Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency
Abstract
1. Introduction
2. State-Space Network (SSN) Superstructure for Diesel Conditioning Pathways
- Steady-State Operation: The diesel conditioning paths are assumed to operate under steady-state conditions, without dynamic adjustments over time or in response to changes in oxidant concentration in the storage tank. This simplifies operation and ensures model stability.
- Focus on Oxidative Stability: The primary degradation concern is assumed to be excessive oxidative instability. The conditioning process is therefore designed specifically to improve this property, while other factors affecting fuel quality are neglected.
- Uniform Additive Distribution: It is assumed that the diesel entering the system already contains antioxidants at a constant and homogeneously distributed volume fraction. This assumption simplifies the modeling and analysis of additive effectiveness.
- Pump Capacity Limit: The maximum flow rate of each transport pump is fixed. The total feed flow rate is constrained to remain below the pump’s capacity to avoid overloading and ensure safe operation.
- One-Way Interface Constraints: Each inlet or outlet of a diesel tank can function either as a feed port or a discharge port, but not both simultaneously. This assumption ensures directional clarity and avoids flow ambiguity.
- Minimum Equipment Availability: At least one feed port, one discharge port, and one conditioning unit must be present to ensure that the system is operable and the conditioning process can proceed successfully.
- Steady, Incompressible, Single-Phase Flow: The diesel–antioxidant mixture is assumed to behave as a steady-state, incompressible, single-phase liquid. Multiphase effects such as evaporation, gas entrainment, or cavitation are not considered. As a result, fluid density is treated as constant, and mass conservation is equivalently expressed as a balance of volumetric flow rates, which simplifies the modeling process and enhances numerical stability.
3. CFD Numerical Simulation Setup
3.1. CFD Model
3.2. Boundary Conditions
4. Mathematical Model and Optimization Framework CFD Numerical Simulation Setup
4.1. Conditioning Path Design
- (a)
- Each individual in the GA population encodes two sets of parameters (A, B, and C and A1, B1, and C1), totaling six genes per individual.
- (b)
- A, B, and C represent the velocity at each of the three tank nozzles (in m/s). The mapping rule is defined as
- ○
- Positive value → inlet with corresponding velocity;
- ○
- Negative value → outlet with absolute value as flow rate;
- ○
- Zero → sealed wall.
- (c)
- A1, B1, and C1 define the configuration of diesel with antioxidant additives. Their constraint condition is
- A11 = −1: diesel outlet (data recorded);
- A11 = 0: sealed boundary;
- A11 = 1: inlet with additive (conditioned diesel);
- A11 = 2: inlet without additive (recycled diesel with same concentration as recorded at outlet);
- B11 and C11 follow the same logic for their respective ports.
4.2. Genetic Algorithm Design
- Crossover uses a two-point strategy, randomly selecting two positions, q1 and q2, in the chromosome and exchanging the gene segments between them to generate new offspring.
- Mutation introduces random perturbations into selected genes to maintain population diversity and avoid premature convergence. The mutation probability decreases over generations, enhancing stability and convergence speed.
- Generation gap is applied to preserve population diversity.
- Elitism guarantees the best-performing individual of each generation is directly carried into the next generation without undergoing genetic operations, ensuring optimal solutions are retained.
4.3. Optimization Workflow
- Initial Population Generation: A random population satisfying path and parameter constraints is generated using MATLAB R2023a-GA. The conditioning parameters are embedded into Fluent journal files.
- CFD Simulation: The journal files are executed in a pre-configured Fluent case to simulate flow and mixing [24].
- Fitness Evaluation: The simulation results are parsed, and the performance of each individual is evaluated based on conditioning effectiveness.
- Convergence Check: If the convergence criterion is met, the optimal result is recorded. If not, the population is updated via selection, crossover, mutation, generation gap, and elitism.
- Population Update: New individuals are translated into journal files, and the simulation is repeated.
- Iterative Optimization: Steps 2–5 are repeated until the termination condition is satisfied and the global optimum is found.
5. Case Study
5.1. Case Configuration
5.2. Genetic Algorithm Optimization Analysis
5.3. SSN-Based Structural Optimization and CFD Validation
6. Discussion
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
GA | Genetic Algorithm |
SSN | State-Space Network |
CFD | Computational Fluid Dynamics |
BHT | Butylated hydroxytoluene (antioxidant additive) |
PM | Maximum pump velocity |
A, B, C | Inlet/outlet velocity variables (for each nozzle) |
A1, B1, C1 | Nozzle configuration state variables |
A11, B11, C11 | Discrete state values for each nozzle (e.g., −1: outlet, 0: closed, 1: inlet) |
y+ | Non-dimensional wall distance for near-wall mesh quality |
η (eta) | Pump efficiency |
ρ (rho) | Density of diesel–antioxidant mixture |
μ (mu) | Dynamic viscosity of fluid |
Q | Volumetric flow rate |
t | Conditioning time |
Ca | Antioxidant concentration |
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Model | Flow Rate Q (m3/h) | Pressure P (MPa) | Speed n (rpm) | Efficiency η (%) | Installation Height (m) | Motor Power (kW) |
---|---|---|---|---|---|---|
65YHC3-25 | 25 | 0.6 | 950 | 65 | 5 | 5.5 |
Property | Specification |
---|---|
Acidity (as KOH)/(mg/100 mL) | ≤7 |
Kinematic viscosity (20 °C)/(mm2/s) | 3.0–8.0 |
Cetane number | ≥45 |
Oxidation stability (insolubles)/(mg/100 mL) | ≤2.5 |
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Zhang, L.; Li, H.; Liu, F.; Chu, X.; Ma, Q.; Ye, H. Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency. Appl. Sci. 2025, 15, 6782. https://doi.org/10.3390/app15126782
Zhang L, Li H, Liu F, Chu X, Ma Q, Ye H. Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency. Applied Sciences. 2025; 15(12):6782. https://doi.org/10.3390/app15126782
Chicago/Turabian StyleZhang, Lanqi, Hao Li, Fengyi Liu, Xiangnan Chu, Qi Ma, and Haotian Ye. 2025. "Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency" Applied Sciences 15, no. 12: 6782. https://doi.org/10.3390/app15126782
APA StyleZhang, L., Li, H., Liu, F., Chu, X., Ma, Q., & Ye, H. (2025). Genetic Algorithm-Based Optimization of Online Diesel Fuel Upgrading Process for Nuclear Power Emergency. Applied Sciences, 15(12), 6782. https://doi.org/10.3390/app15126782