Radial basis function neural network and overlay sampling uniform design toward polylactic acid molecular weight prediction

Abstract

Polylactic acid (PLA) is a potential polymer material as a substitute for traditional plastics, and the accurate molecular weight distribution range of PLA is strictly required in practical applications. Therefore, exploring the relationship between synthetic conditions and PLA molecular weight is crucially important. In this work, direct polycondensation combined with overlay sampling uniform design (OSUD) was applied to synthesize the low molecular weight PLA. Then a multiple regression model and two artificial neural network models on PLA molecular weight versus reaction temperature, reaction time, and catalyst dosage were developed for PLA molecular weight prediction. The characterization results indicated that the low molecular weight PLA was efficiently synthesized under this method. Meanwhile, the experimental dataset acquired from OSUD successfully established three predictive models for PLA molecular weight. Among them, both artificial neural network models had significantly better predictive performance than the regression model. Notably, the radial basis function neural network model had the best predictive accuracy with only 11.9% of mean relative error on the validation dataset, which improved by 67.7% compared with the traditional multiple regression model. This work successfully predicted PLA molecular weight in a direct polycondensation process using artificial neural network models combined with OSUD, which provided guidance for the future implementation of molecular weight-controlled polymer’ synthesis.