| 光谱学与光谱分析 |
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| Spectral Quantitative Analysis by Nonlinear Partial Least Squares Based on Neural Network Internal Model for Flue Gas of Thermal Power Plant |
| CAO Hui1, LI Yao-jiang1, ZHOU Yan2*, WANG Yan-xia1 |
1. State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China |
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Abstract To deal with nonlinear characteristics of spectra data for the thermal power plant flue, a nonlinear partial least square (PLS) analysis method with internal model based on neural network is adopted in the paper. The latent variables of the independent variables and the dependent variables are extracted by PLS regression firstly, and then they are used as the inputs and outputs of neural network respectively to build the nonlinear internal model by train process. For spectra data of flue gases of the thermal power plant, PLS, the nonlinear PLS with the internal model of back propagation neural network (BP-NPLS), the nonlinear PLS with the internal model of radial basis function neural network (RBF-NPLS) and the nonlinear PLS with the internal model of adaptive fuzzy inference system (ANFIS-NPLS) are compared. The root mean square error of prediction (RMSEP) of sulfur dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 16.96%, 16.60% and 19.55% than that of PLS, respectively. The RMSEP of nitric oxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 8.60%, 8.47% and 10.09% than that of PLS, respectively. The RMSEP of nitrogen dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 2.11%, 3.91% and 3.97% than that of PLS, respectively. Experimental results show that the nonlinear PLS is more suitable for the quantitative analysis of glue gas than PLS. Moreover, by using neural network function which can realize high approximation of nonlinear characteristics, the nonlinear partial least squares method with internal model mentioned in this paper have well predictive capabilities and robustness，and could deal with the limitations of nonlinear partial least squares method with other internal model such as polynomial and spline functions themselves under a certain extent. ANFIS-NPLS has the best performance with the internal model of adaptive fuzzy inference system having ability to learn more and reduce the residuals effectively. Hence, ANFIS-NPLS is an accurate and useful quantitative thermal power plant flue gas analysis method.
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Received: 2013-11-01
Accepted: 2014-02-18
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Corresponding Authors:
ZHOU Yan
E-mail: yan.zhou@mail.xjtu.edu.cn
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[1] Huang Y, Wang M, Stephenson P, et al. Fuel, 2012, 101: 244.
[2] WANG Hui-feng, JIANG Xu-qian(王会峰, 江绪前). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(1): 171.
[3] SUN You-wen, LIU Wen-qing, XIE Pin-hua, et al(孙友文, 刘文清, 谢品华, 等). Acta Physica Sinica(物理学报), 2013, 62(1): 010701.
[4] Zhao Luping, Zhao Chunhui, Gao Furong. Chinese Journal of Chemical Engineering, 2012, 20(6): 1191.
[5] Font-i-Furnols Maria, Brun Albert, Tous Nuria, et al. Chemometrics and Intelligent Laboratory Systems, 2013, 122: 58.
[6] Namkung Hankyu, Kim Jaejin, Chung Hoeil, et al. Analytical Chemistry, 2013, 85(7): 3674.
[7] Svante Wold, Nouna Kettaneh-Wold, Bert Skagerberg. Chemometrics and Intelligent Laboratory Systems, 1989, 7: 53.
[8] Jimmy Bak, Anders Larsen. Applied Spectroscopy, 1995, 49(4): 437.
[9] CHENG Zhong, ZHANG Li-qing(成 忠, 张立庆). Chinese Journal of Analytical Chemistry(分析化学), 2009, 37(12): 1820.
[10] Li Zhijun, Ding Liang, Gao Haibo, et al. IEEE Transactions on Fuzzy Systems, 2013, 21(4): 610.
[11] LIU Fei, FANG Hui, ZHANG Fan, et al(刘 飞, 方 慧, 张 帆, 等). Chinese Journal of Analytical Chemistry(分析化学), 2009, 37(1): 67.
[12] Lu Jian, Chen Shuyan, Wang Wei, et al. Expert Systems with Applications, 2012, 39(5): 4775.
[13] Teodoro Aguilera, Jesús Lozano, José A. Paredes, et al. Sensors, 2012, 12(6): 8055.
[14] Mohsen Shahlaei, Armin Madadkar-Sobhani, Afshin Fassihi, et al. Medicinal Chemistry Research, 2012, 21(10): 3246.
[15] Jyh-Shing Roger Jang. IEEE Transactions on Systems, Man, and Cybernetics, 1993, 23(3): 665.
[16] Naes T, Isaksson T, Kowalski B. Anal. Chem., 1990, 62(7): 664.
[17] Saeys W, Beullens K, Lammertyn J, et al. Naes. Anal. Chem., 2008, 80: 4951. |
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