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Application of artificial neural networks in wastewater treatment

机译:人工神经网络在废水处理中的应用

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In the past few years, artificial neural networks (ANNs) have been used in describing and modelling wastewater treatment processes. Artificial neural network models can be identified without a detailed knowledge of the kinetics of the system to be modelled. Also, ANN models can potentially contain a great deal of information about the system itself, including the same type of information contained in conventional deterministic models. The fact that these models can be continuously updated with minimal resource requirements makes them very attractive for application in a real-time control scenario. In the current paper, applications of ANNs in the field of wastewater treatment performance prediction are reviewed. In addition, this paper presents a case study that reports some comprehensive modelling work to develop nonlinear neural network prediction models for the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest sewage treatment facility in Edmonton, Alberta.
机译:在过去的几年中,人工神经网络(ANN)已用于描述和建模废水处理过程。无需详细了解要建模的系统动力学,就可以识别出人工神经网络模型。而且,人工神经网络模型可能包含有关系统本身的大量信息,包括常规确定性模型中包含的相同类型的信息。这些模型可以用最少的资源需求进行连续更新,这一事实使它们对于实时控制场景中的应用非常有吸引力。本文综述了人工神经网络在污水处理性能预测领域的应用。此外,本文还提供了一个案例研究,报告了一些综合建模工作,以开发用于艾伯塔省埃德蒙顿最大的污水处理厂金条污水处理厂(GBWWTP)的非线性神经网络预测模型。

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