光谱学与光谱分析 |
|
|
|
|
|
Near Infrared Spectroscopy Study on Nitrogen in Shortcut Nitrification and Denitrification Using Principal Component Analysis Combined with BP Neural Networks |
HUANG Jian1,2, HUANG Shan1,2, ZHANG Hua1,2, HUANG Xian-huai1,2*, ZHANG Yong1,2, TAO Yong1,2, TANG Yu-chao1,2, WANG Meng1,2 |
1. School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China 2. Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University, Hefei 230601, China |
|
|
Abstract To achieve efficient nitrogen removal and rapid detection of ammonia nitrogen and nitrite nitrogen, principal component analysis and neural networks were used to establish quantitative analysis model of ammonia nitrogen and nitrite nitrogen in shortcut nitrification and denitrification based on near infrared spectroscopy—BP neural networks model. The results showed that ammonia nitrogen concentration decreased from 45.3 to 2.7 mg·L-1 after aerobic, and nitrite nitrogen concentration increased from 0.01 to 19.6 mg·L-1, while nitrite nitrogen concentration decreased from 19.6 to 1.2 mg·L-1 after anoxic, which means that rapid nitrification and denitrification are successfully achieved. The principal component analysis of the original near infrared spectra for water samples showed the first 13 principal components represented the information of the original spectrum data, with cumulative contribution rate being 95.04%. In this way, redundant information can be eliminated to reduce the number of dimensions in the model. The spectral data matrix is accordingly reduced from 192×2203 to 192×13, which contributes greatly to easier calculations and improves the accuracy of the model. The correction results of BP neural networks model showed the coefficient of determination for ammonia nitrogen and nitrite nitrogen concentration was 0.950 4 and 0.976 2 respectively, with the root mean square error of calibration being 0.016 6 and 0.010 9. BP neural networks model yields predicted values fitting well with the expected values for ammonia nitrogen and nitrite nitrogen concentration, with R2 being 0.974 0 and 0.981 4 respectively, with the root mean square error of prediction being 0.033 7 and 0.028 7, suggesting that BP neural networks model had a good prediction results for ammonia nitrogen and nitrite nitrogen concentration. The study demonstrated that ammonia nitrogen and nitrite nitrogen concentration can be rapidly predicted with BP neural networks based analysis of the near infrared spectroscopy of the water sample in shortcut nitrification and denitrification, which may provide timely and flexible control to shortcut nitrification and denitrification operation according to the ammonia nitrogen and nitrite nitrogen concentration changes, and makes a quick and effective detection technique for denitrification.
|
Received: 2015-04-20
Accepted: 2015-10-05
|
|
Corresponding Authors:
HUANG Xian-huai
E-mail: huangxh@ahjzu.edu.cn
|
|
[1] LI Ling-yun, PENG Yong-zhen, YANG Qing, et al. China Environmental Science, 2009,(3): 312. [2] ZHI Xia-hui, HUANG Xia, LI Peng, et al. China Environmental Science, 2009,(5): 486. [3] GAO Rong-qiang, FAN Shi-fu. Analytical Instrumentation, 2002, (3): 9. [4] Balabin R M, Safieva R Z. Analytica Chimica Acta, 2011, 689(2): 190. [5] HE Jin-cheng, YANG Xiang-long, WANG Li-ren, et al. Acta Scientiae Circumstantiae, 2007, 27(12): 2105. [6] Stephens A B, Walker P N. Transactions of the ASAE, 2002, 45(2): 451. [7] LIU Hong-xin, ZHANG Jun, WANG Ba-guang, et al. Optics and Precision Engineering, 2009, 17(3): 525. [8] LIU Hong-xin, ZHANG Jun, WANG Ba-guang, et al. Journal of Analytical Science, 2008, 24(6): 664. [9] WU Jiang, HUANG Fu-rong, HUANG Cai-huan, et al. Spectroscopy and Spectral Analysis, 2013, 33(6): 1537. [10] YU Ze-bin,, SHI Li-ling. Journal of Guilin University of Technology, 2012, 32(2): 189. [11] WANG Chun-juan, FENG Li-hua, LUO Wei. Journal of Water Resources and Water Engineering, 2012, 23(6): 6. [12] ZHANG Yan. Water Sciences and Engineering Technology, 2014,(3): 63. [13] SHANG Shou-peng, YAO Xin-feng, YAO Xia, et al. Chinese Journal of Analytical Chemistry, 2009,(10): 1445. [14] SHI Feng, WANG Xiao-chuan, YU Lei, et al. MATLAB Neural Network Analysis of 30 Cases. Beijing: Beihang University Press, 2010. 9. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
LAN Yan1,WANG Wu1,XU Wen2,CHAI Qin-qin1*,LI Yu-rong1,ZHANG Xun2. Discrimination of Planting and Tissue-Cultured Anoectochilus Roxburghii Based on SMOTE and Inception-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 158-163. |
[5] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[6] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[7] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[8] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[9] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[10] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[11] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[12] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[13] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[14] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[15] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
|
|
|
|