Factorial experimental design approach in semicontinuous emulsion polymerization of methyl methacrylate to study the effect of process variables

Abstract

This article describes the effect of various process variables in the semicontinuous emulsion polymerization of methyl methacrylate. A series of poly(methyl methacrylate) (PMMA) emulsions were prepared using ammonium persulphate as initiator in absence and presence of Dowfax 2AI as surfactant. The effect of process variables such as initiator concentration, monomer concentration (solid content), surfactant concentration, reaction temperature, monomer feeding time, and holding time were systematically studied on monomer conversion, particle size, gel content, and molecular weight using a twolevel fractional factorial experimental method. Analysis of fractional factorial design revealed that surfactant concentration, monomer concentration, initiator concentration, and monomer feeding time affect all the properties. However, the surfactant concentration and the interaction effect of initiator and monomer feeding time are the key variables influencing the properties of PMMA latex.

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

“…Moreover, the magnitude of coefficients in the expression corresponds to the significant level of effects of both main and interaction process factors on the responses. [17][18][19] In addition, the RSM can be used to optimize process variables to obtain the maximum or minimum response. 20,21 Box-Behnken and central composite designs (CCD) are examples of RSMs that have been used in studying the optimization, interactions, and influences of conditions of graft copolymerization on response values, such as degree of grafting and graft yield and mechanical properties.…”

“…An RSM can be applied to determine an expression (typically polynomial function) that elucidates how a dependent variable (response) is affected by those significant factors over some specified domain. Moreover, the magnitude of coefficients in the expression corresponds to the significant level of effects of both main and interaction process factors on the responses 17–19. In addition, the RSM can be used to optimize process variables to obtain the maximum or minimum response 20, 21.…”