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Study on the Influence of Wavelength and Low Temperature on COD Detection by Ultraviolet Spectroscopy |
LI Xin1, SU Cheng-zhi1,2*, YU Dan-yang1, SHENG Yu-bo1, CHANG Chuan1, SHI Lei1, JIANG Ji-guang1 |
1. College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022, China
2. Institute of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China |
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Abstract COD represents the degree of water pollution by reducing substances, compared with the traditional method to detect COD, the detection time is long and the operation is complicated. Ultraviolet spectroscopy has become a mainstream detection method due to its fast detection speed and no need for chemical reagents. Based on the Lambert Beer law, using potassium hydrogen phthalate powder to prepare standard solution as an experimental object, aiming at the detection accuracy of COD ultraviolet spectrum at low temperature, the optimal detection wavelength of COD and the influence of temperature on the detection value of COD were studied respectively. At the same time, the surface water in a certain area of Changchun City is selected as the research object to verify the applicability of the best COD detection wavelength in the actual water sample and the accuracy of the temperature compensation model. When studying the influence of detection wavelength on COD detection value, choose A256,A266,A276,A286,A296 five wavelengths to regression analysis samples, including A256,A266,A276,A286,A296 is A wavelength of 256, 266, 276, 286 and 296 nm absorbance, the absorbance of A linear regression with the COD standard solution, can be seen from the fitting data 276, 286 and 296 nm model representative, 286 nm in fitting out the best effect, 296 nm, Finally, it is 276 nm, of which the correlation coefficient r of 286 nm is 0.994 6 and the determination coefficient R2 is 0.989 4,SSE=0.011 4 and RMSE=0.037 7 at 296 nm, but the determinant coefficient R2 is low. It can be seen that 286 nm is the highest correlation and the smallest error. The results show that 286 nm is also suitable for the detection of actual water samples, and 286 nm is the best detection wavelength. When studying the influence of temperature on COD detection value, UV absorption spectra of COD water samples and standard water samples were collected at different temperatures. The results show that the UV absorption of COD solution increases with the increase of temperature. After thoroughly studying the spectral absorption of the actual and standard water samples with the same concentration of COD at the standard temperature (20 ℃), in order to eliminate the influence of temperature on COD measurement, a temperature compensation model was established by the least square method. The accuracy of the temperature compensation model is verified by the actual water sample, and the error analysis is carried out at the same time. The results show that the maximum relative error between the actual value of COD and the compensated value is 6.38%, the minimum relative error is 0.63%, and most of the relative errors are concentrated in 4%, which shows that the fitting effect of the model is good. Thus, COD temperature compensation model has high compensation accuracy and good effect. Finally, the conclusion is drawn that the best wavelength and temperature compensation model selected for COD detection can effectively improve the accuracy of COD low temperature detection.
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Received: 2019-07-19
Accepted: 2019-12-06
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Corresponding Authors:
SU Cheng-zhi
E-mail: Chengzhi_su@126.com
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[1] WANG Li-qun, ZHANG Ming, LIN Feng-mei, et al(王立群, 张 明, 林丰妹, 等). Chemical Analysis and Meterage(化学分析计量), 2018, 27(3): 113.
[2] SONG Jian-jun, ZHAO Ling(宋建军, 赵 凌). Transducer and Microsystem Technologies(传感器与微系统), 2018, 37(5): 30.
[3] Li J, Tong Y, Guan L, et al. Optik, 2018, 174: 591.
[4] LIU Fei, DONG Da-ming, ZHAO Xian-de, et al(刘 飞, 董大明, 赵贤德, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(9): 2724.
[5] YANG Xiao-min, SU Wen-hong, HUANG Xu-bing, et al(杨晓敏, 苏文洪, 黄旭冰, 等). City and Town Water Supply(城镇供水), 2019,(1): 34.
[6] Hadiyanto, Silviana, PAdetya N, et al. Journal of Physics: Conference Series, 2019, 1217: 012052.
[7] YANG Xiao-rong, JIANG Tao, CHENG Ying, et al(杨孝容, 江 滔, 成 英, 等). Journal of Environment and Health(环境与健康杂志), 2018, 35(6): 551.
[8] LI Meng-fei, YAO Meng(李萌飞, 姚 梦). Journal of Yangtz University·Natural Science Edition(长江大学学报·自科版), 2018, 15(13).
[9] YANG Hong, ZHAO Li(杨 虹,赵 莉). Tianjin Science & Technology(天津科技),2015,42(6):19.
[10] XU Yi-gang, LI Qing, WU Yi, et al(徐熠刚,李 青,吴 轶,等). Computer Measurement & Control(计算机测量与控制),2017,25(11):311
[11] YANG Yun-kai, HE Sheng-hui, YUAN De-fang, et al(杨云开,何胜辉,元德仿,等). Intelligent City(智能城市),2019,5(16):140.
[12] LI Wen, JIN Xu, ZHANG Zhi-yong, et al(李 文,金 旭,张志永,等). Laser & Optoelectronics Progress(激光与光电子学进展),2019,56(13):131201.
[13] Zhou Kunpeng, Bi Weihong, Zheng Qihang, et al. Optoelectronics Letters, 2016, 12(6): 461. |
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