The significance for the decarbonization of the metal industry is enormous: In Germany, approximately 20% of total energy demand is attributable to industrial process heat, with fossil fuels such as natural gas still dominating. The steel and metal industry is responsible for about 7% of global CO₂ emissions. To achieve climate goals by 2050, alternative energy sources must be developed and existing processes fundamentally optimized.
Central challenges include increasing energy efficiency, drastically reducing CO₂ emissions, and managing volatile energy prices. AI-supported systems can make a decisive contribution here through precise prediction models, dynamic process control, and continuous optimization.
Fundamentals of Hybrid Heating in the Metal Industry
Hybrid furnace systems are based on the principle of flexible energy supply through multiple heating systems connected in parallel or sequentially. In practice, this means, for example, the combination of electric induction heating with hydrogen- or gas-based burner systems. The technical principles include adaptive load distribution between energy sources, synchronization of heating cycles, and integration of intelligent control systems.
In reheating furnaces for steel production, electro-hybrid systems are increasingly being used, which can switch between sources depending on energy availability and pricing. Heat treatment furnaces in aluminum processing increasingly rely on the combination of resistance heating and alternative fuel sources.
The hybrid energy sources include electric-gas operated systems, where the electric component achieves optimal efficiency primarily with molten material, while gas burners are used in early process steps. Hydrogen-gas combinations allow for gradual decarbonization, where the hydrogen proportion can be increased step by step depending on availability and infrastructure.
AI Applications in Hybrid Furnace Operation: Process Optimization & Real-time Control
The Synergy of AI and Hybrid Heating for Decarbonization
The interaction of Artificial Intelligence and hybrid heating systems unfolds its full potential only through the intelligent orchestration of multiple energy sources to achieve decarbonization goals. While traditional control systems rely on static rule sets, AI algorithms enable dynamic, self-learning optimization that simultaneously considers energy efficiency, emission reduction, and economic viability.
Predictive Process Models: The Foundation of Intelligent Control
Predictive process models form the foundation of AI-supported hybrid heating systems. LSTM networks (Long Short-Term Memory) have proven to be particularly powerful, as they can model temporal dependencies in temperature profiles over several hours. In the context of hybrid systems, this is crucial: The thermal inertia of different heating systems—electric induction responds in milliseconds, gas-fired burners require seconds to minutes—must be precisely predicted to avoid temperature fluctuations.
Convolutional Neural Networks (CNN) also analyze high-dimensional sensor data (temperature fields, pressure distributions, gas concentrations) and can recognize patterns that remain hidden to human operators. The combination of LSTM for time series analysis and CNN for spatial pattern recognition creates a holistic process understanding that forms the basis for decarbonization strategies.
Automatic Adjustment of Heating Parameters through Reinforcement Learning
The automatic adjustment of heating parameters in hybrid systems represents a highly complex optimization task. Deep Reinforcement Learning (DRL), particularly PPO (Proximal Policy Optimization), has established itself as a key technology. The decisive advantage: DRL agents learn optimal control strategies through trial and error in simulated environments (Digital Twins) without endangering the real process.
In practice, this means: A PPO agent receives as input the current system state (temperatures, energy prices, CO₂ intensity of the power grid, available hydrogen quantity) and decides in real time how to divide the energy supply between electric, gas-, and hydrogen-based heating. The reward function considers multiple objectives: minimal energy costs, minimal CO₂ emissions, maximum product quality, and system availability. Studies show that such systems, after a training phase of 1000-5000 simulation episodes, outperform conventional control strategies by 15-25%.
Optimization of Energy Source Mix: CO₂ Footprint as Control Variable
The intelligent energy source mix is the core of decarbonization through Hybrid Heating. AI systems use multi-objective optimization to navigate the trade-off between costs, efficiency, and emissions. Specifically, this means:
Real-time assessment of CO₂ intensity: Machine learning models integrate live data on the CO₂ intensity of the power grid. When renewable energies dominate (low CO₂ factor), the system prioritizes electric heating. With a high proportion of fossil power generation, it can temporarily switch to hydrogen or biogas, even if this is more expensive in the short term—provided decarbonization goals require it.
Predictive energy price models: Gradient boosting algorithms (XGBoost, LightGBM) forecast day-ahead and intraday electricity prices with high accuracy. The system can thus shift energy-intensive process steps to low-price phases, which is particularly relevant for batch processes (e.g., heat treatment).
Hydrogen availability management: In the future, green hydrogen will not be continuously available. AI models optimize hydrogen use based on availability forecasts, storage levels, and alternative usage possibilities in the plant. Random forest classifiers have proven effective in identifying optimal switching points between energy carriers.
Dynamic Adaptation to Volatile Energy Markets and Grid Stability
The integration of renewable energies leads to highly volatile electricity prices and grid frequencies. Hybrid AI-controlled heating systems can contribute to grid stabilization as “flexible loads.”
Demand Response Integration: AI systems receive grid signals and temporarily reduce electric heating output during grid overload, compensating by increasing gas or hydrogen supply. Studies show that such systems can contribute up to 30% to load flexibility with less than 2% product quality loss.
Predictive Grid Analytics: LSTM networks forecast grid frequency and voltage stability 15-60 minutes in advance, based on weather data (wind/PV forecast) and historical load profiles. The hybrid heating system can proactively respond to expected bottlenecks.
CO₂-optimized operation: The glass industry impressively demonstrates the potential: Through AI-supported optimization of the energy source mix, energy savings of 10-22% and CO₂ reductions of up to 30% were achieved. Critical was the integration of real-time data on grid CO₂ intensity into process control.
The Role of Edge AI for Real-time Capability
For hybrid systems, real-time capability is essential. Edge AI—the execution of AI models directly on industrial controllers or dedicated edge devices—reduces latencies from cloud-based solutions (50-200ms) to less than 10ms. This is critical for:
- Fast burner switching: Under unstable grid conditions, switching between energy sources must occur within seconds
- Temperature control: Overheating can cause product damage or system failures; Edge AI enables predictive countermeasures
- Safety-critical decisions: Hydrogen operation requires continuous monitoring; Edge AI can autonomously initiate emergency shutdowns in case of anomalies
Lightweight models such as MobileNet or DistilBERT have been successfully implemented on industrial PLCs (Siemens S7-1500, Beckhoff CX series) and achieve inference times of 5-15ms despite limited computing power.
Integration with Digital Twins: Simulation-to-Reality Transfer
Digital Twins function as “training grounds” for AI algorithms. DRL agents particularly benefit: They can run through millions of control scenarios in accelerated simulation without consuming real resources. The transfer from simulation to reality is improved through domain randomization—simulation parameters (materials, environmental conditions, disturbances) are systematically varied to learn robust, generalizable policies.
The TwinHeat project demonstrates this impressively: A combined system of CFD simulation, neural networks, and model predictive control optimizes glass melting furnaces. The Digital Twin generates synthetic training data for rare operating states (e.g., hydrogen feed during peak loads) that cannot be tested in reality or only with risk.
Quantifiable Decarbonization Effects
The combination of AI and Hybrid Heating delivers measurable decarbonization successes:
- Steel production (H2 Green Steel): 95% CO₂ reduction through hydrogen-based direct reduction with AI-optimized process control
- Glass industry: 20-30% energy savings and parallel emission reduction through intelligent gas-electric hybridization
- Aluminum recycling: 15% efficiency increase through AI-controlled oxy-fuel combustion with hydrogen
These successes are not based on a single technology, but on intelligent orchestration: AI as the “conductor” of a hybrid energy system that operates flexibly, efficiently, and in a decarbonized manner.
Digital Twins
A Digital Twin is a virtual replication of a physical system that is continuously synchronized with real-time data. In the context of industrial furnaces, the Digital Twin represents the thermal, mechanical, and energetic behavior of the entire system and enables simulations, predictions, and optimizations without intervention in ongoing operations.
The functionality is based on the integration of several components: Sensor data from temperature, pressure, flow, and energy consumption are captured and transmitted in real time to the digital model. Physical models (e.g., CFD simulations for flow and heat transfer processes) are combined with data-driven AI models to enable highly precise predictions.
The integration with AI algorithms occurs at multiple levels. Neural networks learn from the simulation data of the Digital Twin and can thus predict process parameters or develop optimal control strategies. Convolutional Neural Networks (CNN), for example, analyze thermographic images from the simulation to evaluate temperature distributions. The TwinHeat project at MINES Paris develops AI-supported Digital Twins specifically for industrial furnaces that can not only predict but also actively optimize and control.
Virtual process simulation and optimization allow new operating strategies to be tested risk-free. Changes to process control, new energy sources, or modified heating strategies can be evaluated in digital space before being implemented in reality. This significantly reduces downtime and production losses.
Scenario analyses for different energy mixes are particularly valuable: The Digital Twin can simulate how a higher hydrogen proportion, increased electric heating, or alternative fuels affect efficiency, product quality, and emissions.
Economic Viability & Benefits
The economic viability of hybrid AI-supported heating systems is evident in several dimensions. Energy savings of 10-22% are documented through optimized process control and intelligent energy source management. In the glass industry, specific energy consumption could be reduced by up to 20% through AI optimization, which means significant cost savings for energy-intensive processes.
CO₂ reduction represents the central sustainability aspect. Studies show that through electrification combined with renewable electricity, CO₂ emissions can be reduced by 70-95%. Hydrogen-based systems achieve similar values when green hydrogen is used. The H2 Green Steel project in Sweden aims for a 95% CO₂ reduction compared to conventional steel production.
ROI considerations must take into account both investment and operating costs. While hybrid systems require higher initial investments, they pay for themselves through lower energy costs, avoided CO₂ charges, and improved product quality. The integration of AI additionally reduces maintenance costs through predictive maintenance and minimizes production failures. Case studies from the steel industry show payback periods of 3-7 years, depending on local energy prices and regulatory frameworks.
Challenges
Despite the promising potential, significant challenges exist. Data quality and availability are often problematic: Industrial processes generate large amounts of data, but these are frequently incomplete, noisy, or inconsistent. AI models require high-quality, representative training data that must first be systematically captured in many operations.
Integration into existing systems requires significant investments in sensors, communication infrastructure, and control systems. Many industrial furnaces are designed for a service life of 15-20 years; their retrofitting to hybrid systems with AI control is technically complex and cost-intensive. The transition from fossil to decarbonized energy carriers cannot occur instantaneously but must be designed gradually and economically viable.
The complexity of hybrid combustion systems places special demands on control and monitoring. The interaction of different heating systems, different response times, and the need for precise temperature control require highly developed control algorithms. AI models must be robust against process variations and function reliably even under unusual operating conditions.
Hydrogen availability and infrastructure are currently still limited. Green hydrogen will only be available on a larger scale in the coming years. Additionally, the necessary pipeline infrastructure for hydrogen transport and distribution is lacking in many places. The conversion from natural gas to hydrogen requires adjustments to burner systems, seals, and materials.
The skilled labor shortage affects both AI development and industrial process expertise. The implementation and maintenance of AI-supported systems require personnel with interdisciplinary know-how that combines process engineering, data analysis, and computer science. Additionally, investment costs for AI infrastructure, computing capacity, and continuous model maintenance must be considered.
Future Perspectives
The path to complete decarbonization by 2050 requires a comprehensive transformation of process heat generation. AI will play a key role by enabling the complex orchestration of different energy carriers, adaptation to volatile energy markets, and continuous process optimization.
The further development of Green Hydrogen is central to the decarbonization of the metal industry. AI systems will be needed to optimize the integration of hydrogen into existing processes, as its combustion properties differ from natural gas. Machine learning can help identify optimal mixing ratios and minimize NOx emissions.
Autonomous Operations are increasingly becoming reality: Self-learning systems based on reinforcement learning can largely autonomously optimize processes and respond to disruptions. Edge AI enables real-time decisions directly at the machine, without dependence on cloud infrastructures. Studies show that such systems can reduce downtime by up to 80%.
The integration of Generative AI opens up new possibilities: Large Language Models could support process engineers in diagnosing and solving problems, while Generative Adversarial Networks (GANs) can generate synthetic training data for rare process states to make AI models more robust.
Extended Digital Twin networks will in the future map entire production sites or even cross-company value chains. Federated Learning enables the exchange of knowledge between different sites without revealing sensitive operational data. The combination of Physics-Informed Neural Networks and data-driven approaches will further improve prediction accuracy.
Conclusion
The use of Artificial Intelligence in the hybrid operation of industrial furnaces in the metal industry represents a promising approach to achieving decarbonization goals. Through the intelligent combination of different energy sources, predictive control algorithms, and the use of Digital Twins, both energy efficiency and process quality can be significantly improved.
The technological foundations are largely available: Machine learning algorithms such as LSTM, Reinforcement Learning, and ensemble methods have demonstrated their performance in numerous studies. Digital Twins enable risk-free optimization and scenario analyses. Edge AI reduces latencies and increases system robustness.
Nevertheless, challenges exist, particularly in integration into existing systems, ensuring sufficient data quality, and availability of green hydrogen. Economic viability is heavily dependent on local energy prices, funding programs, and CO₂ pricing mechanisms.
The future perspectives are promising: The increasing availability of renewable energies, falling costs for hydrogen technologies, and continuous advances in the field of Artificial Intelligence will accelerate the transformation of the metal industry. Companies that invest early in hybrid, AI-supported systems will not only meet regulatory requirements but also achieve competitive advantages through higher efficiency and flexibility.
The path to a climate-neutral metal industry leads through the intelligent integration of different technologies—and Artificial Intelligence is the key to mastering this complexity and enabling continuous optimization.
