Evaluation of a Coating Process for SiO2/TiO2 Composite Particles by Machine Learning Techniques

In this study, in order to optimize a fabrication process for SiO2/TiO2 composite particles and control their coating ratio (CTi), regression models for the coating process were constructed using various machine learning techniques. The composite particles with a core (SiO2)/shell (TiO2) structure were synthesized by mechanical stress under various fabrication conditions with respect to the supply volume of raw materials (V), addition ratio of TiO2 (rTi), operation time (t), rotor rotation speed (S), and temperature (T). Regression models were constructed by the least squares method (LSM), principal component regression (PCR), support vector regression (SVR), and the deep neural network (DNN) method. The accuracy of the constructed regression models was evaluated using the determination coefficients (R2) and the predictive performance was evaluated by comparing the prediction coefficients (Q2). From the perspective of the R2 and Q2 values, the DNN regression model was found to be the most suitable model for the present coating process. Moreover, the effects of the fabrication parameters on CTi were analyzed using the constructed DNN model. The results suggested that the t value was the dominant factor determining CTi of the composite particles, with the plot of CTi versus t displaying a clear maximum.