[1] Huang Kai, Luo Zhenghong, Chen Fengqiu, et al. Application of nonlinear partial least squarein catalyst modeling [J]. Journal of Southeast University (English Edition), 2004, 20 (1): 65-69. [doi:10.3969/j.issn.1003-7985.2004.01.014]

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Application of nonlinear partial least squarein catalyst modeling()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

20

Issue:

2004 1

Page:

65-69

Research Field:

Chemistry and Chemical Engineering

Publishing date:

2004-03-30

Info

Title:

Application of nonlinear partial least squarein catalyst modeling

Author(s):

Huang Kai¹;  3;  Luo Zhenghong²;  Chen Fengqiu³;  Lü Dewei³

¹Department of Chemistry and Chemical Engineering, Southeast University, Nanjing 210096, China
²Department of Chemical Engineering, Xiamen University, Xiamen 361005, China
³Department of Chemical Engineering, Zhejiang University, Hangzhou 310027, China

Keywords:

partial least square;  catalyst;  oxidative coupling of methane;  neural network;  modeling

PACS:

TQ426.6

DOI:

10.3969/j.issn.1003-7985.2004.01.014

Abstract:

In this paper neural network partial least square(NNPLS)was used to establish a robust reaction model for a multi-component catalyst of methane oxidative coupling. The details, including the learning algorithm, the number of hidden units of the inner network, activation function, initialization of the network weights and the principal components, are discussed. The results show that the structural organizations of inner neural network are 1-10-5-1, 1-8-4-1, 1-8-5-1, 1-7-4-1, 1-8-4-1, 1-8-6-1, respectively. The Levenberg-Marquardt method was used in the learning algorithm, and the central sigmoidal function is the activation function. Calculation results show that four principal components are convenient in the use of the multi-component catalyst modeling of methane oxidative coupling. Therefore a robust reaction model expressed by NNPLS succeeds in correlating the relations between elements in catalyst and catalytic reaction results. Compared with the direct network modeling, NNPLS model can be adjusted by experimental data conveniently and the calculation of the model is simpler and faster than that of the direct network model.

References:

[1] Wold H. Nonlinear estimation by iterative least squares procedures [A]. In: David F, ed. Research Papers in Statistics [C]. New York: John Wiley & Sons, 1966.411-444.
[2] Werbos P J. Beyond regression: new tools for pre￣diction and analysis in the behavioral science [D]. Cambridge, MA: Harvard University, 1974.
[3] Rumelhart D, Hinton G, Williams R. Parallel dis￣tri￣buted processing [M]. Cambridge, MA: MIT Press, 1986.
[4] Hornik K M, Stinchcombe M, White H. Multilayer feed-forward neural networks are universal approximators [J]. Neural Networks, 1989, 2(2): 359-366.
[5] Qin S J, McAvoy T J. Nonlinear PLS modeling using neural networks [J]. Computers Chem Engng, 1992, 16(4): 379-391.
[6] Holcomb T R, Morari M. PLS/neural networks [J]. Computers Chem Engng, 1992, 16(4): 393-411.
[7] Huang K, Chen F Q, Lü D W. Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling [J]. Appl Catal A, 2001, 219: 61-68.
[8] Huang K. Research on computer-aided catalyst design and an application in multi-component catalyst design of methane oxidative coupling [D]. Hangzhou: Department of Chemical Engineering, Zhejiang University, 2001.
[9] Geladi P, Kowalski B R. An example of 2-block pre￣dic￣tive partial least-squares regression with simulated data [J]. Analyt Chim Acta, 1986, 185: 19-32.

Memo

Memo:

Biography: Huang Kai(1973—), male, doctor, lecturer, huangk@seu.edu.cn.

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