Modeling and control of an aeration tank using artificial neural network
In recent years Industrial processes produce a variety of waste water pollutants, some are difficult and/or costly to treat. Waste water characteristics and level of pollutants vary significantly by industry. The task of various waste water control system is to adjust the amount of impurities in the...
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| Format: | Thesis |
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2010
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| Summary: | In recent years Industrial processes produce a variety of waste water pollutants, some are difficult and/or costly to treat. Waste water characteristics and level of pollutants vary significantly by industry. The task of various waste water control system is to adjust the amount of impurities in the process stream into the defined acceptable discharge range Many uncertain factors affect the operation of Wastewater Treatment Plants. Due to the complexity of biological wastewater treatment processes, classical methods show significant difficulties when trying to control them automatically. Consequently soft computing techniques and, specifically, Artificial Neural Network appears to be a good candidate for controlling these ill-defined, time-varying and non-linear systems. This paper describes the development and implementation of a Artificial Neural Network Controller to regulate the dissolve oxygen in an aeration tank. The main goal of this control process is to save energy without decreasing the quality of the effluent discharged. The simulation results proved that Neural Network is a good tool for controlling the aeration of the wastewater treatment plant. |
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