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Study Progress Analysis of effluent quality prediction in Activated Sludge Process Based on CiteSpace

Journal of Water Resource and Protection 2024年第6期16卷 450-465页

作者： Kemeng Xue Beijing No.4 High School International Campus Beijing China

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

关键词： Biological Model effluent quality prediction Activated Sludge Process CiteSpace Knowledge Map Co-Citation Cluster Analysis

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Selective Ensemble Extreme Learning Machine Modeling of effluent quality in Wastewater Treatment Plants

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International Journal of Automation and computing 2012年第6期9卷 627-633页

作者： Li-Jie Zhao 1,2 Tian-You Chai 2 De-Cheng Yuan 1 1 College of Information Engineering,Shenyang University of Chemical Technology,Shenyang 110042,China 2 State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110189,China College of Information Engineering Shenyang University of Chemical Technology Shenyang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment *** to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality *** learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning *** extreme learning machine models overcome variations in different trials of simulations for single *** ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization *** proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,*** results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.

关键词： Wastewater treatment process effluent quality prediction extreme learning machine selective ensemble model genetic algorithm.

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