Design and simulation of reactive distillation systems are more complex than usual distillation systems because of the existence of two simultaneous phenomena, reaction and phase equilibrium. Also investigation on simultaneous chemical reactions and phase equilibrium needs reactive-phase equilibrium thermodynamics. In this research, two models of equilibrium stage and neural networks are used for simulation of reactive distillation columns. Initially, modeling and simulation of the processes were done regarding fundamental equations, mass balances, component mass balances and energy balances which are a basis for dynamic simulation of reactive distillation. So that simulation results and main effect parameters were analyzed on reactive distillation columns performance and compared with experimental data. A good agreement was found between experimental data and the model simulation results. Furthermore simulation and dynamic modeling of reactive distillation systems were carried out, using neural network. According to the results of equilibrium stage simulation, it was possible to identify required system, using neural network by proper data. After the specification of network structure, model structure and network train algorithm, identification of the system was done. At last, effective network parameters and the most appropriate structure for identification of dynamic of the system specified. Indeed, this research leads to control of the system by using of the model identified by neural network.