Energy and Materials-Saving Management via Deep Learning for Wastewater Treatment Plants

IEEE Access

Usman Tariq¹  Md. Jalil Piran²  Keyi Wan³  Xuhong Cheng³  Yu Shen³  Xu Gao³  Jianhui Wang³  Zheng Wen⁴ 

[1] College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia;Computer Engineering Department, Sejong University, Seoul, South Korea;National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China;School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan;

关键词: Wastewater treatment;    energy and material-saving;    deep learning;    optimal management;    genetic algorithm;   

DOI  :  10.1109/ACCESS.2020.3032531

来源: DOAJ

【 摘 要 】

With the increasing public attention on sustainability, conservation of energy and materials has been a general demand for wastewater treatment plants (WWTPs). To meet the demand, efficient optimal management and decision mechanism are expected to reasonably configure resource of energy and materials.In recent years, advanced computational techniques such as neural networks and genetic algorithm provided data-driven solutions to overcome some industrial problems. They work from the perspective of statistical learning, mining invisible latent rules from massive data. This paper proposes energy and materials-saving management via deep learning for WWTPs, using real-world business data of a wastewater treatment plant located in Chongqing, China. Treatment processes are modeled through neural networks, and materials cost that satisfies single indexes can be estimated on this basis. Then, genetic algorithm is selected as the decision scheme to compute overall cost that is able to simultaneously satisfy all the indexes. Empirically, experimental results evaluate that with the proposed management method, total energy and materials cost can be reduced by 10%-15%.

【 授权许可】

Unknown