关键词: 催化裂化, 集总动力学, 神经网络, 人工智能

Abstract: Fluidic catalytic cracking (FCC) is a complex process affected by many highly non-linear and interrelated factors, including properties of raw oil and regenerated catalyst, as well as the operating conditions of reaction. Mathematical modeling and analysis of the process and product yield optimization has been a hot research field in the petroleum processing. Lumped dynamic model is the most commonly used method in mechanism analysis for FCC. The complex composition of raw materials and products can be classified into finite components, which can be used to further analyze the product distribution and its influencing mechanism. It is an important part in the process of building lumped dynamics model to select suitable and fast methods of parameter estimation and calculation. Intelligent algorithms, such as genetic algorithm, particle swarm optimization and simulated annealing algorithm, can overcome the problems that classical algorithms depend on initial values and are difficult to find global optimum to a certain extent. Moreover, they can ensure the convergence of the algorithm. Thus, intelligent algorithms play a great role in promoting the development of lumped dynamics model. In addition, some factors ignored by conventional lumped analysis method can be analyzed by constructing an artificial neural network (ANN) model among the properties of raw oil, regenerated catalyst, operation conditions and product distribution from the statistical point of view, the product distribution can also be further predicted by the ANN, which is a new and effective way to construct the catalytic cracking analysis model. In this paper, the existing research results on data intelligent algorithm and the applications of ANN in the construction of catalytic cracking process model have been reviewed in order to provide possible help for the future research.

Key words: fluidic catalytic cracking, lumped kinetic model, neural network, artificial intelligence