Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and sof...Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.展开更多
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...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.展开更多
基金Supported by the Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(2016RCJJ046)the National Basic Research Program of China(2012CB720500)
文摘Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.
文摘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.
