Development And Research Of Online Monitoring Device For Cyanobacteria Concentration

In recent years,the outburst of cyanobacterial bloom in Taihu Lake has caused serious damage to the ecological environment,and has caused great harm to the lives and health of residents.Therefore,it is of great significance to develop a real-time cyanobacterial concentration monitoring device to monitor the concentration of cyanobacterial and to provide the real-time monitoring information for supervision.At present,most of the cyanobacterial concentrations is detected with a variety of instruments to analyze the results in the laboratory through outdoor sampling.This method is inefficient,and it is difficult to meet the demand for real-time monitoring of cyanobacterial concentration.This article explores the principle of fluorescence detection,and designs the optical path structure,signal processing circuit and control system,and develops an on-line monitoring device for cyanobacterial concentration.Through experimental analysis,the device has a detection range of 0 to 400 mg/L for the concentration of cyanobacteria,and real-time detection of actual water samples with an error within 10%.The research results mainly include the following aspects:(1)According to the fluorescence effect of phycocyanin in cyanophyceae of Taihu Lake,a fluorescence spectrophotometer is used to analyze the fluorescence of long-lived phycocyanin from Taihu Lake.The luminescence wavelength is 660 nm when the excitation wavelength is 610 nm.According to the Lambert-Beer law,when the excitation light intensity and wavelength are constant,the fluorescence intensity produced by phycocyanin is proportional to the solution concentration.It lays a theoretical foundation for the application of fluorescence method.(2)Based on the principle of fluorescence detection,an optical path structure in which the excitation light source and the fluorescence detector are arranged vertically is designed.The excitation light source selects an orange LED with a central wavelength of 610 nm and uses a photoelectric sensor to convert the fluorescence intensity into a voltage signal.Through trial and error research,the method of converting weak current to voltage is improved,and the influence of different current sizes on the detection range of cyanobacterial concentration is analyzed.A dual channel signal detection method is designed.Using the growing blue-green algae solution cultivated by the School of Environment and Civil Engineering of Jiangnan University,a linear regression model is established between the cyanobacterial concentration and the measured voltage of the device.(3)In the actual cyanobacteria water sample detection process,the standard deviation is occasionally greater than 10%.The formation principle of optical path error is analyzed.The BP optical network algorithm is used to establish the corresponding optical path error software compensation model.The experimental results show that the BP neural network model effectively improves the accuracy of device detection.(4)Based on the GPRS network wireless communication technology,a remote monitoring platform for cyanobacterial concentration detection devices is established to realize real-time uploading and preservation of detection data of cyanobacterial concentration detection devices,which provided guarantee for remote monitoring of devices and query and analysis of historical data for cyanobacterial concentration.