Numerical simulation of enhanced fluid flow characteristics in a three-stage double-stirring extraction tank was conducted with the coupling of an Eulerian multiphase flow model and a Morsi-Alexander interphase drag f...Numerical simulation of enhanced fluid flow characteristics in a three-stage double-stirring extraction tank was conducted with the coupling of an Eulerian multiphase flow model and a Morsi-Alexander interphase drag force model. Results show that the addition of a stirring device into the settler can efficiently reduce the volume fraction of out-of-phase impurity in the outlet, and accelerate the settling separation of oil-water mixture. Such addition can also effectively break down the oil-water-wrapped liquid droplets coming from the mixer, inhibit reflux from the outlet, and improve the oil-water separation. The addition of a stirring device induces ignorable power consumption compared with that by the mixer, and can thus facilitate the commercialized promotion of this novel equipment.展开更多
Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear d...Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.展开更多
基金financially supported by the National 863 Plan(2010AA03A405and 2012AA062303)+4 种基金the National 973 Plan(2012CBA01205)the National Natural Science Foundation of China(U120227451204040)the National Science and Technology Support Program(2012BAE01B02)the Fundamental Research Funds for the Central Universities(N130702001 and N130607001)
文摘Numerical simulation of enhanced fluid flow characteristics in a three-stage double-stirring extraction tank was conducted with the coupling of an Eulerian multiphase flow model and a Morsi-Alexander interphase drag force model. Results show that the addition of a stirring device into the settler can efficiently reduce the volume fraction of out-of-phase impurity in the outlet, and accelerate the settling separation of oil-water mixture. Such addition can also effectively break down the oil-water-wrapped liquid droplets coming from the mixer, inhibit reflux from the outlet, and improve the oil-water separation. The addition of a stirring device induces ignorable power consumption compared with that by the mixer, and can thus facilitate the commercialized promotion of this novel equipment.
基金support of National Key Research and Development Program of China(2020YFA0908303)National Natural Science Foundation of China(21878081).
文摘Visual process monitoring is important in complex chemical processes.To address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation processes.BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors.When the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states clearly.This function improves the performance of visual monitoring.Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant analysis.In addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.
