Infrared detection and clustering grey fusion prediction model of water quality turbidity

Du Yuhong; Wei Kunpeng; Shi Yijun; Liu Enhua; Feng Qiyin; Dong Guangyu

doi:10.3788/IRLA201645.1028002

Volume 45 Issue 10

Dec. 2016

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Du Yuhong, Wei Kunpeng, Shi Yijun, Liu Enhua, Feng Qiyin, Dong Guangyu. Infrared detection and clustering grey fusion prediction model of water quality turbidity[J]. Infrared and Laser Engineering, 2016, 45(10): 1028002-1028002(7). doi: 10.3788/IRLA201645.1028002

Citation:

Du Yuhong, Wei Kunpeng, Shi Yijun, Liu Enhua, Feng Qiyin, Dong Guangyu. Infrared detection and clustering grey fusion prediction model of water quality turbidity[J]. Infrared and Laser Engineering, 2016, 45(10): 1028002-1028002(7). doi: 10.3788/IRLA201645.1028002

Infrared detection and clustering grey fusion prediction model of water quality turbidity

doi: 10.3788/IRLA201645.1028002

1.
School of Mechanical Engineering,Tianjin Polytechnic University,Tianjin 300387,China;
2.
Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology,Tianjin 300387,China;
3.
The Technology Center of Tianjin Zhonghuan Creative Technology Limited,Tianjin 300190

Received Date: 2016-02-14
Rev Recd Date: 2016-03-15
Publish Date: 2016-10-25

Abstract

In order to realize real-time and accurate detection of water turbidity in the water treatment process, the turbidity detection system was designed based on infrared light scattering and the turbidity forecasting model was put forward based on clustering grey fusion. The infrared light emitting diode with 890 nm wavelength was used as the light emitting device, the photosensitive diode was used as the receiver, and the response time of the detector was short, and the zero error was small. The data collected by the sensor was processed by the method of grey prediction algorithm and cluster fusion. The data processed by the cluster fusion were as the input data of the grey predictive control, and the output data of the grey predictive control and the fusion data were compared and analyzed. Data tracking and operation were carried out through the actual project. The average error of the measured value and the output value of the turbidity prediction is 0.008 7 NTU. Grey fusion algorithm is superior to the single grey prediction algorithm, to ensure that the water quality turbidity parameters are stable and meet the requirements of water quality, and ensures that the water quality turbidity parameters are more stable and meet the requirements of water quality.
- turbidity,
- infrared light,
- grey forecasting,
- clustering fusion

References

[1]	Hu Xiaoli, Yu Ming, Mo Bin, et al. Design and realization of an ultra low range turbidity sensor[J]. Transducer and Microsystem Technologies, 2014, 33(8):116-118. (in Chinese)
[2]	Wang Xin. Research and design based on PLC sewage treatment control system and the control of PH value[D]. Taiyuan:Taiyuan University of Technology, 2015. (in Chinese)
[3]	Dana D R, Maffione R A. Determining the backward scattering coefficient with fixed-angle backscattering sensors-Revisited[J]. Ocean Optics XVI, Santa Fe, New Mexico, 2002:18-22.
[4]	Song Qimin, Lu Minggang. Research on range and linearity of turbidity measurement with scattered light[J]. Journal of Shanghai University(Natural Science Edition), 1997, 3(5):564-569. (in Chinese)
[5]	Ebie Kunio, Yamaguchi Dabide, Hoshikawa Hiroshi, et al. New measurement principle and basic performance of high-sensitivity turbidimeter with two optical systems in series[J].Water Research, 2006, 40(4):683-691.
[6]	Ran Feng, Yang Hui, Huang Shuping. Design of real-time color video capture system for area array CCD[J]. Optics and Precision Engineering, 2010, 1801:273-280. (in Chinese)
[7]	Wang Qingquan, Li Xuyu, Zhang Maolin. Different turbidity fast detection technology based on CCD[J]. Instrument Technique and Sensor, 2013(1):97-101. (in Chinese)
[8]	Ji Yinglei, Cheng Feng. Research of photoelectric turbidity sensor based on LS-SVR[J]. Transducer and Microsystem Technologies, 2015, 34(1):73-75. (in Chinese)
[9]	Fei Xia, Li Wei, Zhang Shaohua, et al. Design of remote water quality monitoring system based on Infrared Technology[C]//Proceedings of the Fourteenth National Symposium on Infrared Heating and Infrared Medical Development, 2013. (in Chinese)
[10]	Wang Jundong, Qi Weigui. Prediction of river water turbidity based on EMD-SVM[J]. Acta Electronica Sinica, 2009, 37(10):2130-2133. (in Chinese)
[11]	Zhao Sihai, Li Wenchang, Li Ming, et al. Study on the method of detecting consistence of emulsion using testing light turbidity[J]. Journal of China Coal Society, 2011, 36(1):157-160. (in Chinese)
[12]	Sun Yuxia. The research of scheme and system for tiny particles concentration detection[D]. Zhejiang:Zhejiang University of Technology, 2015. (in Chinese)
[13]	Gao Jianqin, Yang Jiajian, Qian Wenjiao, et al. Optimization control of backwash termination in sand filter[J]. China Water Wastewater, 2013, 17:25. (in Chinese)
[14]	Xue Pengsong, Feng Minquan, Xing Xiaopeng. Water quality prediction model based on Markovchain improving gray neural network[J]. Engineering Journal of Wuhan University, 2012, (3):319-324. (in Chinese)
[15]	Yan An, Zhihong Zou, Yanfei Zhao. Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM(1,1) model[J]. Journal of Environmental Sciences, 2015(3):158-164.
[16]	Yu Lei, Zhou Lan, Xiong Liang. Study of asphalt pavement rutting based on clustering gray forecasting model[J]. Journal of Hefei University of Technology, 2015, 38(2):219-221. (in Chinese)
[17]	Duan Zhaolei, Gu Zhimin. Grey prediction based hot spot relief strategy in Web Cache cluster[J]. Transactions of Beijing Institute of Technology, 2010, 30(7):794-797. (in Chinese)
[18]	Zhao Mingfu, Wang Nian, Luo Binbin, et al. Simultaneous measurement of temperature and concentration of sugar solution based on hybrid fiber grating sensor[J]. Chinese Optics, 2014(3):476-482. (in Chinese)
[19]	Liu Ruipeng, Liu Qiao, Qi Zhimei. Interference-resistant turbidity detector based on measurement of scattered light power ratio[J]. Optics and Precision Engineering, 2011, 1906:1221-1227. (in Chinese)
[20]	Wang Yuqing, Wang Suojian. Quality assessment method of IR and visible fusion image[J]. Chinese Optics, 2014(3):396-401. (in Chinese)
[21]	Li Chenyang, Duan Fajie, Xu Fei, et al. Optical online detection method of oil and suspended matters in sewage[J]. Infrared and Laser Engineering, 2015, 44(11):3431-3436. (in Chinese)
[22]	Zhang Qin, Zheng Lihua, Li Minzan, et al. Portable water turbidimeter based on NIR spectroscopy[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013(S2):235, 236-240. (in Chinese)
[23]	Ma Linli, Sun Yao. Information fusion and its application in controling system fault diagnosis[J]. Infrared and Laser Engineering, 2002, 31(1):36-40. (in Chinese)
[24]	Odeberg H. Fusion sensor information using fuzzy measures[J]. Robotica, 1989, 31:217-242.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Infrared detection and clustering grey fusion prediction model of water quality turbidity

1. School of Mechanical Engineering,Tianjin Polytechnic University,Tianjin 300387,China;

2. Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology,Tianjin 300387,China;

3. The Technology Center of Tianjin Zhonghuan Creative Technology Limited,Tianjin 300190

Received Date: 2016-02-14

Rev Recd Date: 2016-03-15

Publish Date: 2016-10-25

Key words:

Abstract: In order to realize real-time and accurate detection of water turbidity in the water treatment process, the turbidity detection system was designed based on infrared light scattering and the turbidity forecasting model was put forward based on clustering grey fusion. The infrared light emitting diode with 890 nm wavelength was used as the light emitting device, the photosensitive diode was used as the receiver, and the response time of the detector was short, and the zero error was small. The data collected by the sensor was processed by the method of grey prediction algorithm and cluster fusion. The data processed by the cluster fusion were as the input data of the grey predictive control, and the output data of the grey predictive control and the fusion data were compared and analyzed. Data tracking and operation were carried out through the actual project. The average error of the measured value and the output value of the turbidity prediction is 0.008 7 NTU. Grey fusion algorithm is superior to the single grey prediction algorithm, to ensure that the water quality turbidity parameters are stable and meet the requirements of water quality, and ensures that the water quality turbidity parameters are more stable and meet the requirements of water quality.

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Reference (24)

[1]	Hu Xiaoli, Yu Ming, Mo Bin, et al. Design and realization of an ultra low range turbidity sensor[J]. Transducer and Microsystem Technologies, 2014, 33(8):116-118. (in Chinese)
[2]	Wang Xin. Research and design based on PLC sewage treatment control system and the control of PH value[D]. Taiyuan:Taiyuan University of Technology, 2015. (in Chinese)
[3]	Dana D R, Maffione R A. Determining the backward scattering coefficient with fixed-angle backscattering sensors-Revisited[J]. Ocean Optics XVI, Santa Fe, New Mexico, 2002:18-22.
[4]	Song Qimin, Lu Minggang. Research on range and linearity of turbidity measurement with scattered light[J]. Journal of Shanghai University(Natural Science Edition), 1997, 3(5):564-569. (in Chinese)
[5]	Ebie Kunio, Yamaguchi Dabide, Hoshikawa Hiroshi, et al. New measurement principle and basic performance of high-sensitivity turbidimeter with two optical systems in series[J].Water Research, 2006, 40(4):683-691.
[6]	Ran Feng, Yang Hui, Huang Shuping. Design of real-time color video capture system for area array CCD[J]. Optics and Precision Engineering, 2010, 1801:273-280. (in Chinese)
[7]	Wang Qingquan, Li Xuyu, Zhang Maolin. Different turbidity fast detection technology based on CCD[J]. Instrument Technique and Sensor, 2013(1):97-101. (in Chinese)
[8]	Ji Yinglei, Cheng Feng. Research of photoelectric turbidity sensor based on LS-SVR[J]. Transducer and Microsystem Technologies, 2015, 34(1):73-75. (in Chinese)
[9]	Fei Xia, Li Wei, Zhang Shaohua, et al. Design of remote water quality monitoring system based on Infrared Technology[C]//Proceedings of the Fourteenth National Symposium on Infrared Heating and Infrared Medical Development, 2013. (in Chinese)
[10]	Wang Jundong, Qi Weigui. Prediction of river water turbidity based on EMD-SVM[J]. Acta Electronica Sinica, 2009, 37(10):2130-2133. (in Chinese)
[11]	Zhao Sihai, Li Wenchang, Li Ming, et al. Study on the method of detecting consistence of emulsion using testing light turbidity[J]. Journal of China Coal Society, 2011, 36(1):157-160. (in Chinese)
[12]	Sun Yuxia. The research of scheme and system for tiny particles concentration detection[D]. Zhejiang:Zhejiang University of Technology, 2015. (in Chinese)
[13]	Gao Jianqin, Yang Jiajian, Qian Wenjiao, et al. Optimization control of backwash termination in sand filter[J]. China Water Wastewater, 2013, 17:25. (in Chinese)
[14]	Xue Pengsong, Feng Minquan, Xing Xiaopeng. Water quality prediction model based on Markovchain improving gray neural network[J]. Engineering Journal of Wuhan University, 2012, (3):319-324. (in Chinese)
[15]	Yan An, Zhihong Zou, Yanfei Zhao. Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM(1,1) model[J]. Journal of Environmental Sciences, 2015(3):158-164.
[16]	Yu Lei, Zhou Lan, Xiong Liang. Study of asphalt pavement rutting based on clustering gray forecasting model[J]. Journal of Hefei University of Technology, 2015, 38(2):219-221. (in Chinese)
[17]	Duan Zhaolei, Gu Zhimin. Grey prediction based hot spot relief strategy in Web Cache cluster[J]. Transactions of Beijing Institute of Technology, 2010, 30(7):794-797. (in Chinese)
[18]	Zhao Mingfu, Wang Nian, Luo Binbin, et al. Simultaneous measurement of temperature and concentration of sugar solution based on hybrid fiber grating sensor[J]. Chinese Optics, 2014(3):476-482. (in Chinese)
[19]	Liu Ruipeng, Liu Qiao, Qi Zhimei. Interference-resistant turbidity detector based on measurement of scattered light power ratio[J]. Optics and Precision Engineering, 2011, 1906:1221-1227. (in Chinese)
[20]	Wang Yuqing, Wang Suojian. Quality assessment method of IR and visible fusion image[J]. Chinese Optics, 2014(3):396-401. (in Chinese)
[21]	Li Chenyang, Duan Fajie, Xu Fei, et al. Optical online detection method of oil and suspended matters in sewage[J]. Infrared and Laser Engineering, 2015, 44(11):3431-3436. (in Chinese)
[22]	Zhang Qin, Zheng Lihua, Li Minzan, et al. Portable water turbidimeter based on NIR spectroscopy[J]. Transactions of the Chinese Society for Agricultural Machinery, 2013(S2):235, 236-240. (in Chinese)
[23]	Ma Linli, Sun Yao. Information fusion and its application in controling system fault diagnosis[J]. Infrared and Laser Engineering, 2002, 31(1):36-40. (in Chinese)
[24]	Odeberg H. Fusion sensor information using fuzzy measures[J]. Robotica, 1989, 31:217-242.

Infrared detection and clustering grey fusion prediction model of water quality turbidity

doi: 10.3788/IRLA201645.1028002

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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