Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network. Issue 23 (1st April 2020)

• Record Type: Journal Article
• Authors: Guo, Zhiwei; Du, Boxin; Wang, Jianhui; Shen, Yu; Li, Qiao; Feng, Dong; Gao, Xu; Wang, Heng
• Is Part Of: RSC advances. Volume 10:Issue 23(2020)
• Page Start: 13410
• Page End: 13419
• Publication Date: 2020-04-01
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d0ra00736f
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : This work proposes a novel data-driven mechanism for prediction of wastewater treatment results through mixture of two neural network models. Abstract : It is widely believed that effective prediction of wastewater treatment results (WTR) is conducive to precise control of aeration amount in the wastewater treatment process (WTP). Conventional biochemical mechanism-driven approaches are highly dependent on complicated and redundant model parameters, resulting in low efficiency. Besides, sharp increase in business volume of wastewater treatment requires automatic operation technologies for this purpose. Under this background, researchers started to introduce the idea of data mining to model the WTP, in order to automatically predict WTR given inlet conditions and aeration amount. However, existing data-driven approaches for this purpose focus on modelling of the WTP at independent timestamps, neglecting sequential characteristics of timestamps during the long-term treatment process. To tackle the challenge, in this paper, a novel prediction and control framework through combination of convolutional neural network (CNN) and recurrent neural network (RNN) is proposed for prediction of the WTR. Firstly, the CNN model is utilized to automatically extract the local features of each independent timestamp in the WTP and make them encoded. Next, the RNN model is employed to represent global sequential features of the WTP on the basis of local feature encoding. Finally, we conduct a large number of experiments to verify efficiency and stability of the proposed prediction framework.