## 1 Introduction  
Fossil fuels that are in use today are primarily characterized by high emissions of CO2 and other pollutants, which worsen the state of the global climate. Generally, the rate of electricity consumption is significantly greater than the rate of electricity usually produced; thus, appropriate concepts are required to propose solutions. Power-to-X has been the energy chain strategy for transforming electricity into energy carriers such as gas, heat, and fuel, which impact multiple industries [1, 2, 3, 4, 5, 6]. Currently, a vast amount of research has been focusing on employing an integrated strategy for the development of the reversible solid oxide cell technology [7, 8, 9, 10, 11]. The dual use both as a fuel cell and as an electrolyzer attracts advancements in the technology. Many studies utilizing advanced techniques have provided insights into the system design aspects so far [12, 13, 14, 15, 16, 17]. Complex fuel sources such as biomass mixtures with different CO, H2, CO2, and CH4 species can be processed by the r-SOC component internally. However, safe operation requires consistency; thus, the management of impurities from the fed fuel stream including complex hydrocarbons and star species benefit from a pre-cleaning using an external reformer that has been utilized in several studies [18, 19, 20, 21, 22, 23, 24]. The pre-reforming process using a suitable pre-reformer component pre-cleans such product streams of varying liquid or gas compositions, resulting in syngas with or without oxygen content that is pure enough to be introduced for SOC operation [25, 26, 27, 28]. In the past, conventional computational methods and experimental measurements were widely used by the authors to predict the detailed design and thermo-chemical reforming performance [29; 30; 31; 32]. In the current research, a combination of so-called traditional methods comprising experimental and numerical means have been blended with artificial intelligence (AI)-based machine learning (ML) modeling where physics-based data have been generated and utilized to systematically evaluate syngas preparation for SOC feed purposes. Recently, it has been demonstrated that AI-based machine learning accurately forecasts energy processes inside the hydrogen economy chain [33; 34; 35; 36; 37]. Thus, the integrated use of proven methods simplifies the management of data acquisition and processing, software, and hardware limitation challenges. In this current study, large data sets generated by empirically validated multiphysics simulation models were processed by accelerated AI-based machine learning models that demonstrated excellent scalability and showed a variety of prediction options using the power of machine learning (ML). The approach is depicted in Figure 1.  
To ensure safe SOC operation using syngas variants, the pre-reformed fuel needs to be carefully assessed. The pre-reforming process prior to SOC operation is beneficial as it prevents oxygen-rich fuel from entering the fuel side of a solid oxide cell, which could result in component degradation and safety issues. High temperatures are essential for the steam-reforming process due to the endothermic nature of steam reforming but also to reduce the chances of carbon deposition. The presence of oxygen in the fuel gas composition, for example, also acts as a reforming agent, thus inducing highly exothermic reactions that may produce both heat and the necessary steam for steam methane reforming processes. Hence, syngas containing small amounts of O2 can be favorable. However, the amount of methane that effectively participates in the reforming reaction in the initial process stages will influence the temperature distribution at this inlet vicinity of the SOC component, which will significantly influence the thermomechanically induced stress field of the SOC. In addition, oxygen must already be depleted.