Study on intracellular polymers using near infrared spectroscopy and extreme learning machine in denitrifying phosphorus removal process
ZHANG Hua1,2, QUAN Gui-jun1,2, HUANG Jian1,2, HUANG Xian-huai1,2, YAN Sheng1,2, LIU Pei-ran1,2, LIU Hang1,2, TIAN Ji-yu1,2
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, Hefei 230601, China
In order to realize rapid determination of intracellular poly-β-hydroxybutyrate (PHB), polyphosphate (Poly-P) and glycogen (Gly) in denitrifying phosphorus removal process with near infrared spectroscopy, the calibration models (ELM models) of PHB, Poly-P, Gly were established by multiple scatter correction preprocessing and extreme learning machine algorithm. The preprocessing results showed that the multiple scattering correction can eliminate the scattering effects on the raw near infrared spectral data of PHB, Poly-P and Gly. The ELM models of PHB, Poly-P and Gly were established with preprocessed spectral data by extreme learning machine. The principal component numbers of ELM models of PHB, Poly-P and Gly were respectively 6, 6 and 7, with the nodes number of hidden layer being 18, 12 and 17 respectively. The ELM models of PHB, Poly-P and Gly showed that the correlation coefficients (rc) were respectively 0.9835, 0.9499, 0.9589, with the root mean square errors of cross validation (RMSECV) being 0.0541, 0.0579, 0.0489 respectively. The prediction results of ELM models of PHB, Poly-P and Gly indicated that the correlation coefficient (rp) were respectively 0.9683, 0.9288, 0.9488, with the root mean square errors of prediction (RMSEP) being 0.0668, 0.0776, 0.0501. It showed that ELM models of PHB, Poly-P and Gly had better prediction performance for the contents of PHB, Poly-P and Gly. This study provides a convenient method for rapid determination of PHB, Poly-P and Gly in denitrifying phosphorus removal process with near infrared spectroscopy and extreme learning machine.
张华, 全桂军, 黄健, 黄显怀, 闫升, 刘沛然, 刘航, 田纪宇. 近红外光谱和极限学习机分析反硝化除磷中胞内聚合物[J]. 中国环境科学, 2017, 37(5): 1823-1830.
ZHANG Hua, QUAN Gui-jun, HUANG Jian, HUANG Xian-huai, YAN Sheng, LIU Pei-ran, LIU Hang, TIAN Ji-yu. Study on intracellular polymers using near infrared spectroscopy and extreme learning machine in denitrifying phosphorus removal process. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(5): 1823-1830.
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