Results 171 to 180 of about 105,836 (304)

Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production. [PDF]

open access: yesSensors (Basel), 2021
Leon-Medina JX   +9 more
europepmc   +1 more source

Effective Equilibrium Reaction Zone Modeling of Hydrogen Plasma Smelting Reduction: A Scenario Analysis

open access: yessteel research international, EarlyView.
The study presents a dynamic zonal model for hydrogen plasma smelting reduction (HPSR) in direct current arc furnaces, predicting time‐dependent behavior of metal, slag, and off‐gas. The study considers various industrial scenarios to quantify energy demand, hydrogen utilization, degree of reduction, and other operational sensitivities.
Tristan K. Wickham   +4 more
wiley   +1 more source

Recycling of Mill Scale Into High‐Purity Iron Through Hydrogen‐Based Reduction

open access: yessteel research international, EarlyView.
Mill scale, an iron oxide–rich steel by‐product, is difficult to recycle due to impurities. This study evaluates hydrogen‐based direct reduction and hydrogen plasma smelting reduction as low‐carbon recycling routes. Impurity segregation limits reduction kinetics. Direct reduction achieves 92% conversion in 180 min, while plasma reduction reaches 91% in
Nathalia C. Verissimo   +8 more
wiley   +1 more source

Correction to: Impact of incorporating spent oil filtering earths into the formulation of alkali‑activated cements based on electric arc furnace slag. [PDF]

open access: yesEnviron Sci Pollut Res Int
Delgado-Plana P   +4 more
europepmc   +1 more source

Development and Validation of a Next‐Generation Mechanistic Model of the Electric Arc Furnace

open access: yessteel research international, EarlyView.
This study presents a next‐generation mechanistic model of the electric arc furnace (EAF). It describes equations for all crucial processes appearing during the steel‐recycling process in an EAF, i.e., thermal, mass, and chemical. The model was parameterized and validated using industrial EAF data.
Vito Logar, Igor Škrjanc
wiley   +1 more source

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