Work

Stochastic Multi-Objective Optimization of Scrap Blending for Product-Quality-Constrained Low-Carbon EAF Steelmaking

Year: 2026

Type: dataset

Abstract: This study presents a reproducible stochastic multi-objective framework for optimizing metallic charge blends in low-carbon electric arc furnace (EAF) steelmaking. The model integrates cost, carbon emissions, circularity, metallic yield, residual-element limits, multi-cycle recycling, CVaR-based risk control, Monte Carlo simulation, and Sobol–Jansen sensitivity analysis. Results demonstrate that product-specific stochastic optimization can substantially reduce off-specification risk while balancing scrap utilization, virgin-iron dilution, cost, and emissions. The framework supports comparative decision-making and can be adapted for plant-specific applications using locally calibrated data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22161999

Authors: M.Ameen, Rayan Basheer, Mohammed, Dilveen Waheed

Institutions: University of Duhok, University of Birmingham

Language: English

Topic: Iron and Steelmaking Processes

Subfield: Metals and Alloys

Field: Materials Science

Domain: Physical Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure, Climate action

Open Access status: green

OpenAlex ID: W7204659274

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