Dynamic process simulation · Operator decision support
SmartMelt
A configurable dynamic steelmaking platform that tracks material melting, bath and slag evolution, thermal state, reaction progress and off-gas behaviour as operating actions occur.
Purpose
A transparent process model for understanding, training and operating-strategy development
SmartMelt is designed to convert furnace inputs and operator actions into a continuously updated representation of the steel bath, slag and gas phases.
Rather than treating steelmaking as a single end-point calculation, the platform advances the process through small time steps. At every step, it reconciles material additions, elemental inventories, sensible and reaction heat, melting progress, phase masses, temperatures and gas generation. Plant-specific parameters can be configured for different furnace sizes, charge practices, power profiles, fuel and oxygen strategies, raw-material quality and target steel grades.
The platform is suitable for engineering studies, operator training, what-if analysis, model development and the preparation of a plant-specific Level-II or digital-twin implementation.
What the model follows
A live state estimate of the furnace
Metal bath
Liquid and solid metal inventories, temperature, carbon and alloying-element evolution, oxidation and recovery.
Slag phase
Slag weight, temperature, oxide composition, FeO generation and reduction, basicity and foaming-related indicators.
Energy system
Electrical or fuel energy, oxygen reactions, sensible heat of additions, melting demand, losses and net heat available.
Off-gas
Time-dependent gas volume and composition, including CO, CO₂, H₂, H₂O, N₂ and hydrocarbons where applicable.
Calculation architecture
From operator action to updated process state
The sequence is auditable so that plant engineers can trace why the model predicts a particular temperature, composition or phase inventory.
Read inputs
Charge, chemistry, flow rates, power, oxygen, fuel and operating events.
Resolve reactions
Oxidation, reduction, combustion, decarburization and slag-metal-gas reactions.
Balance elements
Track Fe, C, Si, Mn, P, S and selected alloying elements between phases.
Balance energy
Combine heat inputs, reaction heat, sensible heat, melting load and losses.
Update state
Advance metal, slag and gas masses, compositions, temperatures and progress.
Model scope
Typical configurable variables and outputs
| Area | Representative inputs | Representative outputs |
|---|---|---|
| Charge and additions | Scrap, DRI, hot metal, fluxes, alloys, carbon, ore and timing of additions | Solid/liquid inventory, melt fraction, yield, residual unmelted mass |
| Energy and utilities | Electrical power, burner/fuel profile, oxygen, air ingress, cooling and heat-loss factors | Bath and slag temperatures, net energy, specific energy, heat-loss estimate |
| Metal chemistry | Initial chemistry, material analyses, reaction and partition parameters | Time-dependent C, Si, Mn, P, S and selected alloying elements |
| Slag chemistry | Flux analyses, carry-over slag, refractory contribution and oxide formation | Slag mass, basicity, FeO/MnO and major-oxide composition |
| Gas system | Combustion assumptions, reaction stoichiometry, post-combustion and leakage | Gas flow, CO/CO₂/H₂/H₂O/N₂/CH₄ composition and chemical energy |
| Operating objectives | Target grade, tap temperature, process time and plant restrictions | Endpoint prediction, action recommendations, scenario comparison and KPI trends |
Engineering-use note: SmartMelt is a configurable decision-support and model-development platform. Plant deployment requires site-specific data mapping, parameter calibration, validation, cybersecurity review and integration with the plant automation hierarchy. It is not a substitute for certified safety interlocks or operating procedures.
Applications
How SmartMelt can be used
Operator training
Practice charge sequencing, power and oxygen strategies, flux additions and endpoint control without disturbing production.
Process development
Compare operating practices, charge mixes and energy strategies before committing to plant trials.
Plant troubleshooting
Reconstruct heats and identify likely causes of temperature, yield, chemistry, slag or energy deviations.
Level-II foundation
Develop the calculation engine and data definitions needed for plant-specific advisory or closed-loop systems.
Digital-twin studies
Combine the dynamic model with live data, state estimation, forecasting and optimization layers.
Techno-economics
Translate operating changes into energy, material, yield, productivity and emissions implications.
Build a plant-specific SmartMelt configuration
ExtractMet can adapt the calculation structure, input files, dashboards and operating logic to the furnace, raw materials, steel grades and data environment of a specific steel shop.