###### Abstract
In this study, Genetic Algorithms (GAs) were selected as the core optimization tool due to their proven effectiveness in solving discrete combinatorial problems, such as the inlet-outlet configuration optimization presented here. GAs offer strong global search capabilities and are particularly suited for problems involving binary or categorical variables. Compared with Particle Swarm Optimization (PSO) [14; 15], which is better adapted to continuous variable spaces, GAs are generally more robust in exploring complex discrete search spaces and avoiding premature convergence to local optima. Additionally, while NSGA-II is widely used in multi-objective optimization [16; 17; 18], the problem addressed in this study is single-objective in nature--minimizing diesel conditioning time. Therefore, GAs were deemed sufficient and computationally efficient for the current framework. Computational Fluid Dynamics (CFD) offers a powerful tool for simulating the complex fluid flow and mixing behavior inside treatment tanks. CFD is widely used in chemical and energy engineering to visualize and quantify velocity fields, pressure distributions, and species concentration gradients [12]. In fuel blending and homogenization processes, CFD enables the evaluation of mixing efficiency and design parameters. For instance, [PERSON] et al. applied CFD to analyze the mixing of B30 biodiesel blends with additives, and their simulations closely matched experimental density data, demonstrating high model reliability [19]. CFD-guided optimization helps determine appropriate flow rates, nozzle configurations, and residence times to ensure uniform additive distribution [20]. State-Space Network (SSN) modeling is a modern approach to process design that captures the flow of materials between unit operations in a directed graph structure. It is particularly suitable for complex systems involving multiple stages and parallel paths. Compared with traditional modular design or superstructure-based methods, SSN provides greater flexibility and computational efficiency in process synthesis and scheduling [12]. For example, [PERSON] et al. demonstrated that integrating SSN with a GA can optimize parallel filtration paths and pumping sequences, resulting in significant time savings [12]. While SSN excels in visual clarity and scalability, its application to large-scale systems requires careful complexity management to ensure computational feasibility. To address this concern, we have revised the Introduction Section to more clearly emphasize the methodological novelty, practical significance, and potential for broader application. We also clarified how our framework contributes to bridging optimization theory and real-world process engineering. In summary, emergency diesel storage systems in nuclear power plants face challenges related to long-term fuel degradation, including oxidative instability and contaminant accumulation. These issues threaten the reliability of emergency power supply systems. The integration of antioxidant-based conditioning, Genetic Algorithm optimization, and CFD simulation has emerged as a promising direction for improving fuel quality and treatment efficiency. Previous studies have explored each aspect independently, including degradation mechanisms, additive strategies, optimization algorithms, and simulation models [2; 9; 12; 19]. However, a comprehensive, integrated approach tailored to nuclear emergency diesel systems remains underexplored. This work aims to fill that gap by developing a hybrid optimization framework that combines State-Space Network modeling, GA optimization, and CFD simulation for enhancing diesel treatment processes in nuclear facilities.