Development of an intelligent dynamic modelling system for the diagnosis of wastewater treatment processes

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

A framework for modelling mobile radio access networks for intelligent fault management

Postprin

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques