Water Pollution Prevention and Control Project, 2023, 4(2); doi: 10.38007/WPPCP.2023.040205.
Qinghai Normal University, Qinghai, China
With the rapid development of industry and technology in modern society, people’s life has become more convenient. However, the industrial development and the uncontrolled discharge of pollution in the daily life of residents have also led to the increasingly serious problem of water pollution. At the same time, the further improvement of people’s living standards also makes more and more people pay more and more attention to their health, which also makes the water pollution problem more and more people pay attention to. The prevention and treatment of drinking water and domestic water pollution in the ecological environment has also become one of the focus issues in the relevant research fields. The pollutants in the existing residential water sources present a variety of situations, which also makes the prevention and treatment of water pollution a relatively difficult task at present. First, various pollutants in the water source were analyzed and classified. Then, appropriate pollution control measures were selected to ensure that the water source can be treated in a short time to meet the standard of residential water use. Therefore, a variety of emerging technologies have been applied to the prevention and treatment of water pollution, of which the intelligent identification technology is the most applicable. In the prevention and treatment mode of water source pollutants under the intelligent identification technology, multiple types of intelligent sensor equipment were usually used to collect, analyze and process the environmental information around the water source. Then, the quality of water source was analyzed automatically through Analytic Hierarchy Process (AHP), so as to select appropriate pollution treatment technology for it. In this paper, a new type of water source pollution prevention and treatment engineering device was proposed through intelligent identification technology and AHP algorithm. Through this device, pollution control can be completed automatically. Through a series of experiments, it was determined that the performance of this new type of engineering device was improved by about 24% on average compared with the existing water pollution treatment.
Water Pollution, Engineering Device, Intelligent Recognition, Analysis Algorithm
Guang Liu. Water Pollution Prevention Engineering Device Based on Intelligent Recognition. Water Pollution Prevention and Control Project (2023), Vol. 4, Issue 2: 35-43. https://doi.org/10.38007/WPPCP.2023.040205.
 Yizheng Lyu. Quantifying the life cycle environmental impacts of water pollution control in a typical chemical industrial park in China. Journal of Industrial Ecology. (2021) 25(6): 1673-1687. https://doi.org/10.1111/jiec.13149
 Kadam A. K. Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India. Modeling Earth Systems and Environment. (2019) 5(3): 951-962. https://doi.org/10.1007/s40808-019-00581-3
 Mengzhi Ji. Bacteriophages in water pollution control: Advantages and limitations. Frontiers of Environmental Science & Engineering. (2021) 15(12): 1-15. https://doi.org/10.1007/s11783-020-1378-y
 Mingjing He. Waste-derived biochar for water pollution control and sustainable development. Nature Reviews Earth & Environment. (2022) 3(7): 444-460. https://doi.org/10.1038/s43017-022-00306-8
 Li He, Juan Lu. Can regional integration control transboundary water pollution? A test from the Yangtze River economic belt. Environmental Science and Pollution Research. (2020) 27(22): 28288-28305. https://doi.org/10.1007/s11356-020-09205-1
 Ahmed Shahid, Saba Ismail. Water pollution and its sources, effects & management: a case study of Delhi. Shahid Ahmed and Saba Ismail (2018)'Water Pollution and its Sources, Effects & Management: A Case Study of Delhi'. International Journal of Current Advanced Research. (2018) 7(2): 10436-10442.
 Deletic Ana, Huanting Wang. Water pollution control for sustainable development. Engineering. (2019) 5(5): 839-840. https://doi.org/10.1016/j.eng.2019.07.013
 Ezemagu I. G. Modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network. South African Journal of Chemical Engineering. (2021) 3(5): 78-88. https://doi.org/10.1016/j.sajce.2020.11.007
 Gongming Wang. Artificial neural networks for water quality soft-sensing in wastewater treatment: a review. Artificial Intelligence Review. (2022) 55(1): 565-587. https://doi.org/10.1007/s10462-021-10038-8
 Rink, Karsten. Virtual geographic environments for water pollution control. International Journal of Digital Earth. (2018) 11(4): 397-407. https://doi.org/10.1080/17538947.2016.1265016
 Chen Tao. Natural polymer konjac glucomannan mediated assembly of graphene oxide as versatile sponges for water pollution control. Carbohydrate polymers. (2018) 202(12): 425-433. https://doi.org/10.1016/j.carbpol.2018.08.133
 Long Bui Ta. Inverse algorithm for Streeter-Phelps equation in water pollution control problem. Mathematics and Computers in Simulation. (2020) 171(5): 119-126. https://doi.org/10.1016/j.matcom.2019.12.005
 Tan Poh Ling, Fran Humphries. Adaptive or aspirational? Governance of diffuse water pollution affecting Australia's Great Barrier Reef. Water International. (2018) 43(3): 361-384. https://doi.org/10.1080/02508060.2018.1446617
 Xiaodong He, Peiyue Li. Surface water pollution in the middle Chinese Loess Plateau with special focus on hexavalent chromium (Cr6+): occurrence, sources and health risks. Exposure and Health. (2020) 12(3): 385-401. https://doi.org/10.1007/s12403-020-00344-x
 Faming Wang. A mesoporous encapsulated nanozyme for decontaminating two kinds of wastewater and avoiding secondary pollution. Nanoscale. (2020) 12(27): 14465-14471. https://doi.org/10.1039/D0NR03217D