Novel Insights into Low-Carbon Metallurgical Process Simulation and Optimization
A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 799
Special Issue Editors
Interests: characterization of extreme service behavior of materials/components; low-carbon spray metallurgy technology; electric arc furnace steelmaking; converter steelmaking; numerical simulation
Special Issues, Collections and Topics in MDPI journals
Interests: system optimization of metallurgical manufacturing processes; data analysis and intelligent control for metallurgical manufacturing processes; carbon footprint analysis and diagnosis of metallurgical processes
Interests: electric arc furnace steelmaking; metallurgical process optimization; low-carbon technologies; life cycle assessment; clean energy utilization; techno-economic analysis; machine learning; data mining; numerical simulation
Special Issue Information
Dear Colleagues,
The metallurgical industry stands at a critical juncture in its transition toward carbon neutrality. Responsible for a significant share of global industrial CO2 emissions, the sector is actively exploring breakthrough technologies including hydrogen-based direct reduction, renewable energy-powered electrolysis, and novel smelting routes . However, the complexity of high-temperature multiphase reactions, coupled with the high cost and technical risk of physical trials, makes process optimization particularly challenging. Computational modeling and simulation have emerged as indispensable tools to accelerate this low-carbon transition. From particle-scale discrete element method (DEM) and computational fluid dynamics (CFD) to system-level process integration, numerical approaches enable researchers to visualize internal furnace phenomena, optimize operating parameters, and quantify energy efficiency and emissions reduction potential before industrial implementation. Recent advances in multi-physics coupling, digital twin technologies, and AI-accelerated surrogate models are opening new frontiers for process intensification.
This Special Issue aims to showcase novel insights into the simulation and optimization of low-carbon metallurgical processes. We invite contributions on hydrogen-based ironmaking, electric smelting furnaces, renewable energy integration, process-scale life cycle assessment, and innovative numerical methods. By bringing together cutting-edge research from academia and industry, this issue seeks to advance the scientific understanding and practical deployment of sustainable metallurgical technologies.
Dr. Fuhai Liu
Dr. Kai Feng
Dr. Hang Hu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- low-carbon metallurgy
- process simulation
- numerical optimization
- hydrogen metallurgy
- electric smelting
- computational fluid dynamics
- discrete element method
- multi-physics coupling
- artificial intelligence
- digital twin
- life cycle assessment
- carbon emission
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