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 investig...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.展开更多
A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF ...A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.展开更多
Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning a...Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in FCC.This paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up research.Compared with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large delay.AI methods applied in product yield analysis build models based on massive data.By fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model accuracy.AI methods applied in flue gas desulfurization can be usually divided into two stages:modeling and optimization.In the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base.AI methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition warning.Knowledge-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction recall.While the data-driven methods are just the opposite.Thus,some studies combine these two methods to obtain better results.展开更多
基金support of the Chinese National Program for Fundamental Research and Development(973 program)(2012CB215006)
文摘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.
基金Projects(60974031,60704011,61174128)supported by the National Natural Science Foundation of China
文摘A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.
基金the State Key Program of National Science Foundation of China(No.61836006)the National Natural Science Fund for Distinguished Young Scholar(No.61625204)+1 种基金the National Natural Science Foundation of China(Nos.62106161 and 61602328)the Key Research and Development Project of Sichuan(No.2019YFG0494).
文摘Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated factors.Product yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in FCC.This paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up research.Compared with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large delay.AI methods applied in product yield analysis build models based on massive data.By fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model accuracy.AI methods applied in flue gas desulfurization can be usually divided into two stages:modeling and optimization.In the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base.AI methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition warning.Knowledge-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction recall.While the data-driven methods are just the opposite.Thus,some studies combine these two methods to obtain better results.
