| Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process： Hydrogen Recovery from Refinery Gases |
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| 作者姓名： | Lei Wang Cheng Shao Hai Wang Hong Wu |
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| 作者单位： | Lei Wang，Cheng Shao，Hai Wang，Hong Wu Institute of Advanced Control Technology，Dalian University of Technology，Dalian 116024，China; Tianbang National Engineering Research Center of Membrane Technology Co.，Ltd，Dalian 116023，China; Sinopec Zhenhai Refining and Chemical Company Limited，Ningbo 315200. China |
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| 基金项目： | Acknowledgements The authors wish to thank Dr. Qinghui Wu and Dr. Yong Li of Institute of Advanced Control Technology, Dalian University of Technology. |
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| 摘 要： | Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control (APC). For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function (RBF) neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.
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| 关 键 词： | 石油精炼 膜分离 氢回收 软传感器 RBF神经网络 精炼厂 最优化 |
| 收稿时间： | 05 15 2006 12:00AM |
| 修稿时间： | 05 30 2006 12:00AM |
Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases |
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| Lei Wang Cheng Shao Hai Wang Hong Wu.Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process: Hydrogen Recovery from Refinery Gases[J].Journal of Natural Gas Chemistry,2006,15(3):230-234. |
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| Authors: | Lei Wang Cheng Shao Hai Wang Hong Wu |
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| Institution: | 1. Institute of Advanced Control Technology, Dalian University of Technology, Dalian 116024, China; |
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| Abstract: | Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control (APC). For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function (RBF) neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process. |
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| Keywords: | membrane separation hydrogen recovery soft sensor RBF neural networks refinery operation optimization |
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