Open Access

ARTICLE

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

Aiping Xu^1,2, Xuan Zou³, Chao Wang^2,*

1 School of Computer Science, Wuhan DongHu University, Wuhan, 430212, China
2 The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, 430074, China
3 The Second Academy of China Aerospace Science and Industry Corp., Beijing, 100000, China

* Corresponding Author: Chao Wang. Email:

Computer Systems Science and Engineering 2023, 47(1), 1043-1059. https://doi.org/10.32604/csse.2023.039645

Received 09 February 2023; Accepted 11 April 2023; Issue published 26 May 2023

Abstract

To protect the environment, the discharged sewage’s quality must meet the state’s discharge standards. There are many water quality indicators, and the pH (Potential of Hydrogen) value is one of them. The natural water’s pH value is 6.0–8.5. The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard. This paper aims to study the deep learning prediction model of wastewater’s pH. Firstly, the research uses the random forest method to select the data features and then, based on the sliding window, convert the data set into a time series which is the input of the deep learning training model. Secondly, by analyzing and comparing relevant references, this paper believes that the CNN (Convolutional Neural Network) model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers. After alternating the combination of the convolutional layer and pooling layer, all features are integrated into a full-connected neural network. Thirdly, the number of input samples of the CNN model directly affects the prediction effect of the model. Therefore, this paper adopts the sliding window method to study the optimal size. Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers. The last full-connection layer contains two layers and 64 neurons per layer. The sliding window size selects as 12. Finally, the research has carried out data prediction based on the optimal CNN deep learning model. The predicted pH of the sewage is between 7.2 and 8.6 in this paper. The result is applied in the monitoring system platform of the “Intelligent operation and maintenance platform of the reclaimed water plant.”

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