This page has only limited features, please log in for full access.
Valentina Coll got her master degree in Engineering at the University of Pisa in 1994 and Her Ph.D. in Industrial and Information Engineering at Scuola Superiore Sant'Anna (SSSA) in 1998. Valentina Colla was researcher at SSSA from 2000 until 2008 and since 2008 is Technical Research Manager. Valentina Colla is currently responsible of the Research Center ICT-COISP of the TeCIP Institute of SSSA. Her research activity deals with simulation, modelling and control of industrial processes and data processing via traditional and AI-based techniques. She holds a considerable experience in process and manufacturing industry. She participated to 63 EU funded projects (6 as project coordinator) and in many projects funded by companies. She is member of the European Steel Technology Platform (ESTEP).
Variable selection is an essential tool for gaining knowledge on a problem or phenomenon, by identifying the factors that shows the highest influence on it. It is also fundamental for the implementation of machine learning-based approaches to modelling and classification tasks, by improving performances and reducing computational cost. Furthermore, in many real-world applications, such as the ones in the medical field, a relevant number of variables are jointly observed, but the number of available observations is quite limited. In these cases, variable selection is clearly essential, but standard variable selection approaches become “unstable”, as the high correlation among different variables or their similar relevance with respect to the considered target lead to multiple solutions leading to similar performances. In machine-learning based classification, the stability of variable selection, namely its robustness with respect variations in the classifier training dataset, is as important as the performance of the classifier itself. The paper presents an automatic procedure for variable selection in classification tasks, which ensures excellent stability of the selection and does not require any a priori information on the available data.
Silvia Cateni; Valentina Colla; Marco Vannucci. A Combined Approach for Enhancing the Stability of the Variable Selection Stage in Binary Classification Tasks. Computer Algebra in Scientific Computing 2021, 248 -259.
AMA StyleSilvia Cateni, Valentina Colla, Marco Vannucci. A Combined Approach for Enhancing the Stability of the Variable Selection Stage in Binary Classification Tasks. Computer Algebra in Scientific Computing. 2021; ():248-259.
Chicago/Turabian StyleSilvia Cateni; Valentina Colla; Marco Vannucci. 2021. "A Combined Approach for Enhancing the Stability of the Variable Selection Stage in Binary Classification Tasks." Computer Algebra in Scientific Computing , no. : 248-259.
Over the last few decades, process industries have invested increasing efforts in developing technical and operating solutions related to industrial symbiosis and energy efficiency in both production processes and auxiliary services. In particular, new technologies that enable industrial symbiosis, such as novel treatment processes for byproduct extraction and valorization, water purification, and energy transformation, were implemented in different sectors. This work analyses recent relevant results in the implementation of industrial symbiosis and energy efficiency solutions within process industries across Europe, based on the transactions of energy and material flows. Current developments, based on the circular economy’s transformation levers and related achieved results, were taken into account by considering the achieved results coming from the literature, EU-funded projects, programmes, and initiatives on the implementation of technical solutions and practices related to industrial symbiosis and energy efficiency. In addition, the most relevant challenges deriving from the implementations of industrial symbiosis and energy efficiency were analysed. A comprehensive picture of the sectors involved in achieving more proactive cross-sectorial cooperation and integration was provided, as well as an analysis of the main drivers and barriers for IS and EE implementation in future scenarios for European process industries.
Teresa Annunziata Branca; Barbara Fornai; Valentina Colla; Maria Ilaria Pistelli; Eros Luciano Faraci; Filippo Cirilli; Antonius Johannes Schröder. Industrial Symbiosis and Energy Efficiency in European Process Industries: A Review. Sustainability 2021, 13, 9159 .
AMA StyleTeresa Annunziata Branca, Barbara Fornai, Valentina Colla, Maria Ilaria Pistelli, Eros Luciano Faraci, Filippo Cirilli, Antonius Johannes Schröder. Industrial Symbiosis and Energy Efficiency in European Process Industries: A Review. Sustainability. 2021; 13 (16):9159.
Chicago/Turabian StyleTeresa Annunziata Branca; Barbara Fornai; Valentina Colla; Maria Ilaria Pistelli; Eros Luciano Faraci; Filippo Cirilli; Antonius Johannes Schröder. 2021. "Industrial Symbiosis and Energy Efficiency in European Process Industries: A Review." Sustainability 13, no. 16: 9159.
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.
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 StyleJohannes 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 StyleJohannes 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.
The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.
Stefano Dettori; Alessandro Maddaloni; Filippo Galli; Valentina Colla; Federico Bucciarelli; Damaso Checcacci; Annamaria Signorini. Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control. Energies 2021, 14, 3998 .
AMA StyleStefano Dettori, Alessandro Maddaloni, Filippo Galli, Valentina Colla, Federico Bucciarelli, Damaso Checcacci, Annamaria Signorini. Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control. Energies. 2021; 14 (13):3998.
Chicago/Turabian StyleStefano Dettori; Alessandro Maddaloni; Filippo Galli; Valentina Colla; Federico Bucciarelli; Damaso Checcacci; Annamaria Signorini. 2021. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control." Energies 14, no. 13: 3998.
The paper proposes an approach to the design of the chemical composition of steel, which is based on neural networks and genetic algorithms and aims at achieving a desired hardenability behavior possibly matching other constraints related to the steel production. Hardenability is a mechanical feature of steel, which is extremely relevant for a wide range of steel applications and refers to the steel capability to improve its hardness following a heat treatment. In the proposed approach, a neural-network-based predictor of the so-called Jominy hardenability profile is exploited, and an optimization problem is formulated, where the optimization function allows taking into account both the desired accuracy in meeting the target Jominy profile and other constraint. The optimization is performed through genetic algorithms. Numerical results are presented and discussed, showing the efficiency of the proposed approach together with its flexibility and easy customization with respect to the user demands and production objectives.
Marco Vannucci; Valentina Colla. Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms. Neural Computing and Applications 2021, 1 .
AMA StyleMarco Vannucci, Valentina Colla. Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms. Neural Computing and Applications. 2021; ():1.
Chicago/Turabian StyleMarco Vannucci; Valentina Colla. 2021. "Automatic steel grades design for Jominy profile achievement through neural networks and genetic algorithms." Neural Computing and Applications , no. : 1.
In recent years, the European Steel Industry, in particular flat steel production, is facing an increasingly competitive market situation. The product price is determined by competition, and the only way to increase profit is to reduce production and commercial costs. One method to increase production yield is to create proper scheduling for the components on the available machines, so that an order is timely completed, optimizing resource exploitation and minimizing delays. The optimization of production using efficient scheduling strategies has received ever increasing attention over time and is one of the most investigated optimization problems. The paper presents three approaches for improving flexibility of production scheduling in flat steel facilities. Each method has different scopes and modelling aspects: an auction-based multi-agent system is used to deal with production uncertainties, a multi-objective mixed-integer linear programming-based approach is applied for global optimal scheduling of resources under steady conditions, and a continuous flow model approach provides long-term production scheduling. Simulation results show the goodness of each method and their suitability to different production conditions, by highlighting their advantages and limitations.
Vincenzo Iannino; Valentina Colla; Alessandro Maddaloni; Jens Brandenburger; Ahmad Rajabi; Andreas Wolff; Joaquin Ordieres; Miguel Gutierrez; Erwin Sirovnik; Dirk Mueller; Christoph Schirm. Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-Based Approaches: Challenges and Perspectives. Collaboration in a Hyperconnected World 2021, 619 -632.
AMA StyleVincenzo Iannino, Valentina Colla, Alessandro Maddaloni, Jens Brandenburger, Ahmad Rajabi, Andreas Wolff, Joaquin Ordieres, Miguel Gutierrez, Erwin Sirovnik, Dirk Mueller, Christoph Schirm. Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-Based Approaches: Challenges and Perspectives. Collaboration in a Hyperconnected World. 2021; ():619-632.
Chicago/Turabian StyleVincenzo Iannino; Valentina Colla; Alessandro Maddaloni; Jens Brandenburger; Ahmad Rajabi; Andreas Wolff; Joaquin Ordieres; Miguel Gutierrez; Erwin Sirovnik; Dirk Mueller; Christoph Schirm. 2021. "Improving the Flexibility of Production Scheduling in Flat Steel Production Through Standard and AI-Based Approaches: Challenges and Perspectives." Collaboration in a Hyperconnected World , no. : 619-632.
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.
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 StyleMichael 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 StyleMichael 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.
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.
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 StyleStefano 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 StyleStefano 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.
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.
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 StyleStefano 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 StyleStefano 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.
Within the implementation of the Industry 4.0 paradigm in the steel sector, robots can play a relevant role in improving health and safety conditions at the workplace, by overtaking cumbersome, repetitive and risky operations. However, the implementation of robotics solutions in this particular sector is hampered by harsh operating conditions and by particular features of many procedures, which require a combination of force and sensitivity. Human–robot cooperation is a viable solution to overcome existing barriers, by synergistically combining human and robot abilities in the sense of a human-centered Industry 5.0. In this sense, robotics solution should be designed in a way to integrate and meet the end-users’ demands in a common development process for successfully implementation and widely acceptance. The paper presents the outcomes of the field evaluation of a robotic workstation, which was designed for a complex maintenance operation that is daily performed in the steel shop. The system derives from a co-creation process, where workers were involved since the beginning in the design process, according to the paradigm of social innovation combining technological and social development. Therefore, the evaluation aimed at assessing both system reliability and end-users’ satisfaction. The results show that the human-centered robotic workstations are successful in reducing cumbersome operations and improving workers’ health and safety conditions, and that this fact is clearly perceived by system users and developers.
Valentina Colla; Ruben Matino; Antonius Schröder; Mauro Schivalocchi; Lea Romaniello. Human-Centered Robotic Development in the Steel Shop: Improving Health, Safety and Digital Skills at the Workplace. Metals 2021, 11, 647 .
AMA StyleValentina Colla, Ruben Matino, Antonius Schröder, Mauro Schivalocchi, Lea Romaniello. Human-Centered Robotic Development in the Steel Shop: Improving Health, Safety and Digital Skills at the Workplace. Metals. 2021; 11 (4):647.
Chicago/Turabian StyleValentina Colla; Ruben Matino; Antonius Schröder; Mauro Schivalocchi; Lea Romaniello. 2021. "Human-Centered Robotic Development in the Steel Shop: Improving Health, Safety and Digital Skills at the Workplace." Metals 11, no. 4: 647.
Steel manufacturing involves a series of dynamic processes that require an efficient and effective management of the plant resources. The coordination and the allocation of the resources is an important aspect for ensuring a continuous production flow as well as a good quality of the final products, especially when unexpected events can compromise the overall system performance. This paper presents an agent-based protocol for dynamic resource allocation in order to establish collaboration among agents within steel production processes. The proposed protocol is based on the brokering mechanism and is designed in order to solve the problem of the concurrency, which arises when several agents are interested in using the same resources, and to handle dynamic changes, such as unexpected events that can affect the resource allocation process. Experimental results show how the designed protocol allows the agents coordination by guaranteeing the use of the resources and the correct flow of the production.
Vincenzo Iannino; Claudio Mocci; Valentina Colla. A Brokering-Based Interaction Protocol for Dynamic Resource Allocation in Steel Production Processes. Advances in Intelligent Systems and Computing 2021, 119 -129.
AMA StyleVincenzo Iannino, Claudio Mocci, Valentina Colla. A Brokering-Based Interaction Protocol for Dynamic Resource Allocation in Steel Production Processes. Advances in Intelligent Systems and Computing. 2021; ():119-129.
Chicago/Turabian StyleVincenzo Iannino; Claudio Mocci; Valentina Colla. 2021. "A Brokering-Based Interaction Protocol for Dynamic Resource Allocation in Steel Production Processes." Advances in Intelligent Systems and Computing , no. : 119-129.
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.
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 StyleIsmael 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 StyleIsmael 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.
In today’s steel market, the level of intimacy with customer can be a key factor for European steelmakers to achieve a competitive advantage with respect to extra-European companies. From the steel customers point of view, the assurance of the fulfillment of individual products requirements is an essential point that can affect the customer-supplier relationship and, as a consequence, the selection of a specific supplier. In this context, the reliability of product quality information provided to customers plays a fundamental role. In this work, several Artificial Intelligence and Machine Learning based techniques are applied to the production of product quality information and to the assessment of their reliability in order to provide customers with trustworthy data. A set of fully automated procedures exploiting, among the others, Neural Networks and Fuzzy Inference Systems, is set up to detect potentially erroneous information within the data gathered from multiple and heterogeneous sensor systems, to produce quality data for products certification as well as to grant the integrity of exchanged quality indicators.
Marco Vannucci; Valentina Colla; Antonio Ritacco; Antonella Vignali. AI and ML Techniques for Generation and Assessment of Products Properties Data. Advances in Intelligent Systems and Computing 2021, 67 -77.
AMA StyleMarco Vannucci, Valentina Colla, Antonio Ritacco, Antonella Vignali. AI and ML Techniques for Generation and Assessment of Products Properties Data. Advances in Intelligent Systems and Computing. 2021; ():67-77.
Chicago/Turabian StyleMarco Vannucci; Valentina Colla; Antonio Ritacco; Antonella Vignali. 2021. "AI and ML Techniques for Generation and Assessment of Products Properties Data." Advances in Intelligent Systems and Computing , no. : 67-77.
In the production of cars, steel is the preferred material for the body because of its high strength, good formability and excellent recycling opportunities. In order to have a smooth production when pressing automotive body parts, the mechanical forming properties of the steel strip need to be uniform and consistent. These properties are governed by the microstructure of the steel. The final microstructure is largely determined by both the thermal process conditions during the continuous annealing process and the incoming state of the material. This paper describes the attempt to monitor the incoming state using the IMPOC device prior to the annealing process. Using advanced analytics methods, these data have been searched for correlations with process data and data from a second IMPOC device at the end of the production line.
Frenk Van Den Berg; Danique Fintelman; Haibing Yang; Claudio Mocci; Marco Vannucci; Valentina Colla. The Use of Advanced Data Analytics to Monitor Process-Induced Changes to the Microstructure and Mechanical Properties in Flat Steel Strip. Advances in Intelligent Systems and Computing 2021, 78 -91.
AMA StyleFrenk Van Den Berg, Danique Fintelman, Haibing Yang, Claudio Mocci, Marco Vannucci, Valentina Colla. The Use of Advanced Data Analytics to Monitor Process-Induced Changes to the Microstructure and Mechanical Properties in Flat Steel Strip. Advances in Intelligent Systems and Computing. 2021; ():78-91.
Chicago/Turabian StyleFrenk Van Den Berg; Danique Fintelman; Haibing Yang; Claudio Mocci; Marco Vannucci; Valentina Colla. 2021. "The Use of Advanced Data Analytics to Monitor Process-Induced Changes to the Microstructure and Mechanical Properties in Flat Steel Strip." Advances in Intelligent Systems and Computing , no. : 78-91.
Progressive digitalization is changing the game of many industrial sectors. Focusing on product quality the main profitability driver of this so-called Industry 4.0 will be the horizontal integration of information over the complete supply chain. Therefore, the European RFCS project “Quality 4.0” aims in developing an adaptive platform, which releases decisions on product quality and provides tailored information of high reliability that can be individually exchanged with customers. In this context Machine Learning will be used to detect outliers in the quality data. This paper discusses the intermediate project results and the concepts developed so far for this horizontal integration of quality information.
Jens Brandenburger; Christoph Schirm; Josef Melcher; Edgar Hancke; Marco Vannucci; Valentina Colla; Silvia Cateni; Rami Sellami; Sébastien Dupont; Annick Majchrowski; Asier Arteaga. Quality 4.0 - Transparent Product Quality Supervision in the Age of Industry 4.0. Advances in Intelligent Systems and Computing 2021, 54 -66.
AMA StyleJens Brandenburger, Christoph Schirm, Josef Melcher, Edgar Hancke, Marco Vannucci, Valentina Colla, Silvia Cateni, Rami Sellami, Sébastien Dupont, Annick Majchrowski, Asier Arteaga. Quality 4.0 - Transparent Product Quality Supervision in the Age of Industry 4.0. Advances in Intelligent Systems and Computing. 2021; ():54-66.
Chicago/Turabian StyleJens Brandenburger; Christoph Schirm; Josef Melcher; Edgar Hancke; Marco Vannucci; Valentina Colla; Silvia Cateni; Rami Sellami; Sébastien Dupont; Annick Majchrowski; Asier Arteaga. 2021. "Quality 4.0 - Transparent Product Quality Supervision in the Age of Industry 4.0." Advances in Intelligent Systems and Computing , no. : 54-66.
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.
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 StyleAntonella 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 StyleAntonella 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.
The paper presents a machine learning-based system aimed at improving the homogeneity of tensile properties of steel strips for automotive applications over their strip length in the annealing and hot dip galvanizing lines. A novel modular approach is proposed exploiting process and product data and combining smart data pre-processing and cleansing algorithms, an ensemble of neural networks targeted to specific product classes and an ad-hoc developed iterative procedure for identifying the variability ranges of the most relevant process variables. A decision support concept is implemented through a software tool, which facilitates exploitation by plant managers and operators. The system has been tested on site. The results show its effectiveness in improving the control of the thermal evolution of the strip with respect to the standard operating practice.
Valentina Colla; Silvia Cateni; Alessandro Maddaloni; Antonella Vignali. A Modular Machine-Learning-Based Approach to Improve Tensile Properties Uniformity Along Hot Dip Galvanized Steel Strips for Automotive Applications. Metals 2020, 10, 923 .
AMA StyleValentina Colla, Silvia Cateni, Alessandro Maddaloni, Antonella Vignali. A Modular Machine-Learning-Based Approach to Improve Tensile Properties Uniformity Along Hot Dip Galvanized Steel Strips for Automotive Applications. Metals. 2020; 10 (7):923.
Chicago/Turabian StyleValentina Colla; Silvia Cateni; Alessandro Maddaloni; Antonella Vignali. 2020. "A Modular Machine-Learning-Based Approach to Improve Tensile Properties Uniformity Along Hot Dip Galvanized Steel Strips for Automotive Applications." Metals 10, no. 7: 923.
Process manufacturing industries are complex and dynamic systems composed of several processes, subject to many operations and unexpected events that can compromise overall system performance. Therefore, the use of technologies and methods that can transform traditional process industries into smart factories is necessary. In this paper, a smart industrial process based on intelligent software agents is presented with the aim of providing a technological solution to the specific needs of the process industry. An event-driven agent-based simulation model composed of eight reactive agents was designed to simulate and control the operations of a generic industrial process. The agents were modeled using the actor approach and the communication mechanism was based on the publish–subscribe paradigm. The overall system was tested in different scenarios, such as faults, changing operating conditions and off-spec productions. The proposed agent-based simulation model proved to be very efficient in promptly reacting to different dynamic scenarios and in suitably handling different situations. Furthermore, the usability and the practicality of the proposed software tool facilitate its deployment and customization to different production chains, and provide a practical example of the use of multi-agent systems and artificial intelligence in the context of industry 4.0.
Vincenzo Iannino; Claudio Mocci; Marco Vannocci; Valentina Colla; Andrea Caputo; Francesco Ferraris. An Event-Driven Agent-Based Simulation Model for Industrial Processes. Applied Sciences 2020, 10, 4343 .
AMA StyleVincenzo Iannino, Claudio Mocci, Marco Vannocci, Valentina Colla, Andrea Caputo, Francesco Ferraris. An Event-Driven Agent-Based Simulation Model for Industrial Processes. Applied Sciences. 2020; 10 (12):4343.
Chicago/Turabian StyleVincenzo Iannino; Claudio Mocci; Marco Vannocci; Valentina Colla; Andrea Caputo; Francesco Ferraris. 2020. "An Event-Driven Agent-Based Simulation Model for Industrial Processes." Applied Sciences 10, no. 12: 4343.
The presence of hydrogen severely affects the mechanical properties of semi‐manufactured and final steel products. Such presence is affected by different factors which show up throughout the whole manufacturing process and varies during the different stages according to both product features and various process parameters. This article presents an experimental methodology to assess the critical hydrogen concentration in as‐cast and hot‐rolled billets in medium carbon steels, which allows highlighting their robustness to hydrogen embrittlement. Such approach is based on a combination of hydrogen‐induced cracking and slow strain rate tensile tests, which are conducted on both as‐cast and hot‐rolled billets samples. The experiments highlight that the hydrogen presence is more critical on the as‐cast semi‐products than on the hot‐rolled billets, which show a higher robustness, as they can tolerate higher values of hydrogen content.
Valentina Colla; Renzo Valentini. Assessment of Critical Hydrogen Concentration in As‐Cast and Hot‐Rolled Billets in Medium Carbon Steels. steel research international 2020, 91, 1 .
AMA StyleValentina Colla, Renzo Valentini. Assessment of Critical Hydrogen Concentration in As‐Cast and Hot‐Rolled Billets in Medium Carbon Steels. steel research international. 2020; 91 (9):1.
Chicago/Turabian StyleValentina Colla; Renzo Valentini. 2020. "Assessment of Critical Hydrogen Concentration in As‐Cast and Hot‐Rolled Billets in Medium Carbon Steels." steel research international 91, no. 9: 1.
Silvia Cateni; Valentina Colla; Marco Vannucci. A Genetic Algorithm-Based Approach for Selecting Input Variables and Setting Relevant Network Parameters of a SOM-Based Classifier. International journal of simulation: systems, science & technology 2020, 1 .
AMA StyleSilvia Cateni, Valentina Colla, Marco Vannucci. A Genetic Algorithm-Based Approach for Selecting Input Variables and Setting Relevant Network Parameters of a SOM-Based Classifier. International journal of simulation: systems, science & technology. 2020; ():1.
Chicago/Turabian StyleSilvia Cateni; Valentina Colla; Marco Vannucci. 2020. "A Genetic Algorithm-Based Approach for Selecting Input Variables and Setting Relevant Network Parameters of a SOM-Based Classifier." International journal of simulation: systems, science & technology , no. : 1.