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A Soft-sensing Technique for Wastewater Treatment Based on BP and RBF Neural Networks

机译：基于BP和RBF神经网络的污水处理软测量技术。

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摘要

With the modern industry, the water resources which are essential for human survival have been greatly destroyed. With the goal of managing wastewater effectively, economically, and ecologically, scientists have been working years for the simplicity and effectiveness of a wastewater treatment system. However, the quality parameters of wastewater treatment usually cannot be detected on line or otherwise the measurement meters are very expensive. In this paper, the soft-sensing method based on the combined back-propagation (BP) and radial basis function (RBF) neural networks is proposed to solve this problem. Wastewater treatment technique is analyzed systematically. BOD, COD, N and P which cannot be detected on-line are taken as the primary variables. ORP, DO, PH and MLSS which can be detected on-line are taken as the secondary variables. Neutral network for soft-sensing is proposed and trained using the testing data of practical treatment, processes. The simulation results show that the soft-sensing system of wastewater treatment based on BP and RBF neural networks can correctly estimate the quality parameters in real time.
机译：随着现代工业的发展，人类赖以生存的水资源被极大地破坏了。为了有效，经济和生态地管理废水，科学家们多年来一直致力于废水处理系统的简单性和有效性。但是，废水处理的质量参数通常无法在线检测，否则测量仪非常昂贵。本文提出了一种基于反向传播（BP）和径向基函数（RBF）神经网络的软传感方法来解决这一问题。对废水处理技术进行了系统的分析。不能在线检测的BOD，COD，N和P被作为主要变量。可以在线检测的ORP，DO，PH和MLSS作为次要变量。提出并使用实际处理过程的测试数据对中性网络进行软传感。仿真结果表明，基于BP和RBF神经网络的废水处理软传感系统可以实时正确估计质量参数。

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