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Proceedings Paper

Research on multi-parameter detection method for environmental water quality
Author(s): Haixiu Chen; Jiangzhou Zhang; Zhishuai Li; Jie Liu; Yan Hou
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Paper Abstract

The droplet analysis technology and the detection principles of water quality parameters are combined to achieve quantitative detection of multi-parameter of water. The detection platform is designed based on fiber and capacitance droplet analysis technology, which is mainly composed of the droplet sensor, dissolved oxygen probe, liquid supply pump, photoelectric conversion elements, and the signal processing circuit. The detection of three quality parameters (refractive index, turbidity and dissolved oxygen) is carried out on this platform through experiments. For the turbidity of the water, the sample’s rainbow-peak value of the fingerprint obtained with the droplet sensor is proved to be highly correlated with turbidity. And the prediction model of turbidity is established by regression analysis method with Formazine standard solution, with he maximum relative error 3.9%. The measurement model of dissolved oxygen is researched by collecting the fluorescence signal excited by the dissolved oxygen probe and the sample’s temperature, and the performance of the BP neural network model and the regression model is compared. And it shows that BP neural network model performs better in the detection of dissolved oxygen. The measurement model of refractive index is determined through regression analysis, and the value of the rainbow-peak is selected as the key factor through the experiments with NaCl solution. The establishment of the three parameters’ detection model shows us a method to realize multi-parameter detection for environmental water quality.

Paper Details

Date Published: 13 November 2019
PDF: 6 pages
Proc. SPIE 11343, Ninth International Symposium on Precision Mechanical Measurements, 113430X (13 November 2019); doi: 10.1117/12.2548545
Show Author Affiliations
Haixiu Chen, Nanjing Univ. of Information Science and Technology (China)
Jiangzhou Zhang, Nanjing Univ. of Information Science and Technology (China)
Zhishuai Li, Nanjing Univ. of Information Science and Technology (China)
Jie Liu, Nanjing Univ. of Information Science and Technology (China)
Yan Hou, Nanjing Univ. of Information Science and Technology (China)


Published in SPIE Proceedings Vol. 11343:
Ninth International Symposium on Precision Mechanical Measurements
Liandong Yu, Editor(s)

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