Deep learning-based intelligent management for sewage treatment plants

基于深度学习的污水处理厂智能管理

It is generally believed that intelligent management for sewage treatment plants(STPs) is essential to the sustainable engineering of future smart cities.The core of management lies in the precise prediction of daily volumes of sewage.The generation of sewage is the result of multiple factors from the whole social system.Characterized by strong process abstraction ability,data mining techniques have been viewed as promising prediction methods to realize intelligent STP management.However,existing data mining-based methods for this purpose just focus on a single factor such as an economical or meteorological factor and ignore their collaborative effects.To address this challenge,a deep learning-based intelligent management mechanism for STPs is proposed,to predict business volume.Specifically,the grey relation algorithm(GRA) and gated recursive unit network(GRU) are combined into a prediction model(GRAGRU).The GRA is utilized to select the factors that have a significant impact on the sewage business volume,and the GRU is set up to output the prediction results.We conducted a large number of experiments to verify the efficiency of the proposed GRA-GRU model.

污水处理厂的智能化管理对于未来智慧城市的可持续发展至关重要,这项管理工作的核心之一在于对每日污水业务量的准确预测。从系统科学的角度而言,污水的产生是整个社会系统多种因素共同作用的结果。数据挖掘技术具有很强的抽象能力,能够用来构建实现污水处理厂智能化管理的预测方法。然而,现有的基于数据挖掘的方法只关注单一因素,如经济或气象因素,而忽略了它们的协同效应。针对这一挑战,本文提出了一种基于深度学习的污水厂智能化管理机制来预测污水业务量。将灰色关联算法(GRA)和门控递归单元网络(GRU)结合成预测模型(GRA-GRU)。利用GRA选择对污水业务量有显著影响的因素,利用GRU输出预测结果。本文进行了大量的实验来验证所提出的GRA-GRU模型的有效性。