We have proposed the Intelligent Operation Support System (IOSS) for sewage plants, which can be applied to bulking prediction and control for the activated sludge process. The IOSS can provide guidance and control messages to operators on actions to take based on various kinds of integrated information including on-line process data, image signals on microorganisms, heuristics on plant control and data analyzed manually. To get on-line microorganism image information, a high resolution submerged microscope was developed and applied in a full scale plant. By combining image data with heuristics, the IOSS allows prediction of the symptoms of abnormal phenomena like bulking occurrence, and then provides ways to control the abnormalities thus decreasing the operators' work loads. Furthermore, diagnosis methods for detecting sensor troubles are proposed. Simulation results indicate the IOSS is effective.
Some artificial intelligence (AI) paradigms have been applied to water and wastewater treatment systems. An artificial neural network (ANN), which can learn historical data of a plant, provides operational guidance for plant operators, and a fuzzy system (FS) provides a framework to put operators' heuristics into practical use as fuzzy rules in a fuzzy rulebase. In application, however, the practical problems remain that the ANN is a blackbox model which is unfamiliar to plant operators, and the FS usually requires much time-consuming work by system engineers and operators for knowledge acquisition and rulebase maintenance. The authors think that integration of the paradigms can give appropriate solutions to these problems. As one method which realizes such integration, an automatic fuzzy rule extraction method using an ANN is proposed. Simulation results of the proposed method using full-scale plant data demonstrated that an FS whose rulebase was modified automatically with extracted rules had better performance than a conventional FS whose rulebase included only operators' heuristics. This effect is thought to be realized by enhancement of knowledge source with the proposed method.
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