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The optimal control problem of a Fluidized Catalytic Cracking (FCC) process was achieved using a fuzzy neural network to analyze the process mechanism of the FCC equipment at the Dagang Oil Refinery. The network model was used to study the system identification, modeling and optimal control of the process. The Fuzzy Neural Network (FNN) has the advantages of multiple hidden layers, multiple neurons in each hidden layer, strong approximation ability and a quick convergence rate. Moreover, differential equations can be used to relate the input variables to the output variables, which facilitates the optimization. Fuzzy neural networks are useful for system identification and modeling of complex non-linear production processes.
The optimal control problem of a Fluidized Catalytic Cracking (FCC) process was achieved using a fuzzy neural network to analyze the process mechanism of the FCC equipment at the Dagang Oil Refinery. The network model was used to study the system identification, modeling and optimal control of the process. The Fuzzy Neural Network (FNN) has the advantages of multiple hidden layers, multiple neurons in each hidden layer, strong approximation ability and a quick convergence rate. Moreover, differential equations can be used to relate the input variables to the output variables, which facilitates the optimization. Fuzzy neural networks are useful for system identification and modeling of complex non-linear production processes.