Deep Learning-based Management For Wastewater Treatment Plants Under Blockchain Environment
Keyi Wan, Zhiwei Guo, Jianhui Wang, Wenru Zeng, Xu Gao, Yu Shen, Keping Yu · 2020
Smart management for sewage treatment plants has always been a hot issue. It is generally implemented on the basis of a data scheduling platform, in which intelligent algorithms can be embedded. The most essential problem for such management is to predict daily business volumes, including amount and quality of wastewater. To achieve a comprehensive perspective, the generation of wastewater is viewed as collaborative effect of multiple factors in social system. This paper proposes a deep learning-based management for sewage treatment plants. Specially, it combines two classical neural network models to construct a hybrid model for precise prediction of business volumes. At last, a set of experiments are carried out to assess the proposed management mechanism. Results reveal that it performs better than general baselines.