This paper proposes an AI-based module for a loading pattern (L/P) optimization algorithm applied to the i-SMR, designed

for fl exible operation. The AI module can be used as a surrogate model in the simulated annealing (SA) screening process,

which allows for more effi cient optimization. The convolution neural network (CNN) model was trained using reactor core

L/Ps and corresponding core parameter values derived from a realistic core simulation code. For load-following operations,

we selected core parameters such as control rod insertion depth, radial peaking factor, axial shape index, and eff ective

multiplication factor. To calculate the objective function of an L/P during the SA process using core design codes, it takes

approximately 3 s, while the AI-based module can predict the objective function within about 0.1 ms. During the prediction

of selected parameters, we discovered two factors aff ecting prediction accuracy. First, the model exhibited a signifi cant

increase in error when trained on dataset containing negative values. Second, utilizing batch normalization (BN) layer and

squeeze and excitation (SE) module, intended to improve accuracy, resulted in a decrease in performance of the model.

Our study demonstrated that the CNN-based model achieves excellent prediction accuracy and has an ability to accelerate

optimization algorithms by taking advantage of artifi cial intelligence’s inherent computational speed.