Abstract: It is hard to obtain derivative functions for branched modules in polycondensation process optimization problems with current differentiation approaches,such as symbolic differentiation,extended automatic differentiation (XAD), etc .In this paper,symbolic automatic differentiation (SAD) and numerical automatic differentiation (NAD),the two components of XAD,are integrated in a flexible way to solve this problem.The resulting derivative evaluation approach,named as hybrid automatic differentiation (HAD),is used to solve a polycondensation optimization problem.In the polycondensation model,a flash distillation process model is adopted to calculate the concentration of volatile species in the reaction mass,which is a typical branched module.So the specific module is taken apart from the main model and each of the two parts is managed in a different way.Accordingly,the derivative function obtained by HAD contains two parts:one is a NAD tool to compute the derivatives of the flash distillation process module,the other is a series of symbolic derivatives generated by SAD for derivative evaluation of the main model.Numerical results demonstrate that the HAD based optimization can significantly reduce the solution time,and is of great importance for on-line process optimization.