###### Abstract  
To enhance the oxidative stability of aging diesel fuel stored in nuclear power emergency systems, we propose a novel hybrid optimization framework that integrates a Genetic Algorithm (GA), State-Space Network (SSN) modeling, and Computational Fluid Dynamics (CFD) simulation. Unlike previous studies that address treatment efficiency, flow optimization, or simulation separately, our method achieves real-time, simulation-informed optimization by embedding CFD-based performance evaluation directly into the GA fitness function. The SSN is employed to construct a comprehensive superstructure of feasible conditioning paths, which are dynamically explored and optimized by the GA under flow and boundary constraints. The CFD model, implemented via Ansys Fluent, accurately simulates the antioxidant mixing process in the tank and provides feedback on concentration uniformity at key monitoring points. The results demonstrate that the proposed framework reduces the conditioning time by 5.38% and significantly enhances the additive distribution uniformity. This work offers a generalizable approach for intelligent diesel upgrading in high-reliability energy systems and contributes a structured pathway for integrating data-driven optimization with physical process simulation. genetic algorithm; CFD simulation; aged diesel; online upgrading; optimization; state-space network +
Footnote †: journal: Applied sciences  
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gum-like substances, which alter the chemical properties of the fuel and can lead to filter clogging and injector fouling [5]. Extensive studies have shown that diesel's oxidative stability decreases with storage time, posing increased risks to system performance. For example, [PERSON] et al. demonstrated that contact between metal ions and biodiesel-diesel blends reduced the oxidation induction period from compliant levels to as low as 0.05 h, revealing the catalytic role of metals in accelerating oxidative degradation [6]. As a result, improving the oxidative stability of stored diesel is essential for ensuring the functionality and safety of backup power systems in nuclear power plants [7]. Adding antioxidants is a widely used method for enhancing diesel oxidative stability. Antioxidants function by scavenging free radicals and decomposing peroxides, thereby interrupting the chain reaction of oxidation and prolonging the shelf life of fuels [7; 8]. Numerous studies have reviewed the selection and effectiveness of various antioxidant additives across different fuel types. For instance, [PERSON] et al. [9] investigated a composite additive containing tetraethylenepentamine (TEPA) and tert-butylhydroquinone (TBHQ), which increased the oxidation induction time of Jatropha biodiesel from 4.24 h to 7.83 h, significantly enhancing fuel stability. Both synthetic and natural antioxidant systems are under active investigation. However, there is no universal solution, as different diesel compositions require customized antioxidant formulations. Further research is needed to understand the mechanisms and compatibility of antioxidant additives with various diesel systems. Genetic Algorithms (GAs), known for their global search capabilities and flexibility in handling nonlinear, non-convex, and discrete problems, have been extensively applied in industrial process optimization [10]. GAs mimic natural evolution to iteratively improve candidate solutions, making it ideal for solving complex scheduling and parameter tuning problems [11]. In the context of diesel treatment, [PERSON] et al. used a GA to optimize the configuration of filtration modules, reducing treatment time and improving throughput efficiency [12]. GAs have proven effective in the multi-objective optimization of process configurations, especially when integrated with simulation models [13].