Artificial neural network prediction of particle size of agglomerated polybutadiene latex

Abstract

Polymer agglomeration is a useful way to enlarge the particle size of polybutadiene latex (PBL). However, the agglomeration process is very complex and is affected by many factors. Therefore, it is difficult to predict the mean particle size and distribution of agglomerated PBL. This study proposed a novel simulation method based on the artificial neural network (ANN) model. Firstly, the influence of agglomeration conditions on the particle size of agglomerated PBL were investigated, including the particle size of pre‐agglomerate PBL, the amount, the α‐methacrylic acid (α‐MAA) content, the solid content and the particle size of agglomerating agent. It is found that these factors have a close nonlinear relationship with the mean particle size and polydispersion index (PDI) of agglomerated PBL. Then, a model containing 236 data points was built using ANN including these properties as input with mean particle size and PDI of agglomerated PBL as output. The coefficient of determination (R2) values predicted by ANN for training, validation, test and total data are 0.9879, 0.9964, 0.9930, and 0.9902, respectively. It indicates that there is a good agreement between experimental data and predicted results by ANN. This study demonstrates that a simple machine learning method has good performance in predicting the particle size of agglomerated PBL, which further improves the development efficiency of PBL polymer agglomeration.