Development and comparative selection of surrogate models using artificial neural network for an integrated regenerative transcritical cycle

Abstract

Surrogate models are becoming increasingly important in replacing the computationally-expensive physics-based simulation models in many applications, such as system optimization, sensitivity analysis and design space exploration. As one of the fastest-growing field, machine learning, specifically artificial neural networks (ANN) have been adapted to model various energy systems. In the present study, five ANN-based surrogate models are developed in replacing the physics-based model of a novel regenerative transcritical power cycle using methanol as the working fluid that is integrated with a small modular reactor. The input layer of the surrogate models consists of the seven design parameters of the cycle, and the output layer returns the 1st-law efficiency, levelized cost of energy and penalty. The evaluation results show that all five candidate surrogate models have demonstrated high R2 score, low relative absolute errors (RAE) and low L1 losses, with the separate multi-layer feed-forward (MLF) neural network model outperforming the others. Once coupled with global optimization, the surrogate model is expected to find the optimal design parameters in order to minimize levelized cost of energy (LCOE) and penalty value in the system.