It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neu...It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neural Network (RBFNN) has the characteristics of optimal approximation and global approximation. The mixed coding of binary system and decimal system is introduced to the structure and parameters of RBFNN, which is trained in course of the genetic optimization. Finally, a fault diagnosis system according to the frequent faults in condensation and feed water system of nuclear power plant is set up. As a result, Genetic-RBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability. The diagnosis speed and accuracy are also improved.展开更多
An experimental study on intensifying osmotic dehydration was carried out ina state of nature and with acoustic cavitation of different cavitating intensity (0.5A, 0.7A and0.9A) respectively, in which the material is ...An experimental study on intensifying osmotic dehydration was carried out ina state of nature and with acoustic cavitation of different cavitating intensity (0.5A, 0.7A and0.9A) respectively, in which the material is apple slice of 5mm thickness. The result showed thatacoustic cavitation remarkably enhanced the osmotic dehydration, and the water loss was acceleratedwith the increase of cavitating intensity. The water diffusivity coefficients ranged from1.8x10^(-10)m^2·s^(-1) at 0.5A to 2.6x10^(-10)m^2·s^(-1) at 0.9A, and solute diffusivitycoefficients ranged from 3.5x10^(-11) m^2·s^(-1) at 0.5A to 4.6X10^(-11)m^2·s^(-1) at 0.9A. On thebasis of experiments, a mathematical model was established about mass transfer during osmoticdehydration, and the numerical simulation was carried out. The calculated results agree well withexperimental data, and represent the rule of mass transfer during osmotic dehydration intensified byacoustic cavitation.展开更多
文摘It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neural Network (RBFNN) has the characteristics of optimal approximation and global approximation. The mixed coding of binary system and decimal system is introduced to the structure and parameters of RBFNN, which is trained in course of the genetic optimization. Finally, a fault diagnosis system according to the frequent faults in condensation and feed water system of nuclear power plant is set up. As a result, Genetic-RBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability. The diagnosis speed and accuracy are also improved.
文摘An experimental study on intensifying osmotic dehydration was carried out ina state of nature and with acoustic cavitation of different cavitating intensity (0.5A, 0.7A and0.9A) respectively, in which the material is apple slice of 5mm thickness. The result showed thatacoustic cavitation remarkably enhanced the osmotic dehydration, and the water loss was acceleratedwith the increase of cavitating intensity. The water diffusivity coefficients ranged from1.8x10^(-10)m^2·s^(-1) at 0.5A to 2.6x10^(-10)m^2·s^(-1) at 0.9A, and solute diffusivitycoefficients ranged from 3.5x10^(-11) m^2·s^(-1) at 0.5A to 4.6X10^(-11)m^2·s^(-1) at 0.9A. On thebasis of experiments, a mathematical model was established about mass transfer during osmoticdehydration, and the numerical simulation was carried out. The calculated results agree well withexperimental data, and represent the rule of mass transfer during osmotic dehydration intensified byacoustic cavitation.
