Abstract
In this paper mathematical models were created using Artificial Neural Network (ANN) for designing the thickener area of the clarifier by correlating the process control parameters, including the mean cell residence time (θc ), initial suspended solids concentration (Co ), underflow concentration (Cu ), and recycling ratio (R). The test data were applied to the neural network for each value of θc and R. A feed-forward ANN model had been proposed to predict the performance of secondary clarifier. The training time was varied between 0.009 and 22 s. The epochs required for the trained feed-forward network varied between 100 and 500. Training specifications of the ANN model determined that the error was 1e−5 and the training data required 139 epochs. The simulation results obtained by the ANN coincided well with the experimental data. This narrow band of error measured throughout the groups for the modelled parameters was an indication of the robustness of the ANN. Models such as the one developed in this study allow plant operators to assess the expected plant effluent, given the quality of the waste stream at input locations. Keywords: Activated Sludge Process, ANN, Paper and Pulp Effluent, Secondary Clarifier, Solid Flux
Highlights
1.1 BackgroundWith the ever-increasing social awareness of environmental protection issues, proper operation and control of wastewater treatment plants (WWTPs) have come under scrutiny
The artificial neural network (ANN) approach helped the WWTP engineers of the above paper manufacturing industry to be glad about the performance enhancement of the secondary clarifier, which was possible because of the optimized WWTP design
This paper presents predictive models based on the concept of ANN, a widely used application of artificial intelligence that has shown fairly a promise in a variety of applications in paper making process identification[7,11,18,33]
Summary
With the ever-increasing social awareness of environmental protection issues, proper operation and control of wastewater treatment plants (WWTPs) have come under scrutiny. Long-range predictive control algorithms are being considered by industries to improve the overall plant operability, energy efficiency, waste and effluent minimization, and control performance[1,2,3,4,5,6,7,8,9]. Several algorithms[9,11] and modeling approaches[12,13] have been developed to tackle the complex technical problems associated with the control of industrial plants. An improved performance and adherence to stringent standards for process effluents as well as minimized operational costs and environmental impacts, are needed for most WWTPs
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