The application of artificial intelligence techniques to the operation of water and wastewater treatment plants in recent years is reviewed. The expert system approach is the most prevalent, but difficulties in acquir...
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The application of artificial intelligence techniques to the operation of water and wastewater treatment plants in recent years is reviewed. The expert system approach is the most prevalent, but difficulties in acquiring and representing knowledge of the complex phenomena in these plants have led to the search for additional approaches. Fuzzy logic and statistical processcontrol are used for formulating expert rules from plant historical operating data, but artificial neural networks, which can learn from examples, are believed to be a better solution for this task and for many additional problems encountered in the operation of the plants. Basic concepts of neural network organization and training are given as well as recent advances in learning speed improvement that have paved the way for easy application of this technique in large industrial plants. Current and future utilization of neural networks in areas of water and wastewater plant modelling, expert rule extraction, fault detection and diagnosis, plant and instrument monitoring, dynamic forecasting, and robust control are discussed. Examples are given from the application of neural networks to the operation of the Shafdan wastewater treatment plant in israel. Some limitations of the neural network approach, together with ways of overcoming these limitations, are described. The overall conclusion is that we will soon see neural network techniques applied to achieve better plant operation.
The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, deriving of causal relatio...
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ISBN:
(纸本)078034054X
The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, deriving of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANN. Example of the application of these techniques is given of the faulty LED display benchmark. References of the application of these techniques are given of diverse large scale ANN models of industrial processes.
Artificial neural networks (ANN) are used for modeling of industrial processes. However, most of the published papers deal with small or medium scale systems. One of the possible reasons, the slow learning or non conv...
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Artificial neural networks (ANN) are used for modeling of industrial processes. However, most of the published papers deal with small or medium scale systems. One of the possible reasons, the slow learning or non convergence of large scale networks can now be overcome by the use of non-developed ANN process model may be optimized, after the elimination of non-relevant input and hidden-layer "neurons". Causal relationships may be extracted from the ANN process model. This paper describes the experience acquired using these algorithms during the last six years in developing ANN models of industrial plants. Examples are given of an activated-sludge urban wastewater treatment plant and a batch reactor for the production of organic chemicals.
The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relat...
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The paper describes the development and application of several techniques for knowledge extraction from trained ANN models, such as the identification of redundant inputs and hidden neurons, derivation of causal relationships between inputs and outputs, and analysis of the hidden neuron behavior in classification ANNs. An example of the application of these techniques is given of the faulty LED display benchmark. References of the application of these techniques are given of diverse large scale ANN models of industrial processes.
Response data from a novel micro-hotplate gas sensor were used to test a robust classification technique based on an artificial neural network. Four different compounds were identified even when the sensor signal was ...
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Response data from a novel micro-hotplate gas sensor were used to test a robust classification technique based on an artificial neural network. Four different compounds were identified even when the sensor signal was corrupted by high levels of noise and drift. Additional verification rules in doubtful cases were provided by examining unique binary patterns of outputs of the hidden layer neurons. Similarities to proposed models of the human nose are noted.
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