A Study On The System Of Water Quality-monitoring And Forecasting And Transmission Of Source Water

For surface water polluted seriously, water quality deteriorated sharply, mass organics on raw water and so on recently, many nontraditional treatments would be added to diagrams of water purifications to meet the standard quality of supplied waters on lots of water plants at home. A system of water quality-monitoring and forecasting and transmission on raw water is significant in the operation of nontraditional treatments, real-time optimization and adjustment of diagram and parameters of units of water purification so that the plant run economically and the water supplied reach the relative standard.The water on Luan River reached to Tianjin has a sharply change which lots of algae can be found on later spring and summer. Plants along the rivers run awful. Three water works under Tianjin water limited company carry out different pre-treatments in order to decrease the impact of algae on units of water purification. For the quality of water supplied and the optimization of water purification's diagram, the system of water quality-monitoring and forecasting and transmission on water of Tianjin's Luan River was built under this study.1683 non- continuous diary data (1997.8.12-2000.10.3, and 2001.5.1-2002.10.16) about water qualities of Yixingfu pump station where is to the pre- sedimentation tank of Jieyuan water work as one of three plants were collected for water quality model about forecasting the flourishing algae. Since the frequency of chlorophyll measured is the highest on all water parameters and the intervened data of chlorophyll measured is the lowest, chlorophyll would be an indicative parameter of algae. The growth of algae is so complex and the linear model is so simple that the best method for forecasting model about the intensity of chlorophyll is the artificial neural network. The input parameters were selected via analyzing linear correlation, grey correlation, clustering, passaging and the principle of algae growth. According to inputs, forecasting models were divided into 5 groups. Lots of mathematic tests on the forecasting model that the inputs were selected by grey correlation were made so that the preferences ofthe neural network model, for instance: mode topology, input delay and so on, were chosen, then the preferences were applied on other model and the optimized artificial neural network was selected among these models, which inputs were chosen by clustering. Its topology, delay, and inputs are 6-3-3-1, 6 and chlorophyll, turbidity, ammonia, water temperature, pH, total alkalinity. The trained model can simulate the volume of chlorophyll the third day by all data 6 days before. The forecasts accurate can reach 80% and meet user's needs.The system of real-time monitoring on raw water quality was built on the pre-sedimentation tank where the source water of two works comes from at Xihe by author. The automatic instruments were chosen based on the investigation about the measuring principals and current productions. After I have studied the corresponding software and hardware about the system of data collecting, the system was design and install elaborately, then the software on real-time monitoring water quality was programmed on LabView, all these can have the system run reliably.The remote virtual instrument on source water quality was built economically and reliably via data transmission on GPRS and the Datasocket technique on LabView. At present, Jieyuan water work can monitor real-time its source water and collect, save and treat the monitored water quality data so that the frequency of measurement manually on raw water can be largely decreased. All these ends would be an solid base for the system of source water warning next stage.