## 4 Conclusions  
In using both conventional and accelerated contemporary machine learning techniques, the present research delves into advanced assessments in reversible solid oxide cell (r-SOC) fuel pre-processing technologies. A sophisticated supervised physics-based machine learning model is developed and employed to assess reforming processes pertinent to SOC development. Emphasis within the current study is placed on a holistic approach that integrates the use of an experimentally validated CFD model with machine learning techniques. Data are generated to construct and train the multi-regression-type AI-powered machine learning model. The intricate thermochemistry of the pre-reforming auxiliary SOC component containing syngas is evaluated and systematically assessed using DoE. The devised ML model effectively forecasts and optimizes reforming procedures using various fuel compositions, including oxygen-containing syngas. The verification against traditional CFD predictions demonstrates a high level of agreement in model predictions with an RMS, MSE, and R\({}^{2}\) of 18.37, 337.64, and 95%, respectively, contributing significantly to enhanced understanding and qualitative advantages in optimally preparing high-quality syngas. This enhancement promotes improved fuel processing, fostering sustainable research and bolstering the safe and consistent operation of r-SOC technology. The clear non-linear relationships between parameters like fuel flow and air flow emphasize the need for precise control mechanisms, especially in scenarios where the temperature can rapidly rise beyond desired levels. The insights gained from the outcomes can be used to adjust operating conditions to maintain a stable thermal environment at the fuel outlet, thereby improving the overall efficiency and safety of the system.  
In particular, the AI-augmented framework allowed the processing of 48 CFD-generated data sets, resulting in highly accurate predictions of fuel outlet temperatures with an error margin as low as 0.34%, compared to conventional experimentally validated CFD simulations. Key parameters such as air flow rate and air temperature were identified as critical influences on pre-reformer thermal behavior, with the air flow rate showing an estimated 75% main effect, the most significant impact on the temperature distribution, followed by air temperature contributing approximately 10-12% to the fuel outlet temperature. The machine learning model also excelled in predicting non-linear interactions between variables like fuel flow rate, oxygen content, and catalyst porosity. This allowed for the precise tuning of operating conditions, reducing excessive temperature spikes and ensuring optimal fuel processing. The model's scalability further suggests that it can effectively replace computationally expensive CFD methods in future applications, once set and trained with reliable data.  
By achieving these quantitative insights, the combined CFD-AI approach not only enhances syngas preparation efficiency but also reduces the need for resource-intensive experimentation, promoting sustainable development in r-SOC technology. These findings highlight the potential for AI-driven machine learning optimization to streamline future energy systems while ensuring safer, more reliable operations. Hence, the integrated approach facilitates the early availability of valuable information in the pre-development stages.  
The author would like to thank the Federal Ministry of Education and Research (BMWF), which is funding this project with the code 01 DD21005.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author gratefully acknowledges the International Future Laboratory of the Technical University of Munich (TUM) for its collaboration throughout the REDEFINE H2 project.  
There are no conflicts of interest.