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An Ammonia Nitrogen Concentration Online Soft Measure Method based on the Neural Network

机译:基于神经网络的氨氮浓度在线软测量方法

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Aiming at the problem that the key water quality parameters in wastewater treatment processing is difficult to detect real-time accurately. An ammonia nitrogen concentration soft measure model based on the artificial neural network (ANN) is proposed in this paper, and utilizing existing data to achieve parameters detection in real-time accurately during the process of wastewater treatment processing. Firstly, parameters which are easily to detect are chosen as instrumental variables, then optimize the network's parameters by gradient descent algorithm. The experimental results show that the fitting results of the effluent ammonia nitrogen in this paper is good when the hidden neurons are 15 and the learning rate is 0.07, which achieves the detection of the effluent ammonia nitrogen in real-time accurately.
机译:针对废水处理处理中的关键水质参数的问题难以准确地检测实时。本文提出了一种基于人工神经网络(ANN)的氨氮浓度软测量模型,并利用现有数据在废水处理处理过程中准确地实现参数检测。首先,选择容易检测的参数被选为乐器变量,然后通过梯度下降算法优化网络的参数。实验结果表明,当隐藏的神经元为15并且学习率为0.07时,该纸张氨氮的拟合结果是良好的,并且学习率为0.07,这使得实时地检测污水氨氮。

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