Prediction of styrene conversion of polystyreneatural rubber graft copolymerization using reaction conditions: Central composite design versus artificial neural networks

摘要

Gross copolymer or the total product of graft copolymerization of polystyrene (PS) and rubber, prepared via emulsion polymerization using a redox initiator, is used to investigate the utilization of central composite design and artificial neural network (ANN) approaches in correlating the graft copolymerization conditions to the monomer conversion. The conditions were manipulated by changing four factors: reaction temperature and time, percentage of deproteinized natural rubber (DPNR) in the rubber mixture also containing NR, and amount of chain transfer agent. For DPNR preparation, the incorporation of ultrasound energy into a deproteinizing method (i.e., urea treatment) was preexamined. A shorter reaction time, a lower total nitrogen content, and no agglomeration of rubber particles suggest the success of the incorporation. Results exhibit that the relationship between those factors and the response can be better described by the ANN model, which is further proved to be an excellent tool for the prediction of the conversion at other reaction conditions. In addition, the thermal behavior of gross copolymer is similar to its parents, the rubber and neat PS, but more to the former owing to the larger amount of rubber component.