Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

doi:10.1007/s11783-021-1430-6

Front. Environ. Sci. Eng.

2021, Vol. 15

Issue (6) : 136 https://doi.org/10.1007/s11783-021-1430-6

RESEARCH ARTICLE

Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm

Qiyun Zhu¹, April Gu², Dan Li³, Tianmu Zhang¹, Lunhong Xiang¹, Miao He¹(

)

¹. School of Environment, Tsinghua University, Beijing 100084, China
². Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
³. Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China

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Abstract

• UV-vis absorption analyzer was applied in drainage type online recognition.

• The UV-vis spectrum of four drainage types were collected and evaluated.

• A convolutional neural network with multiple derivative inputs was established.

• Effects of different network structures and input contents were compared.

Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%.

Keywords Drainage online recognition UV-vis spectra Derivative spectrum Convolutional neural network

Corresponding Author(s): Miao He

Just Accepted Date: 11 March 2021 Issue Date: 13 April 2021

Cite this article:

Qiyun Zhu,April Gu,Dan Li, et al. Online recognition of drainage type based on UV-vis spectra and derivative neural network algorithm[J]. Front. Environ. Sci. Eng., 2021, 15(6): 136.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1430-6
https://academic.hep.com.cn/fese/EN/Y2021/V15/I6/136

Tab.1 Sample sites, types, and sampling areas

Fig.1 Online sampling system and UV-vis spectra testing device.

Fig.2 Schematic overview of classification system.

Fig.3 UV-vis spectra of each class. (a) the average value of each type; (b, c, d, e) average value±1 standard deviation of each type.

Fig.4 First-order derivative UV-vis spectra of each class.

Fig.5 Sample distribution in the space of the two first principal components of the original spectrum (a) and the 1st derivative spectrum (b).

Tab.2 Sensitivity and specificity of each input type, network structure, and wastewater class

Fig.6 Histogram of sewage types proportions in all observed sites.

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