Development of an intelligent dynamic modelling system for the diagnosis of wastewater treatment processes
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In the 21st Century, water is already a limited and valuable resource, in particular the limited availability of fresh water sources. The projected increase in global population from 6 billion people in 2010 to 9 billion in 2050 will only increase the need for additional water sources to be identified and used. This situation is common in many countries and is frequently exacerbated by drought conditions. Water management planning requires both the efficient use of water sources and, increasingly, the re-use of domestic and industrial wastewaters. A large body of published research spanning several decades is available, and this research study looks specifically at ways of improving the operation of wastewater treatment processes.Process fault diagnosis is a major challenge for the chemical and process industries, and is also important for wastewater treatment processes. Significant economic and environmental losses can be attributed to inappropriate Abnormal Event Management (AEM) in a chemical/processing operation, and this has been the focus of many researchers. Many researchers are now focusing on the application of several fault diagnosis techniques simultaneously in order to improve and overcome the limitations experienced by the individual techniques. This approach requires resolution of the conflicts ascribed to the individual methods, and incurs additional costs and resources when employing more than one technique. The research study presented in this thesis details a new method of using the available techniques. The proposal is to use different techniques in different roles within the diagnostic approach based upon their inherent individual strengths. The techniques that are excellent for the detection of a fault should be employed in the fault detection, and those best applied to diagnosis are used in the diagnosis section of a diagnostic system.Two different techniques are used here, namely a mathematical model and data mining are used for detection and diagnosis respectively. A mathematical model is used which is based upon the principal of analytical redundancy in order to establish the presence of a fault in a process (the fault detection), and data mining is used to produce production rules derived from the historical data for the diagnosis. A dataset from an industrial wastewater treatment facility is used in this study.A diagnostic algorithm has been developed that employs the techniques identified above. An application in Java was constructed which allows the algorithm to be applied, eventually producing an intelligent modelling agent. Thus the focus of this research work was to develop an intelligent dynamic modelling system (using components such as mathematical model, data mining, diagnostic algorithm, and the dataset) for simulation of, and diagnosis of faults in, a wastewater treatment process where different techniques will be assigned different roles in the diagnostic system.Results presented in Chapter 5 (section 5.5) show that the application of this combined technique yields better results for detection and diagnosis of faults in a process. Furthermore, the dynamic update of the set value for any process variable (presented in Chapter 5, section 5.2.1) makes possible the detection of any process disturbance for the algorithm, thereby mitigating the issue of false alarms. The successful embedding of both a detection and a diagnostic technique in a single algorithm is a key achievement of this work, thus reducing the time taken to detect and diagnose a fault. In addition, the implementation of the algorithm in the purposebuilt software platform proved its practical application and potential to be used in the chemical and processing industries.
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