|
|
|
| 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 |
|
|
|
|
Abstract 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.
|
|
Received: 19 September 2016
|
|
|
|
|
|
| [1] |
Zhao W H, Zhang Y, Lv D M, et al. Advanced nitrogen and phosphorus removal in the pre-denitrification anaerobic/anoxic/aerobic nitrification sequence batch reactor (pre-A2NSBR) treating low carbon/nitrogen (C/N) wastewater [J]. Chemical Engineering Journal, 2016,302:296-304.
|
| [2] |
戴 娴,彭永臻,王晓霞,等.不同厌氧时间对富集聚磷菌的SNDPR系统处理性能的影响 [J]. 中国环境科学, 2016,36(1): 92-99.
|
| [3] |
康婷婷,王 亮,何洋洋,等.亚硝酸型反硝化除磷工艺特性及其应用 [J]. 中国环境科学, 2016,36(6):1705-1714.
|
| [4] |
Wei Z, Wang X, Li B, et al. Nitritation and denitrifying phosphorus removal via nitrite pathway from domestic wastewater in a continuous MUCT process [J]. Bioresource Technology, 2013,143(9):187-195.
|
| [5] |
Nielsen P H, Saunders A M, Hansen A A, et al. Microbial communities involved in enhanced biological phosphorus removal from wastewater—a model system in environmental biotechnology [J]. Current Opinion in Biotechnology, 2012,23(3): 452-459.
|
| [6] |
Wang Y Y, Geng J J, Peng Y Z, et al. A comparison of endogenous processes during anaerobic starvation in anaerobic end sludge and aerobic end sludge from an anaerobic/anoxic/oxic sequencing batch reactor performing denitrifying phosphorus removal [J]. Bioresource Technology, 2012,104(1):19-27.
|
| [7] |
Zeng W, Zhang J, Wang A Q, et al. Denitrifying phosphorus removal from municipal wastewater and dynamics of"Candidatus Accumulibacter"and denitrifying bacteria based on genes of ppk1, narG, nirS and nirK [J]. Bioresource Technology, 2016,207:322-331.
|
| [8] |
Pan Y W, Cheng K Y, Kaksonen A H, et al. A novel post denitrification configuration for phosphorus recovery using polyphosphate accumulating organisms [J]. Water Research, 2013,47(17):6488-6495.
|
| [9] |
余鸿婷,李 敏.反硝化聚磷菌的脱氮除磷机制及其在废水处理中的应用 [J]. 微生物学报, 2015,55(3):67-71.
|
| [10] |
吕冬梅,彭永臻,赵伟华,等.A2O-BAF工艺反硝化聚磷效果的影响因素 [J]. 中国环境科学, 2015,35(11):3266-3274.
|
| [11] |
Wang Y Y, Guo G, Wang H, et al. Long-term impact of anaerobic reaction time on the performance and granular characteristics of granular denitrifying biological phosphorus removal systems [J]. Water Research, 2013,47(14):5326-5337.
|
| [12] |
王动民,纪俊敏,高洪智.多元散射校正预处理波段对近红外光谱定标模型的影响 [J]. 光谱学与光谱分析, 2014,34(9):2387-2390.
|
| [13] |
白英奎,孟宪江,丁 东,等.利用神经网络提高偏最小二乘法的NIR多组分分析精度 [J]. 光谱学与光谱分析, 2005,25(3):381-383.
|
| [14] |
王 杰,毕浩洋.一种基于粒子群优化的极限学习机 [J]. 郑州大学学报(理学版), 2013,45(1):100-104.
|
| [15] |
Pan T, Han Y, Chen J M, et al. Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis [J]. Chemometrics & Intelligent Laboratory Systems, 2016,156:217-223.
|
| [16] |
Chen L, Yuan H F, Zhao Z, et al. A new multivariate calibration model transfer method of near-infrared spectral analysis [J]. Chemometrics & Intelligent Laboratory Systems, 2016,153:51-57.
|
| [17] |
黄 健,黄 珊,张 华,等.基于间隔偏最小二乘法短程硝化反硝化中无机盐氮的近红外光谱 [J]. 中国环境科学, 2015,35(7): 2014-2020.
|
| [18] |
Zhao M, Downey G, O'Donnell C P. Exploration of microwave dielectric and near infrared spectroscopy with multivariate data analysis for fat content determination in ground beef [J]. Food Control, 2016,68:260-270.
|
| [19] |
宋海燕,刘海芹,程 旭,等.近红外透射光谱法快速测定瓶装醋中挥发酸含量的研究 [J]. 山西农业大学学报(自然科学版), 2013,33(2):122-125.
|
| [20] |
Zhu F L, Yong H E, Shao Y N. Application of Near-Infrared Hyperspectral Imaging to Predicting Water Content in Salmon Flesh [J]. Spectroscopy & Spectral Analysis, 2015,35(1):113-117.
|
| [21] |
Páscoa R N M J, Lopo M, Santos C A T D, et al. Exploratory study on vineyards soil mapping by visible/near-infrared spectroscopy of grapevine leaves [J]. Computers & Electronics in Agriculture, 2016,127:15-25.
|
| [22] |
丁海泉,卢启鹏.近红外光谱技术在土壤养分分析中的研究进展及应用前景 [J]. 光谱学与光谱分析, 2012,32(1):88-91.
|
| [23] |
Nan Q U, Zhu M C, Dou S. Application of Near-and Mid-infrared Diffuse Reflectance Spectroscopic Techniques in Soil Analysis [J]. Journal of Instrumental Analysis, 2015,34(1): 120-126.
|
| [24] |
Pan T, Li M, Chen J. Selection method of quasi-continuous wavelength combination with applications to the near-infrared spectroscopic analysis of soil organic matter [J]. Applied Spectroscopy, 2014,68(3):263-271.
|
| [25] |
Pan T, Chen Z H, Chen J M, et al. Near-infrared spectroscopy with waveband selection stability for the determination of COD in sugar refinery wastewater [J]. Analytical Methods, 2012,4(4): 1046-1052.
|
| [26] |
Zangerlé A, Hissler C, McKey D, et al. Using near infrared spectroscopy (NIRS) to identify the contribution of earthworms to soil macroaggregation in field conditions [J]. Applied Soil Ecology, 2016,104:138-147.
|
| [27] |
Majed N, Matthäus C, Diem M, et al. Evaluation of intracellular polyphosphate dynamics in enhanced biological phosphorus removal process using raman microscopy [J]. Environmental Science & Technology, 2009,43(14):5436-5642.
|
| [28] |
Mesquita D P, Leal C, Cunha J R, et al. Prediction of intracellular storage polymers using quantitative image analysis in enhanced biological phosphorus removal systems [J]. Analytica Chimica Acta, 2013,770(3):36-44.
|
| [29] |
徐 玲,李卫华,杨 英,等.基于改进偏最小二乘法的近红外快速分析强化生物除磷污泥胞内PHA [J]. 中国环境科学, 2016, 36(5):1426-1434.
|
| [30] |
张 华,朱 菁,宋 箭,等.反硝化除磷系统中PHB红外光谱解析及其与磷去除率的相关性 [J]. 环境科学研究, 2015,28(8): 1274-1280.
|
| [31] |
Jovi? O. Durbin-Watson partial least-squares regression applied to MIR data on adulteration with edible oils of different origins [J]. Food Chemistry, 2016,213:791-798.
|
| [32] |
王动民,纪俊敏,高洪智.多元散射校正预处理波段对近红外光谱定标模型的影响 [J]. 光谱学与光谱分析, 2014,34(9):2387-2390.
|
|
|
|
