FEATURE ADVANCED MATERIALS & PROCESSES | NOVEMBER 2026 23 Temperature uniformity survey results show that temperature uniformity remains within ±5°C during switching between heating modes when the circulation fan is operating, regardless of the heating configuration or heating strategy. Figure 1 shows the arrangement of burners and electric heaters for the ATLAS Green furnace. In this configuration, it is possible to heat a large portion of the process purely electrically or exclusively with gas. However, during particularly energy-intensive phases of the process, it may be necessary to use both heating methods in combination. The need for intelligent heating control. The availability of hybrid heating systems enables the use of the most economical and sustainable energy source. To fully exploit this potential, energy demand must be predicted in advance. This allows heat treatment processes to be scheduled according to energy availability, increasing the use of renewable energy and reducing both operating costs and CO₂ emissions. Furnace simulation for energy demand forecasting. To support energy-aware production scheduling, a simulation model of the batch chamber furnaces is developed to predict the energy demand of complete heat treatment cycles. The simulation employs a rule-based model of the complete furnace system, comprising process control, auxiliary equipment, and a thermodynamic model of the heating chamber. The control structure reproduces the process sequence, temperature control, and auxiliary equipment operation of the real furnace. Realistic representation of the hybrid heating system is of particular importance, as heating is by far the dominant contributor to the overall energy demand. Therefore, energy demand is calculated using a transient thermodynamic model based on coupled energy balance equations. The model describes heat transfer between the batch, furnace atmosphere, and heating chamber through radiation and convection. To this end, first-principles heat transfer formulations are employed and supplemented with experimentally validated para- meters only where required to represent furnace-specific characteristics such as thermal losses. This approach enables accurate prediction of gas and electricity consumption over complete heat treatment cycles while maintaining low computational effort. Validation of the simulation results against industrial process data demonstrates good agreement between predicted and measured energy consumption and heating power profiles as shown in Fig. 2. The resulting energy demand forecasts can be used to optimize process schedules, reduce peak electrical loads throughout the plant, and align energy-intensive process phases with periods of high renewable energy availability, leading to reduced cost and CO2 emissions for electric heating. Experiments have shown that changing the start time of a 12 h case hardening process alone can save up to 24% of CO2 emissions by shifting the energy intensive heat-up phase to times of high share of renewable energy in the grid with lower CO2 intensity. CARBON FOOTPRINT TRANSPARENCY Increasing sustainability reporting requirements, particularly under the EU Corporate Sustainability Reporting Directive (CSRD), have created a growing demand for transparent carbon accounting across industrial value chains. To address this need, Ipsen developed a software solution for calculating the CO₂ footprint of individual heat treatment batches in accordance with the GHG Protocol. The approach allocates emissions to each batch based on measured electricity, gas, and process media consumption across all relevant production equipment, including furnaces, washing systems, and endogas generators. In addition to process-related emissions, a proportional share of idle emissions is assigned to account for energy consumption while equipment remains in idle state. To improve accuracy and account for fluctuating shares of renewable energy, dynamic emission factors are Fig. 2 — Validation of simulated furnace power demand for a case-hardening process conducted on a real furnace and the simulation model.
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