| 光谱学与光谱分析 |
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| 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, China2. Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Anhui Jianzhu University, Hefei 230601, China |
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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.
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Received: 2015-04-20
Accepted: 2015-10-05
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Corresponding Authors:
HUANG Xian-huai
E-mail: huangxh@ahjzu.edu.cn
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