Recent Patents on Engineering

ISSN: 1872-2121

Upcoming Articles


Novel Membrane Contactors Used in Waste Gas/Liquid Separation
Zhang Guoliang, Yang Zhihong, Sun Haimin and Meng Qin
[Abstract]


Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine
Sheng-Wei Fei, Yu-Bin Miao and Cheng-Liang Liu
[Abstract]



Abstracts


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Novel Membrane Contactors Used in Waste Gas/Liquid Separation
Zhang Guoliang, Yang Zhihong, Sun Haimin and Meng Qin

This work reviews the recent patents and progresses of novel membrane contactors used in gas/liquid separation. Compared with conventional separation facilities, membrane contactors will play more and more important role in waste treatment in the near future. The advantages in separation include wider operation range, higher separation efficiency, independent flow between liquid and gas, linear scale-up and compact structure, etc. The structure of hollow fiber membrane contactor are emphatically analyzed and typical gas/liquid separation process such as membrane adsorption, membrane distillation and membrane structured packing in waste treatment are investigated in detail. Some successful commercial products in use such as Liqui-Cel membrane contactor were introduced for example.


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Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine
Sheng-Wei Fei, Yu-Bin Miao and Cheng-Liang Liu

Forecasting of grain production is an important resource for establishing agriculture policy. Particle swarm optimization-based support vector machine (PSO-SVM) is applied to forecast grain production in this paper. In PSO-SVM model, particle swarm optimization (PSO) is used to determine free parameters of support vector machine. PSO is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. The Chinese grain production is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network in Chinese grain production forecasting. Consequently, PSO-SVM is a proper method in Chinese grain production forecasting.