A Convolutional Neural Network-based Prediction Mechanism for Sewage Treatment. Issue 1 (March 2020)

• Record Type: Journal Article
• Authors: Zeng, Wenru; Guo, Zhiwei; Wang, Jianhui; Shen, Yu; Gao, Xu
• Is Part Of: IOP conference series. Volume 790:Issue 1(2020)
• Publication Date: 2020-03
• Subjects: Sewage treatment -- Prediction methods -- Convolutional neural network -- Data-driven model; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/790/1/012151
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Aiming at the extraordinary dependency on manual experience during the process of sewage treatment, it is required to explore intelligent prediction mechanism for sewage quality. Due to the fact that convolutional neural network (CNN) has some unique advantages for fast prediction, this paper proposes a convolutional neural network-based prediction mechanism for sewage treatment (CPST). It contains two mains parts respectively named learning process and training process. A group of experiments are managed to evaluate the efficiency of the proposed CPST, and the results demonstrate that CPST obtain the lower prediction error compared with baselines.