Development of an optimized thermodynamic model for VVER-1200 reactor-based nuclear power plants using genetic algorithm

In this work, an optimized thermodynamic model for the secondary coolant loop of a VVER-1200 reactor-based nuclear power plant is developed. The values of some of the thermophysical parameters such as coolant flow rate, coolant inlet and outlet temperatures from the steam generator and the turbines, etc. are obtained from the available literature. On the other hand, the unknown plant parameters such as feedwater heater pressures are predicted using a Genetic Algorithm-based optimization process. Three crossover rates, 0.5, 0.7 and 0.9, and three mutation rates, 0.01, 0.05 and 0.1, are considered. Results reveal that the value of the optimization process of the thermodynamic model is not significantly affected by the change in the crossover rate or mutation rate. The optimized model is compared with two theoretical models, a model available in the literature and experimental data. Results reveal that the predicted values of the optimized model are more accurate compared to the other available options. The optimized model also predicts that the maximum overall efficiency and the maximum output power of a plant situated in tropical region should be around 35.87% and 1152.16MW(e) respectively for 28 degrees C tertiary coolant temperature, down by 3.94% from the rated values in cold weather. However, the power output at 35 degrees C tertiary coolant temperature is calculated to be 1117.88 MWe, almost 6.69% lower than the rated value for VVER-1200. Thus, the efficiency of the plant is highly sensitive to the weather variation of a region. (C) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

Automated summary

In this work, an optimized thermodynamic model for the secondary coolant loop of a VVER-1200 reactor-based nuclear power plant is developed. The model accurately predicts the plant parameters and reveals the sensitivity of the plant efficiency to weather variation.