Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input ...Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.展开更多
We consider the process of hemodialysis performed by means of a hollow fiber dialyzer with a special focus on the dynamics of the light solutes （including metabolic waste products） through the porous fibers membrane...We consider the process of hemodialysis performed by means of a hollow fiber dialyzer with a special focus on the dynamics of the light solutes （including metabolic waste products） through the porous fibers membrane. The model we illustrate here completes the one formulated in a previous paper in which solutes concentrations in the dialyzate were neglected. Exploiting the large difference between the characteristic time of the processes in the machine and the relaxation time to equilibrium in the body, we confine our study to the case of constant input data in order to emphasize the role of the solute transport mechanisms. Numerical solutions show that diffusion is dominant at the early stage of filtration.展开更多
基金Supported by Beijing Municipal Education Commission (No.xk100100435) and the Key Research Project of Science andTechnology from Sinopec (No.E03007).
文摘Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
文摘We consider the process of hemodialysis performed by means of a hollow fiber dialyzer with a special focus on the dynamics of the light solutes （including metabolic waste products） through the porous fibers membrane. The model we illustrate here completes the one formulated in a previous paper in which solutes concentrations in the dialyzate were neglected. Exploiting the large difference between the characteristic time of the processes in the machine and the relaxation time to equilibrium in the body, we confine our study to the case of constant input data in order to emphasize the role of the solute transport mechanisms. Numerical solutions show that diffusion is dominant at the early stage of filtration.
