a hierarchical control strategy based on multi-period and multi-objective optimization techniques and model predictive control. The overall target is the optimization of the re-use of off-gas produced in integrated steelworks by minimizing costs and maximizing revenues. Training and validation of models have been carried out by exploiting real historical data provided by steelmaking companies and have been successful tested.

ACS Style

Stefano Dettori; Ismael Matino; Valentina Colla; Valentine Weber; Sahar Salame. Neural Network-based modeling methodologies for energy transformation equipment in integrated steelworks processes. Energy Procedia 2019, 158, 4061 -4066.

AMA Style

Stefano Dettori, Ismael Matino, Valentina Colla, Valentine Weber, Sahar Salame. Neural Network-based modeling methodologies for energy transformation equipment in integrated steelworks processes. Energy Procedia. 2019; 158 ():4061-4066.

Chicago/Turabian Style

Stefano Dettori; Ismael Matino; Valentina Colla; Valentine Weber; Sahar Salame. 2019. "Neural Network-based modeling methodologies for energy transformation equipment in integrated steelworks processes." Energy Procedia 158, no. : 4061-4066.

Journal article
Published: 01 February 2019 in Energy Procedia
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The online optimization of the use of process off gases in integrated steelworks can greatly contribute to increase the sustainability of the steel production. A correct management of these gases could allow both the reduction of natural resources exploitation (e.g. natural gas) and of the facility’s environmental impact. However, in order to achieve an almost complete use of these gases, it is fundamental to forecast their production and consumption according to the production plan and to use such forecasting to optimize the gases distribution inside the network by considering possible interactions. According to these needs, this paper presents two models, which allow forecasting the consumption of blast furnace gas by some major consumers: the hot blast stoves. Due to the almost regular operation of these plants, two kinds of models can be applied: an Echo State Network-based model, which is more complex and sensitive to the variations of the operating practices and a simpler switch model, which does not require training and is very easy to use. Both models provide good results and the user can interchangeably exploit them.

ACS Style

Ismael Matino; Stefano Dettori; Valentina Colla; Valentine Weber; Sahar Salame. Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves. Energy Procedia 2019, 158, 4043 -4048.

AMA Style

Ismael Matino, Stefano Dettori, Valentina Colla, Valentine Weber, Sahar Salame. Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves. Energy Procedia. 2019; 158 ():4043-4048.

Chicago/Turabian Style

Ismael Matino; Stefano Dettori; Valentina Colla; Valentine Weber; Sahar Salame. 2019. "Two innovative modelling approaches in order to forecast consumption of blast furnace gas by hot blast stoves." Energy Procedia 158, no. : 4043-4048.

Journal article
Published: 01 February 2019 in Energy Procedia
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The efficient use of resources is a relevant research topic for integrated steelworks. Process off-gases, such as the ones produced during blast furnace operation, are valid substitutes of natural gas, as they are sources of a considerable amount of energy. Currently they are recovered but sometimes part of such gas is flared due to non-optimal management of such resource. In order to exploit the off-gases produced in an integrated steelworks, the interactions between gas producers and users in the whole gas network need to be considered. The paper describes a model exploited by a Decision Support Tool that is under development within a European project. Such model forecasts the blast furnace gas amount and its heating power by obtaining an error between 3 and 7 % in a time horizon of 2 hours. The forecasted values of blast furnace gas allow a continuous optimal planning of the blast furnace gas usage according to its availability and to the needs in the steelworks, by avoiding losses of a valuable secondary resource and related emissions.

ACS Style

Ismael Matino; Stefano Dettori; Valentina Colla; Valentine Weber; Sahar Salame. Application of Echo State Neural Networks to forecast blast furnace gas production: pave the way to off-gas optimized management. Energy Procedia 2019, 158, 4037 -4042.

AMA Style

Ismael Matino, Stefano Dettori, Valentina Colla, Valentine Weber, Sahar Salame. Application of Echo State Neural Networks to forecast blast furnace gas production: pave the way to off-gas optimized management. Energy Procedia. 2019; 158 ():4037-4042.

Chicago/Turabian Style

Ismael Matino; Stefano Dettori; Valentina Colla; Valentine Weber; Sahar Salame. 2019. "Application of Echo State Neural Networks to forecast blast furnace gas production: pave the way to off-gas optimized management." Energy Procedia 158, no. : 4037-4042.

Journal article
Published: 01 January 2019 in Matériaux & Techniques
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The European steel industry is constantly promoting developments, which can increase efficiency and lower the environmental impact of the steel production processes. In particular, a strong focus refers to the minimization of the energy consumption. This paper presents part of the work of the research project entitled “Optimization of the management of the process gas network within the integrated steelworks” (GASNET), which aims at developing a decision support system supporting energy managers and other concerned technical personnel in the implementation of an optimized off-gases management and exploitation considering environmental and economic objectives. A mathematical model of the network as a capacitated digraph with costs on arcs is proposed and an optimization problem is formulated. The objective of the optimization consists in minimizing the wastes of process gases and maximizing the incomes. Several production constraints need to be accounted. In particular, different types of gases are mixing in the same network. The constraints that model the mixing make the problem computationally difficult: it is a non-convex quadratically constrained quadratic program (QCQP). Two formulations of the problem are presented: the first one is a minimum cost flow problem, which is a linear program and is thus computationally fast to solve, but suitable only for a single gas network. The second formulation is a quadratically constrained quadratic program, which is slower, but covers more general cases, such as the ones, which are characterized by the interaction among multiple gas networks. A user-friendly graphical interface has been developed and tests over existing plant networks are performed and analyzed.

ACS Style

Alessandro Maddaloni; Ruben Matino; Ismael Matino; Stefano Dettori; Antonella Zaccara; Valentina Colla. A quadratic programming model for the optimization of off-gas networks in integrated steelworks. Matériaux & Techniques 2019, 107, 502 .

AMA Style

Alessandro Maddaloni, Ruben Matino, Ismael Matino, Stefano Dettori, Antonella Zaccara, Valentina Colla. A quadratic programming model for the optimization of off-gas networks in integrated steelworks. Matériaux & Techniques. 2019; 107 (5):502.

Chicago/Turabian Style

Alessandro Maddaloni; Ruben Matino; Ismael Matino; Stefano Dettori; Antonella Zaccara; Valentina Colla. 2019. "A quadratic programming model for the optimization of off-gas networks in integrated steelworks." Matériaux & Techniques 107, no. 5: 502.

Research article
Published: 19 December 2018 in Frontiers of Chemical Science and Engineering
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For improving wastewater quality, one of the dare of steelworks is reducing cyanide in wastewater of gas washing treatment of blast furnaces. Costs of existing treatments, stringent environmental regulations and changeable composition of water from gas treatment, have led to study how available treatments can be modified and to examine new ones. Ozonation is one of cyanide treatments, tested within a European project. A process model was set up with Aspen Plus®, to assess operating conditions and wastewater distinctive characteristics and to demonstrate treatment robustness. Process was modeled by theoretical reactors, taking into account all more affecting reactions. A genetic algorithm was exploited to find kinetic parameters of these reactions. After validation, the model was used to analyse scenarios, by considering also real contexts. Pilot tests were extended, process knowledge was enhanced and suggestions were obtained. To promote cyanide removal with ozone, temperature and pH values were 30°C and 10, respectively. With an ozone (mg/h)/ water (L/h) ratio of 100 mg/L, batch mode ensure reaching cyanide regulation limit (0.2 mg/L) after maximum 4.5 h, if initial amount was less than 20 mg/L. Higher removal was obtained than in continuous mode due to constraints related to this last run. Higher wastewater contamination needed further time and more ozone.

ACS Style

Ismael Matino; Valentina Colla; Teresa A. Branca. Extension of pilot tests of cyanide elimination by ozone from blast furnace gas washing water through Aspen Plus® based model. Frontiers of Chemical Science and Engineering 2018, 12, 718 -730.

AMA Style

Ismael Matino, Valentina Colla, Teresa A. Branca. Extension of pilot tests of cyanide elimination by ozone from blast furnace gas washing water through Aspen Plus® based model. Frontiers of Chemical Science and Engineering. 2018; 12 (4):718-730.

Chicago/Turabian Style

Ismael Matino; Valentina Colla; Teresa A. Branca. 2018. "Extension of pilot tests of cyanide elimination by ozone from blast furnace gas washing water through Aspen Plus® based model." Frontiers of Chemical Science and Engineering 12, no. 4: 718-730.

Original paper
Published: 08 March 2018 in Waste and Biomass Valorization
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Electric steelworks can be considered virtuous industries, as they recover end-of-life products, the scrap, but margins of improvement exist for the sustainability of their production process. An increased internal reuse of by-products (e.g. slags) can provide economic advantages reducing raw material exploitation and avoiding disposal of wastes. Although slags reuse can imply significant advantages, their internal recycling is sometimes hampered because of their composition variability, which might lead to not perfectly known process behavior and effects on the final product. The paper faces this problem and presents a study related to the analyses of process behavior and performance in the case of slags’ reuse in an Italian steelworks. An ad-hoc developed general purpose-monitoring tool was exploited to simulate and evaluate the technical feasibility of two case studies related to the replacement of lime and dolime with Ladle Furnace slag with or without the partial recovery of Electric Arc Furnace slag for the production of two steel families. The effect on the production route, the environmental and energy impacts and steel composition were evaluated through advanced simulations. In particular, the simulations show that lime and dolime replacement is possible by recovering only Ladle Furnace slag, as a small increase of 3–4% of required electric energy is compensated by a reduction of non-metallic raw materials of about 14–16% without negative effects on steel composition and metallic yield. In addition, the exploited tool proved to be valid to monitor slags’ composition and to evaluate the suitability of slags’ reuse on a cast-by-cast basis.

ACS Style

Ismael Matino; Valentina Colla; Stefano Baragiola. Internal Slags Reuse in an Electric Steelmaking Route and Process Sustainability: Simulation of Different Scenarios Through the EIRES Monitoring Tool. Waste and Biomass Valorization 2018, 9, 2481 -2491.

AMA Style

Ismael Matino, Valentina Colla, Stefano Baragiola. Internal Slags Reuse in an Electric Steelmaking Route and Process Sustainability: Simulation of Different Scenarios Through the EIRES Monitoring Tool. Waste and Biomass Valorization. 2018; 9 (12):2481-2491.

Chicago/Turabian Style

Ismael Matino; Valentina Colla; Stefano Baragiola. 2018. "Internal Slags Reuse in an Electric Steelmaking Route and Process Sustainability: Simulation of Different Scenarios Through the EIRES Monitoring Tool." Waste and Biomass Valorization 9, no. 12: 2481-2491.

Journal article
Published: 01 December 2017 in Applied Energy
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ACS Style

Ismael Matino; Valentina Colla; Stefano Baragiola. Quantification of energy and environmental impacts in uncommon electric steelmaking scenarios to improve process sustainability. Applied Energy 2017, 207, 543 -552.

AMA Style

Ismael Matino, Valentina Colla, Stefano Baragiola. Quantification of energy and environmental impacts in uncommon electric steelmaking scenarios to improve process sustainability. Applied Energy. 2017; 207 ():543-552.

Chicago/Turabian Style

Ismael Matino; Valentina Colla; Stefano Baragiola. 2017. "Quantification of energy and environmental impacts in uncommon electric steelmaking scenarios to improve process sustainability." Applied Energy 207, no. : 543-552.