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
Operation optimization is an effective method to explore potential economic benefits for existing plants.The maximum potential benefit from operation optimization is determined by the distances between current operati...Operation optimization is an effective method to explore potential economic benefits for existing plants.The maximum potential benefit from operation optimization is determined by the distances between current operating point and process constraints,which is related to the margins of design variables.Because of various disturbances in chemical processes,some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints.Thus the benefit of steady-state optimization can not be fully achieved while that of dynamic optimization can be really achieved.In this study,the steady-state optimization and dynamic optimization are used,and the potential benefit is divided into achievable benefit for profit and unachievable benefit for control.The fluid catalytic cracking unit(FCCU)is used for case study.With the analysis on how the margins of design variables influence the economic benefit and control performance,the bottlenecks of process design are found and appropriate control structure can be selected.展开更多
A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA)...A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.展开更多
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Supported by the National Natural Science Foundation of China(21006127)the National Basic Research Program of China(2012CB720500)the Science Foundation of China University of Petroleum(KYJJ2012-05-28)
文摘Operation optimization is an effective method to explore potential economic benefits for existing plants.The maximum potential benefit from operation optimization is determined by the distances between current operating point and process constraints,which is related to the margins of design variables.Because of various disturbances in chemical processes,some distances must be reserved for fluctuations of process variables and the optimum operating point is not on some process constraints.Thus the benefit of steady-state optimization can not be fully achieved while that of dynamic optimization can be really achieved.In this study,the steady-state optimization and dynamic optimization are used,and the potential benefit is divided into achievable benefit for profit and unachievable benefit for control.The fluid catalytic cracking unit(FCCU)is used for case study.With the analysis on how the margins of design variables influence the economic benefit and control performance,the bottlenecks of process design are found and appropriate control structure can be selected.
基金The National Natural Science Foundation ofChina(No60504033)
文摘A novel nonlinear combination process monitoring method was proposed based on techniques with memory effect (multivariate exponentially weighted moving average (MEWMA)) and kernel independent component analysis (KICA). The method was developed for dealing with nonlinear issues and detecting small or moderate drifts in one or more process variables with autocorrelation. MEWMA charts use additional information from the past history of the process for keeping the memory effect of the process behavior trend. KICA is a recently developed statistical technique for revealing hidden, nonlinear statistically independent factors that underlie sets of measurements and it is a two-phase algorithm: whitened kernel principal component analysis (KPCA) plus independent component analysis (ICA). The application to the fluid catalytic cracking unit (FCCU) simulated process indicates that the proposed combined method based on MEWMA and KICA can effectively capture the nonlinear relationship and detect small drifts in process variables. Its performance significantly outperforms monitoring method based on ICA, MEWMA-ICA and KICA, especially for long-term performance deterioration.
