Catalytic Cracking and PSO-RBF Neural Network Model of FCC Cycle Oil

Catalytic cracking experiments of FCC cycle oil were carried out in a fixed fluidized bed reactor.Effects of reaction conditions,such as temperature,catalyst to oil ratio and weight hourly space velocity,were investigated.Hydrocarbon composition of gasoline was analyzed by gas chromatograph.Experimental results showed that conversion of cycle oil was low on account of its poor crackability performance,and the effect of reaction conditions on gasoline yield was obvious.The paraffin content was very high in gasoline.Based on the experimental yields under different reaction conditions,a model for prediction of gasoline and diesel yields was established by radial basis function neural network(RBFNN).In the model,the product yield was viewed as function of reaction conditions.Particle swarm optimization(PSO)algorithm with global search capability was used to obtain optimal conditions for a highest yield of light oil.The results showed that the yield of gasoline and diesel predicted by RBF neural network agreed well with the experimental values.The optimized reaction conditions were obtained at a reaction temperature of around 520℃,a catalyst to oil ratio of 7.4 and a space velocity of 8 h-1.The predicted total yield of gasoline and diesel reached 42.2% under optimized conditions.