Online Model Demo Hub

SMARTMELT — Induction Furnace Steelmaking

An operator-oriented dynamic control model for induction-furnace steelmaking, designed to track melting progress, bath temperature, steel chemistry, slag development and material additions during the heat.

• Charge and melting-state monitoring
• Dynamic metal, slag and thermal balances
• Operator guidance for additions and tapping
• Scenario analysis for plant-specific heats

BOF Integrated Control Model

A process-control model for basic oxygen steelmaking that links charge conditions, oxygen-blowing practice, slag formation, decarburization and endpoint prediction.

• Hot-metal, scrap and flux input definition
• Blow-progress and reaction tracking
• Endpoint carbon and temperature prediction
• Slag condition and process-control support

EAF Integrated Control Model

A dynamic electric-arc-furnace model covering scrap and DRI melting, electrical and chemical energy, oxygen and carbon practice, slag behaviour and tapping conditions.

• Charge-mix and melting-progress analysis
• Electrical and chemical energy balance
• Slag foaming and oxidation control
• Tap-time, yield and temperature guidance

Ferrochrome Smelter Control Model

A submerged-arc-furnace decision-support model for ferrochrome production, linking ore, reductant, flux, electrical conditions, alloy recovery and slag chemistry.

• Charge-mix and reductant evaluation
• Electrical and thermal operating indicators
• Chromium recovery and alloy-yield assessment
• Slag composition and tapping support

AOD Stainless-Steelmaking Control Model

A control model for argon–oxygen decarburization of stainless steel, focused on selective carbon removal, chromium retention, gas practice, thermal evolution and refining endpoint.

• Oxygen–argon blowing strategy
• Carbon removal and chromium-loss assessment
• Bath temperature and reaction evolution
• Endpoint and alloy-adjustment guidance

Rotary-Kiln DRI Control Model

A rotary-kiln direct-reduction model for tracking material movement, reduction progress, kiln thermal state, fuel and air practice, product metallization and operating stability.

• Ore, coal, dolomite and air inputs
• Reduction and kiln-temperature progression
• Product metallization and carbon indicators
• Fuel-use, accretion-risk and productivity support

MIDREX Process DRI Making

A gas-based shaft-furnace process-control family combining first-principles thermochemistry, gas/solid balances, reduction and heat-transfer logic with plant-data calibration for static target setting and spatial process-state analysis.

• DRI production, metallization and carbon estimation
• Reducing-gas demand, utilization and exit-gas composition
• Heat-balance, bustle-gas and hydrogen-transition studies
• Static/1D target calculations and 2D spatial process analysis

PCI–RAFT Calculation Model

A blast-furnace raceway model for estimating raceway adiabatic flame temperature and evaluating pulverized-coal injection, oxygen enrichment, blast temperature and moisture effects.

• RAFT estimation under changing blast conditions
• PCI and oxygen-enrichment sensitivity
• Tuyere-zone heat and gas calculations
• Operating-window comparison for stable furnace practice

Blast Furnace First-Principles Simulation

A first-principles blast-furnace process simulator for integrated burden, gas, thermal and reaction analysis across the furnace, supporting fuel-rate, productivity and hot-metal studies.

• Burden, coke, PCI and blast inputs
• Gas–solid reaction and heat-balance analysis
• Top-gas, fuel-rate and productivity prediction
• Hot-metal chemistry and thermal-state assessment

Blast Furnace Machine-Learning Simulation

A data-driven blast-furnace model that uses machine-learning relationships to study process behaviour, predict key performance indicators and support rapid operational scenario evaluation.

• Plant-data-based prediction
• Rapid what-if and sensitivity studies
• Performance and stability indicators
• Complementary use with first-principles models

Steel Plant Carbon Footprint, Carbon Credit & Decarbonization Calculation Model

A plant-level carbon-accounting and scenario-analysis model for estimating greenhouse-gas emissions across iron and steel production, identifying major emission sources and evaluating practical decarbonization pathways.

• Plant and process carbon-footprint calculation in CO₂-equivalent terms
• Source-wise emissions breakdown for fuels, electricity, reductants and process reactions
• Comparison of decarbonization scenarios, energy substitutions and operating improvements
• Indicative carbon-credit and avoided-emission assessment subject to applicable methodology, project boundary and regulatory requirements

Hall–Héroult Aluminium Extraction Digital Twin

A hybrid Level-2 and digital-twin model for primary aluminium reduction cells, linking electrical, electrochemical, thermal and feeding behaviour with state estimation and predictive operator guidance.

• Cell voltage/current, alumina feed, tapping and anode-event context
• Soft sensing of dissolved alumina, ACD/resistance and thermal condition
• Current-efficiency, specific-energy and anode-effect-risk assessment
• What-if simulation, feed/ACD guidance and pot-performance optimization

Continuous Casting Process Digital Twin

An end-to-end continuous-casting digital twin for tracking thermal state, mould and secondary-cooling behaviour, shell growth, metallurgical length and quality-risk indicators across the caster.

• Ladle/tundish superheat and casting-speed context
• Mould heat transfer, shell thickness and solidification progression
• Secondary-cooling and metallurgical-length assessment
• Scenario analysis for breakout risk, quality stability and productivity

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Iron Ore Sintering Process Digital Twin

A hybrid physics-and-data digital twin for sinter-bed preparation, thermal-front progression, burn-through-point prediction, permeability, productivity, fuel use and sinter-quality guidance.

• Blend, return fines, coke breeze, moisture and bed-state inputs
• Ignition and moving thermal-front / BTP prediction
• Permeability, strand speed, fuel and thermal-state assessment
• Operator guidance for productivity, quality consistency and energy efficiency

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