Conventional drinking water treatment requires the removal of suspended solids by means of coagulation, flocculation and sedimentation. Treating water with coagulating chemicals is costly and complex, and at present water companies must rely on the skills and intuitions of water treatment experts to achieve good results, usually in conjunction with jar testing. A mathematical model would allow this process to be optimized and, potentially, automated. Our research focused on optimizing ferric sulfate and Clar+Ion dosages to achieve a given level of top-of-filter turbidity at a conventional treatment plant. We obtained four years of data on the treatment of reservoir water from a local northern Kentucky utility, where these two coagulants are regularly used. The data consisted of daily records of influent turbidity, coagulant dosages, temperature, pH, alkalinity, hardness, amount treated water, and top-of-filter (output) turbidity. Our goal was to predict top-of-filter turbidity based on the values of these other variables. We developed models to address this problem from two sides. An essentially linear regression model (with one nonlinear term) provided a good fit for the data, and revealed that influent turbidity, temperature, ferric sulfate and Clar+Ion dosages were significant variables while pH, alkalinity, hardness and amount of treated water were not significant. We next constructed a feed-forward nonlinear neural network model that used the back-propagation algorithm to discover relationships between these four input variables and output turbidity. A variety of networks were studied; a typical one contained 84 input units, a hidden layer of ten units, and an output layer of ten units. All real numbers were digitized. The network was trained on a subset of data from the years 1997 through 1999, and, once trained, it successfully predicted output turbidity for the first six months of 2000, with the exception of a period of days in early spring. The spring of 2000 had much higher turbidities relative to the training data; hence the conditions of the spring 2000 were new to the model, giving us a poor fit. With increasingly strict federal and state regulations, it is essential that drinking water utilities become more efficient, educated, and automated if they are to guarantee their customers quality drinking water at the lowest price. Our multidisciplinary team of student and faculty researchers drawn from three fields (computer science, biology, and chemistry) allowed us to construct a discussion support tool for choosing optimal coagulant dosages. Includes figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 310 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 10 |
| Published : | 01/01/2001 |
