Neural networks for prediction of wastewater treatment plant influent disturbances

C. Kriger, R. Tzoneva
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引用次数: 13

Abstract

In order to develop an effective control strategy for the activated sludge process (ASP) of a wastewater treatment plant, an understanding of the nature of the influent load disturbances to the wastewater treatment plant is necessary. The wastewater treatment processes are dynamic and the interrelationships between variables are very complex. The values of the influent disturbances are usually measured off-line in a laboratory, as there are still no reliable on-line sensors available. This work proposes development of a neural network model for prediction of the values of the influent disturbances, which ultimately affect the activated sludge process. Three different dynamic multilayer perceptron feed-forward neural network models and three recurrent neural networks are developed for the prediction of the influent disturbances of chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN) and flowrate respectively. The predictive performance of the multi-layer perceptron is compared to that of the recurrent neural network.
用于污水处理厂进水扰动预测的神经网络
为了对污水处理厂的活性污泥过程(ASP)制定有效的控制策略,有必要了解污水处理厂进水负荷扰动的性质。污水处理过程是动态的,变量之间的相互关系非常复杂。进水扰动的值通常在实验室离线测量,因为仍然没有可靠的在线传感器可用。这项工作提出了一种神经网络模型的发展,用于预测最终影响活性污泥过程的进水扰动值。建立了三种不同的动态多层感知器前馈神经网络模型和三种递归神经网络模型,分别用于化学需氧量(COD)、总凯氏定氮(TKN)和流量的进水扰动预测。将多层感知器的预测性能与递归神经网络进行了比较。
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