Modeling and optimization of the self-embrittle corrosive bifunctional detergent for corrosive deep decontamination of stainless steel surface by RAFT one-pot method based on machine learning and response surface methodology

Abstract

Nuclear facilities generate lots of contaminated stainless steel metallic material during maintenance and decommissioning. As a new radioactive decontamination method, the self-embrittle decontamination method has the advantage of less secondary contaminants and being able to operate machinery remotely. Adding a certain amount of corrosive components to the self-embrittle compound detergent can achieve the dual functions of self-embrittlement and corrosive decontamination. It was used to optimized the response surface methodology (RSM) and train and evaluate four different machine learning models by the recorded data set. The purpose of the analysis was to quantify the accuracy of the corrosion decontamination effect of RSM model and four types of machine learning model. The results exhibits that the long short-term memory neural network (LSTM) model performs well. The prepared detergent can achieve the average corrosion depth of 5.9454 μm on stainless steel, which can satisfy the corrosion decontamination of radioactively contaminated stainless steel surfaces.