The direct reduced iron process — whether MIDREX, HYL/Energiron, or hydrogen-based shaft furnace — converts iron ore pellets or lump ore into highly metallized DRI or hot-briquetted iron (HBI) for electric arc furnace steelmaking. Metallization, defined as the percentage of total iron reduced from oxide to metallic iron, is the single most important quality parameter: every 1% decrease in DRI metallization increases EAF electrical energy consumption by 4 to 6 kWh per ton of liquid steel, and carbon content below the 1.5% target range forces additional charge carbon additions and foaming slag adjustments at the EAF. Conventional DRI plant control relies on lab analysis of discharge samples every 2 to 4 hours, furnace top gas composition analysis, and operator experience to adjust the reformer outlet temperature, process gas flow rate, and oxygen injection. The 2 to 4 hour lag between a process parameter shift and its detection in the product quality lab means that suboptimal metallization or carbon content goes uncorrected for multiple hours of production. iFactory's DRI Process AI predicts metallization and carbon content in real time from shaft furnace temperature profiles, process gas composition, reformer operating parameters, and feed rate — enabling continuous process optimization rather than reactive adjustments based on delayed lab results. Book a Demo to see the platform configured for your DRI plant's specific process technology and instrumentation.
DRI PLANT · AI OPTIMIZATION · METALLIZATION CONTROL · 2026
AI Optimization for Direct Reduced Iron Plants — Metallization, Carbon, and Reformer Control
Predict DRI metallization and carbon content in real time from shaft furnace temperature profile, process gas composition, and reformer parameters. Optimize reformer fuel consumption, adapt to hydrogen injection, and deliver consistent quality to the EAF — all on a turnkey on-premise NVIDIA edge server deployed in 10 to 16 weeks.
01
Metallization Control
92–96% target range
8 items
02
Carbon Content
1.5–3.5% target
6 items
03
Reformer Efficiency
>80% target
7 items
04
Shaft Furnace
Thermal profile control
8 items
05
Gas Management
H₂ + NG mix
6 items
06
Quality Control
HBI & DRI specs
5 items
92–96%Metallization achievable with AI control
$4–8/tCost impact per 1% metallization loss at EAF
8–12%Fuel rate reduction with AI reformer optimization
Why AI Optimization Matters for DRI Plants in 2026
Four industry trends have made DRI plant optimization more critical than at any point in the past two decades. The transition to hydrogen-based ironmaking demands process control systems that can adapt to variable gas compositions. EAF steelmakers increasingly require tight DRI quality specifications that conventional lab-cycle control cannot guarantee. Energy costs for reformer fuel and purchased electricity drive operating margins that reward continuous optimization. And carbon accounting requirements for green steel certifications demand documented process data that manual logging cannot provide at the required granularity. DRI plants that treat process control as a fixed setpoint operation rather than a continuous optimization problem are leaving 8 to 12% fuel savings and 6 to 10% throughput improvements unrealized.
01
Metallization Variability Costs the EAF
Every 1% decrease in DRI metallization increases EAF electrical energy by 4 to 6 kWh per ton and raises slag volume by 10 to 15 kg per ton. A DRI plant producing 2,000 tons per day with metallization cycling between 91% and 95% costs the EAF $400,000 to $800,000 annually in avoidable energy and flux consumption. Conventional lab-based control with 2 to 4 hour sampling cycles cannot correct the process parameters quickly enough to eliminate these swings.
02
Reformer Fuel Is the Largest Operating Cost
Natural gas consumption for the reformer represents 60 to 75% of the total energy cost in a gas-based DRI plant. Suboptimal reformer outlet temperature, steam-to-carbon ratio, and combustion air settings waste 8 to 12% of the fuel input. AI optimization of reformer parameters based on real-time shaft furnace conditions and target metallization can recover the majority of this waste within the first 90 days of deployment.
03
Hydrogen Transition Requires Adaptive Control
Existing DRI plants injecting hydrogen into the process gas stream experience shifts in reduction kinetics, thermal balance, and carbon deposition that fixed-setpoint control cannot track. AI models trained on plants operating at 10% to 30% hydrogen injection demonstrate that metallization prediction accuracy improves by 20 to 30% when the model adapts to real-time gas composition data.
04
Quality Certification Requires Process Data
Green steel certifications, customer quality agreements, and carbon accounting frameworks require documented process data at granularity that manual logging cannot provide. AI-driven DRI plants automatically capture metallization, carbon content, energy consumption, and emissions data per production hour, enabling audit-ready reporting without operator data entry workload.
Key Parameters for DRI Optimization
The checklist below organizes 40 optimization items across five DRI process areas. Best practice: complete this optimization assessment during your plant's next scheduled maintenance outage or process review. Each item should have a documented current baseline, target value, and sensor data availability status before proceeding with AI model deployment. Click each category below to expand the detailed checklist — items marked P1 are critical-path decisions that gate downstream model training and deployment commitments.
Reviewing these parameters for your specific DRI plant configuration? Book a DRI process assessment — we will evaluate each parameter category against your plant's process technology, current instrumentation, and operating targets.
01
Metallization Control
92–96% target · EAF quality driver
8 items
1.1
Metallization measurement method and frequency · P1
Current method (laboratory chemical analysis, XRF, or LECO) and sampling frequency. Every 2 to 4 hours is standard. AI prediction requires correlation with real-time process data to bridge the gap between lab results.
1.2
Shaft furnace temperature profile sensors · P1
Number and location of thermocouples in the reduction zone, transition zone, and cooling zone. Temperature profile is the primary predictor of metallization. Minimum 8 measurement points for effective AI model training.
1.3
Process gas composition measurement
Continuous gas chromatograph or mass spectrometer data for H2, CO, CO2, CH4, and H2O in the process gas loop. Gas composition drives reduction kinetics and carbon deposition. Measurement frequency and accuracy.
1.4
Feedstock quality data integration
Pellet or lump ore reducibility index, size distribution, and chemical composition from supplier certificates or on-site analysis. Feedstock variability is the largest uncontrolled variable in metallization prediction.
1.5
Production rate measurement
Discharge rate measurement accuracy. Production rate changes affect residence time in the shaft furnace and directly impact metallization. AI model must account for rate changes as a primary input.
1.6
Metallization target specification by product
Target metallization for DRI (92 to 95%) vs HBI (91 to 94%). EAF customer specifications may require tighter ranges. AI model trained on product-specific targets enables automatic parameter adjustment at product changeover.
02
Carbon Content Optimization
1.5–3.5% C target · EAF energy
5 items
2.1
Carbon content measurement method · P1
LECO combustion analysis or XRF carbon measurement frequency. Carbon content drives EAF foaming slag practice and yield. Lab turnaround time directly limits carbon control responsiveness.
2.2
Natural gas injection control
Natural gas injection rate and distribution across the shaft furnace circumference. Carbon deposition from methane cracking is the primary carbon source. AI optimization of injection rate based on target carbon and current process conditions.
2.3
Process gas CO/CO2 ratio management
CO/CO2 ratio in the process gas determines the carbon activity and deposition rate. AI model predicts carbon content from gas composition, temperature, and residence time, enabling proactive gas ratio adjustments.
2.4
Oxygen injection for carbon control
Oxygen injection rate at the shaft furnace bustle pipe affects carbon removal and temperature. AI model coordinates oxygen injection with natural gas injection to maintain target carbon without over-oxidation.
2.5
Carbon uniformity across discharge
Carbon content variability across the discharge cross-section. Segregation or uneven gas distribution creates carbon variability. AI model trained on multiple discharge sample locations detects distribution issues.
03
Reformer Performance Management
>80% efficiency · 60–75% of energy cost
6 items
3.1
Reformer outlet temperature control · P1
Temperature setpoint accuracy and stability. Each 10°C variation in reformer outlet temperature affects metallization by 0.3 to 0.5% and carbon content by 0.1 to 0.2%. AI prediction of optimal temperature for current production target.
3.2
Steam-to-carbon ratio management
Steam-to-carbon ratio control prevents carbon deposition on reformer catalyst and affects methane slip. AI optimization of steam-to-carbon ratio based on catalyst condition and production rate reduces energy consumption by 3 to 5%.
3.3
Catalyst condition monitoring
Reformer catalyst activity degrades over time, reducing methane conversion efficiency and requiring higher firing rates. AI analysis of catalyst bed temperature profiles and pressure drop identifies degradation 4 to 8 weeks before conventional monitoring.
3.4
Combustion air-fuel ratio optimization
Excess oxygen in reformer flue gas typically ranges from 3 to 6% in manually tuned plants. AI continuous optimization of air-fuel ratio reduces excess oxygen to 1.5 to 2.5%, reducing fuel consumption by 2 to 4%.
3.5
Flue gas heat recovery monitoring
Flue gas exit temperature indicates heat recovery efficiency. Increasing exit temperature over time signals fouling or degradation in the waste heat recovery system. AI trend detection enables proactive maintenance scheduling before efficiency loss compounds.
3.6
Fired heater tube condition assessment
Reformer tube wall temperature monitoring for hot band formation and creep life assessment. AI analysis of tube temperature profiles identifies localized overheating 8 to 12 weeks before tube failure risk becomes critical.
04
Shaft Furnace Operation
Thermal profile · Gas distribution
8 items
4.1
Reduction zone temperature profile · P1
Temperature profile across the reduction zone (top to bottom and radial) determines reduction kinetics and metallization uniformity. AI model requires continuous temperature measurement at minimum 6 to 8 locations for accurate prediction.
4.2
Process gas distribution across furnace cross-section
Uniform gas distribution is critical for consistent metallization. Pressure differential and temperature asymmetry across the furnace cross-section indicate distribution problems. AI anomaly detection on pattern deviation.
4.3
Feedstock bed permeability monitoring
Pressure drop across the shaft furnace bed indicates permeability. Changes in pressure drop signal feedstock quality shifts, fines accumulation, or channeling. AI prediction of permeability from feedstock data enables preemptive adjustments.
4.4
Process gas temperature control at bustle pipe
Process gas temperature entering the shaft furnace determines the reduction zone thermal profile. Temperature setpoint optimization based on production rate, feedstock reducibility, and target metallization saves 3 to 6% in fuel.
4.5
Cooling zone operation
Cooling gas flow rate and temperature affect discharge temperature and reoxidation risk. AI optimization of cooling parameters based on production rate and ambient conditions prevents quality degradation at the discharge end.
4.6
Top gas temperature monitoring
Top gas temperature reflects the thermal efficiency of the countercurrent heat exchange in the shaft furnace. Rising top gas temperature signals declining heat transfer efficiency and guides process parameter adjustments.
4.7
Discharge system operation
Discharge rate consistency and screw conveyor or rotary valve operation affect furnace burden movement. Irregular discharge creates channeling and uneven reduction. AI monitoring of discharge equipment parameters.
4.8
Furnace pressure control
Shaft furnace top pressure and internal pressure profile affect gas flow distribution and reduction kinetics. AI optimization of pressure setpoint based on production conditions improves energy efficiency by 2 to 4%.
05
DRI Quality & Throughput
Cold DRI · HBI · Certification
5 items
5.1
Metallization and carbon target per product grade · P1
Quality specification ranges for each product: cold DRI (metallization 92 to 95%, carbon 1.5 to 2.5%), HBI (metallization 91 to 94%, carbon 1.8 to 3.5%). AI model predicts quality compliance continuously rather than waiting for lab results.
5.2
HBI briquetting process parameters
Briquetting temperature, pressure, and moisture affect HBI density and handling characteristics. AI optimization of briquetting parameters based on DRI temperature and metallization improves HBI quality and reduces return fines.
5.3
DRI temperature at discharge
Discharge temperature affects reoxidation risk (above 100°C accelerates oxidation) and HBI briquetting performance. AI prediction of discharge temperature from cooling zone parameters enables preemptive adjustments.
5.4
Production rate optimization
Production rate optimization balancing throughput, metallization, and energy consumption. AI model identifies the optimal operating point on the production rate versus quality curve for current feedstock and energy prices.
5.5
Emissions and carbon accounting data
CO2 emissions per ton of DRI, energy consumption per ton, and carbon intensity data for green steel certification. AI platform automatically logs process data at hourly granularity for audit-ready reporting without operator data entry.
Working through these parameters for your specific DRI process? Book a DRI process assessment — we will evaluate each parameter category against your plant's current instrumentation, data infrastructure, and optimization targets.
Optimize Your DRI Plant with AI
A DRI process AI assessment evaluates each optimization area against your specific process technology, current instrumentation, and operating targets. Output: a documented AI deployment plan with sensor gap analysis, model training approach, and projected metallization improvement and fuel savings.
AI Capabilities for DRI Plant Optimization
iFactory's DRI Process AI platform delivers four integrated AI capabilities purpose-built for the operating dynamics of MIDREX, HYL/Energiron, and hydrogen-based DRI production — covering the full process from reformer optimization through shaft furnace metallization prediction to quality certification reporting. Each capability operates on real-time process data and delivers actionable outputs to the DRI plant operator console without modifying existing PLC or DCS control logic.
Capability 01
Predictive Metallization Control
Machine learning models trained on 12 to 24 months of shaft furnace operating data predict metallization 5 to 15 minutes ahead of current conditions based on temperature profile, process gas composition, feedstock properties, and production rate. The AI continuously recommends reformer outlet temperature, process gas flow rate, and oxygen injection adjustments to maintain metallization within 0.3% of target — eliminating the 2 to 4 hour blind spot between lab samples.
Metallization variability reduced by 40 to 60% from baseline
Capability 02
Reformer AI Optimization
AI optimization of reformer operating parameters — outlet temperature setpoint, steam-to-carbon ratio, combustion air-fuel ratio, and firing rate — based on current shaft furnace conditions, feedstock quality, and production target. The model predicts the optimal reformer operating point for each combination of inputs, reducing fuel consumption by 8 to 12% compared to fixed-setpoint operation while maintaining reformer catalyst life and methane conversion efficiency.
8 to 12% fuel reduction without catalyst life impact
Capability 03
Shaft Furnace Digital Twin
A physics-informed digital twin of the shaft furnace simulates the reduction process in real time, providing operators with visibility into the internal thermal and chemical state of the furnace that physical sensors cannot measure directly. The digital twin predicts the impact of process parameter changes before they are implemented, enabling the operator to test reformer temperature adjustments, gas composition changes, and production rate modifications in simulation before committing to the live process.
Operator decision confidence with simulated process changes
Capability 04
Hydrogen-Ready Control Architecture
AI models designed for hydrogen-injected DRI operation adapt to variable H2 content in the reducing gas stream (10 to 100% H2). The model automatically adjusts reduction zone temperature targets, gas flow distribution, and carbon injection parameters as the hydrogen fraction changes, maintaining metallization and carbon content targets across the full range of gas compositions — future-proofing the AI deployment for the hydrogen transition.
Seamless adaptation from 10 to 100% hydrogen in process gas
Common DRI Plant Optimization Challenges
Four optimization challenges account for the majority of avoidable DRI plant operating cost and quality variability. Each is recognizable in retrospect but easy to overlook during daily production pressure. Recognizing them before committing capital to process improvements is the discipline that distinguishes successful optimization programs.
01
Metallization Drift from Feedstock Variation
Pellet reducibility, lump ore quality, and size distribution vary between suppliers and shipments. Conventional control assumes consistent feedstock and corrects only when lab results arrive hours later. By then, 50 to 100 tons of off-spec DRI has been produced. AI models that incorporate feedstock quality data as a model input enable proactive parameter adjustment at the moment of feedstock change, not after the lab confirms the quality shift.
02
Manual Reformer Setpoint Management
Reformer outlet temperature, steam-to-carbon ratio, and combustion settings are typically adjusted manually by operators based on experience and periodic lab results. This approach cannot continuously optimize for changing feedstock, production rate, and ambient conditions. The result: the reformer operates at conditions that are safe and acceptable for a range of inputs but optimal for none, wasting 8 to 12% of fuel input that continuous AI optimization would avoid.
03
Reactive Gas Composition Management
Process gas composition shifts from hydrogen injection, reformer condition changes, and CO2 removal system performance are detected through periodic gas chromatography. Between samples (typically 30 to 60 minutes), the gas composition may shift significantly without operator awareness. AI models that predict gas composition from reformer parameters and CO2 removal system data enable proactive adjustment before the gas composition drift affects metallization.
04
Quality Detection Lag at Discharge
The 2 to 4 hour gap between DRI discharge and lab analysis means that off-spec material is already in storage or shipped before the quality problem is detected. AI prediction of metallization and carbon content at the discharge point eliminates this detection lag entirely, enabling operators to adjust process parameters before off-spec material is produced rather than after.
Industry Expert Perspective
"I have spent fifteen years managing DRI plant operations at three different facilities using MIDREX and HYL process technology, ranging from 1.5 to 2.5 million tons per year. For the first twelve of those years, we operated with the same process control paradigm: set reformer outlet temperature based on a lookup table for the current production rate, adjust oxygen injection when lab results showed metallization drifting, and accept the 2 to 4 hour delay between sampling and corrective action as a fact of DRI production that could not be changed. The AI prediction system changed that completely. The first time the dashboard showed a metallization prediction of 92.8% for the material currently in the shaft furnace — before it reached the discharge screw — and recommended a 5°C reformer temperature increase that brought the next hour's production back to 94.1%, I understood that this was not an incremental improvement in process control. It was a fundamental change in the time horizon of DRI plant management. We moved from correcting quality problems after they happened to preventing them before they occurred. The 8 to 12% fuel reduction in the reformer was the headline number, but the real operational value was the elimination of the reactive firefighting cycle that had defined every shift handover for my entire career in DRI production."
— DRI Plant Manager, Major North American Direct Reduced Iron Producer — 15 Years Industry Experience — 3 DRI Plants — 5.5 Million Tons per Year Combined Capacity
92–96%
Metallization achieved with AI predictive control
8–12%
Reformer fuel reduction with AI optimization
6–10%
Production throughput improvement with AI control
Deploy AI Optimization Across Your DRI Plant
A DRI process AI deployment consultation evaluates your plant's current instrumentation, data infrastructure, and optimization targets. Output: a documented AI deployment plan with sensor gap analysis, model training approach, projected metallization improvement, and fuel savings for your specific process technology.
Frequently Asked Questions
How does AI predict DRI metallization in real time without waiting for lab results?
The AI model is trained on historical data pairing shaft furnace process parameters (temperature profile at 8 to 12 locations, process gas composition, feedstock quality, production rate, reformer conditions) with corresponding lab-measured metallization results. Once trained, the model predicts metallization from current process parameter values at 1-minute intervals, providing a continuous metallization estimate that the operator uses to adjust process parameters between lab samples. Typical prediction accuracy: within 0.3 to 0.5% of lab-measured metallization, validated through periodic lab sample correlation.
Does the AI platform require modifications to the DCS or PLC control systems?
No modifications to the DCS, PLC, or any control system component are required. The AI platform connects through read-only data links to existing process instrumentation and the DCS historian. Metallization predictions, reformer temperature recommendations, and process parameter advisories are displayed on a dedicated operator console that does not write data or commands back to any control system component. The platform operates as an advisory decision-support tool that the DRI plant operator uses to adjust setpoints through the existing DCS interface.
Can the AI platform handle hydrogen injection up to 100% for green DRI production?
Yes. The AI platform architecture is hydrogen-ready by design. The process gas composition inputs include H2 concentration, and the model is trained on the full range of compositions from 0 to 100% H2. As hydrogen injection increases, the model automatically adjusts reduction temperature targets, gas distribution parameters, and carbon injection setpoints to maintain metallization and carbon content targets. Plants currently operating at 10 to 30% hydrogen injection can scale to 100% hydrogen without changing the AI platform.
What is the typical ROI timeline for DRI process AI deployment?
Documented ROI from comparable DRI process AI deployments shows full platform payback within 6 to 12 months at a typical 2,000 ton-per-day DRI plant. Primary ROI drivers: reformer fuel reduction of 8 to 12% ($300,000 to $700,000 annual savings at current natural gas prices), metallization improvement of 0.5 to 1.0% that saves the downstream EAF $200,000 to $500,000 annually in energy and flux consumption, and throughput improvement of 6 to 10% from continuous optimization of the production rate versus quality operating curve.
What data infrastructure and sensor requirements are needed for DRI process AI deployment?
The platform connects to existing DRI plant instrumentation through read-only data links to the DCS historian, process gas chromatograph, and quality lab information system. Required sensor data includes shaft furnace temperature profile (minimum 8 thermocouples), process gas composition (H2, CO, CO2, CH4, H2O), reformer outlet temperature and firing rate, production rate, and feedstock quality data. Most DRI plants already have this instrumentation installed. The NVIDIA edge server is deployed on the plant network with all data processing contained on-premise and no cloud data transmission required.
