1,720,990 research outputs found
Editorial for the Special Issue: Overview, state of the art, recent developments and future trends regarding Hydrogen route for a green steel making process
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Simulation of Direct Reduction Processes to be included in a process chain multipurpose simulation toolkit
No full textFlowsheet Model and Simulation of Produced Slag in Electric Steelmaking to Improve Resource Management and Circular Production
Full text linkThe steel industry is one of the most energy-intensive sectors, as it requires a great amount of resources and produces a considerable quantity of by-products, with not negligible environmental impact. Therefore, the main challenge of steelworks consists in improving sustainability and reducing carbon footprint of the production process, by ensuring the required quality of final products. In this context, the reuse and recycling of by-products can play a key role in preventing their landfilling and waste of valuable products, reducing the exploitation of primary raw materials, decreasing CO2 emissions, and supporting the implementation of the Circular Economy concept. In particular, one of the main by-products is slag, which can be used as a potentially valuable source of secondary raw materials, leading to a substantial reduction of natural resources usage and related costs. This paper concerns part of the work developed inside the EU-funded project entitled “Optimising slag reuse and recycling in electric steelmaking at optimum metallurgical performance through on-line characterization devices and intelligent decision support system – iSlag”. The main focus of this project is the valorisation of slags produced in the electric steelmaking route, by defining good practices, investigating new recycling paths, and promoting industrial symbiosis solutions. In this paper, the adaptation and the improvement of a previously developed Aspen Plus® simulation model are presented to obtain an accurate prediction of slag features. In particular, the model estimates amount and composition of slags produced in the primary and the secondary steelmaking processes, and it allows simulating different case scenarios including usual and unusual conditions, for instance, process operating conditions, raw materials compositions, steel families to be produced. In addition to slag features, product compositions and environmental and energy impacts can be monitored with the model
A Deep Learning-based approach for forecasting off-gas production and consumption in the blast furnace
No full textThis 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
Simulation of 6lag &omposition 5esulting)rom Electric Arc Furnace)ed:ith DRI or HBI
No full textThe transformation towards new technologies or the modification of existing processes for green steelmaking has to ensure, among other things, the implementation of circular economy concepts, e.g. slag recycling. Understanding how modifications affect slag characteristics is crucial for end applications. In the European project InSGeP, different models were developed to address this demand. This paper focuses on the use of an Electric Arc Furnace flowsheet model, simulating process behavior, steel and slag compositions for a feed containing Direct Reduced Iron or Hot Briquetted Iron. The model was tuned and validated with industrial and technology provider’s data and is being used for scenario analyses
A Deep Learning-Based Approach to the Estimation of Jominy Profile of Medium-Carbon Quench Hardenable Steels
Full text linkThe possibility to estimate the Jominy profile of steel based on its chemical composition is of utmost importance and high practical relevance for industries, at enables a preliminary assessment of the suitability of a specific steel grade to a particular application or to the requirements of a customer, by saving time and resources as the Jominy end-quench test is costly and time consuming. More importantly, an estimator can be used in steel grade design, by supporting the investigation of the most suitable chemistry to meet some given specifications. The paper proposes a novel approach to estimate the hardenability profile of medium Carbon quench hardenable steels, which exploits the potential of deep learning to correlate the steel metallurgy to the entire shape of the curve rather than to its single points, by thus being adaptable to a wide range of steel grades while providing very accurate estimates. Moreover, the proposed approach is suitable implement a transfer learning paradigm, as it can exploit the knowledge acquired by training on a specific dataset to adapt the model to different steel grades for which less data or data holding different features are available
Guiding the Transition Toward H2-DRI-Based Steelworks Through a Related Simulation Toolkit
No full textDirect reduction is a promising process to reduce emissions in steelmaking, especially if the reducing gas contains significant amount of hydrogen. However, the introduction of the related plant into existing integrated steelworks may lead to not completely known effects on production and energy management. In the European MaxH2DR project, a multipurpose simulation toolkit was developed to help industrial managers in the transition from current configurations to H2-DRI based steelworks. Models were developed to consider production and energy management aspects. The contribution describes the toolkit and the models of gas and energy management area and of DRI production processes
Exploring the use of alternative non-fossil carbon sources in electric steelworks through dedicated flowsheet model|Esplorare l’uso di fonti alternative e non fossili di carbonio nelle acciaierie elettriche attraverso un modello flowsheet dedicato
No full textLe acciaierie elettriche svolgono un ruolo fondamentale nella transizione dell’industria siderurgica verso la decarbonizzazione. Poiché i rottami vengono utilizzati come materia prima principale, esse implementano intrinsecamente il concetto di economia circolare. Tuttavia, si stanno investendo ulteriori sforzi di ricerca per adattare il processo di produzione dell’acciaio al forno elettrico ad arco alle nuove sfide legate ai processi “C-lean”. I potenziali miglioramenti riguardano il carbonio e l’energia: il carbonio fossile deve essere sostituito da materiali carboniosi di origine biologica o comunque alternativa. Gli effetti dell'introduzione di questi materiali nel percorso standard dell'EAF non sono completamente noti e sono necessarie indagini. La ricerca su questi argomenti fa parte di alcune delle attività previste nel progetto finanziato dall’UE dal titolo “Gradual Integration of REnewable carbon and alternative non-carbon Energy sources and modular HEATIing technologies in EAF for progressive CO2 decrease – GreenHeatEAF”. Il progetto si basa sull’applicazione parallela e complementare di test dimostrativi e pilota industriali, simulazioni digitali e strategie di monitoraggio e controllo. Uno degli strumenti applicati in GreenHeatEAF è un modello flowsheet del processo di produzione dell'acciaio al forno elettrico ad arco, che è stato adattato per gestire l'uso e l'iniezione di nuove fonti di carbonio sfruttando la letteratura e dati industriali reali. Prime simulazioni sono state effettuate, che riguardano analisi di sensitività sugli effetti della variazione del contenuto di C ed S nel materiale carbonioso, e analisi di scenario sull’uso di diverse fonti di C alternative. Diversi indicatori chiave di prestazione sono calcolati per confrontare i risultati delle simulazioni. A parità di materiale carbonioso impiegato, i parametri principali di processo e prodotto appaiono influenzati quasi linearmente dalla variazione del contenuto di C e S nel materiale carbonioso. D'altro canto, le diverse fonti di carbonio determinano comportamenti diversi del processo EAF e dei parametri del prodotto senza una chiara correlazione. Ulteriori simulazioni sono in corso per generalizzare i risultati preliminari ottenuti
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