Abstract: The system of reaction and generation unit of RFCCU is a highly non-linear and strong coupled operation system and is too hard to be described by traditional model. The combination of the artificial neural network (ANN) with strong nonlinear prediction and self-learning ability and the genetic algorithm (GA) with global optimization ability provides a promising way to solve the problem. The optimal initial weights and threshold value are calculated by GA for the BP neural network first and feeded back to BP model to improve the method for random uncertain choice of initial value and the mapping accuracy. In a practical application of this method for a 2.8 Mt/a MIP device, a 6-11-1 type of BP neural network where the GA is used to optimize the weights and values of the BP network was established using the temperatures of two reactors and two regenerators along with the pressures of the reactor and regenerator as six input variables to predict the output variable gasoline yield. The results show that the predictive gasoline yield by BP neural network without GA has the mean squared error (MSE) of 5.16 while the one with GA optimization has the MSE of 4.92.