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Research on Clustering Algorithm of Sewage Treatment in Anomaly Detection

Author: HuangZuo
Tutor: SuYong
School: Jiangsu University of Science and Technology
Course: Applied Computer Technology
Keywords: Clustering algorithm CLARANS algorithms Genetic algorithm Datapreprocessing Sewage treatment
CLC: X832
Type: Master's thesis
Year: 2013
Downloads: 1
Quote: 0
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Abstract


China’s water resources pollution shocking, this aggravated the shortage of freshwater at the same time also affects human health. Effectively deal with sewage treatmentprocess, become an urgent demand to reduce energy consumption. At present, the sewagetreatment process mainly depends on the knowledge of expert system in monitoring fault,and the key to building a knowledge system is the establishment of the knowledge base,which depends on the experience of expert and operator, therefore there are somelimitations: when abnormal state happened during operation, only a small number ofexperienced operator to maintenance. Therefore, it is proposed that the sewage treatmentprocess anomaly detection based on data mining technology, this will improve theoperating efficiency of the sewage treatment plant and the management level. In this study,cluster analysis technology as the theory basis, giving the Twi-CLARANS clusteringalgorithm which has higher clustering efficiency. Combined with genetic algorithmoptimization, the optimization algorithm is applied to find abnormal data in monitoringdata of the sewage treatment plant, further more draw fault rules and prompt the factorytake the right measures to restore normal as soon as possible.Content and results of this study are as follows:1. Through comparing various clustering algorithm with characteristics, theapplication range and the core idea, to select the CLARANS algorithm as a prototype andclassificate the abnormal data of sewage treatment plant.2. The paper put forward an improved clustering algorithm based on traditionalCLARANS algorithm and grid:grid-based secondary CLARANS algorithm. Comparedwith the traditional CLARANS clustering with selecting initial node randomly from all thedata, the improved algorithm select from the dense grid. It can improve the algorithm ofexecution time and avoid falling into local solution. In addition, select the first optimalsolution as the initial node of second CLARANS clustering which can reduce thepossibility of isolated point ignored, ensure the integrity of the data samples. After a lot ofdata simulation tested, experiment data can realize highly gathered together, and at thesame time, improve the efficiency of clustering.3. Twi-CLARANS clustering algorithm has higher efficiency and quality, butprinciple of classification algorithm is a kind of local search mechanism, which makes it limited. So combined with genetic algorithm optimization to make up the limitations of theTwi-CLARANS.Research shows that combined Twi-CLARANS algorithm with genetic optimizationcan find abnormal objects from the monitoring data of the sewage treatment plant. What’smore, according to the abnormal object’s properties could grouped into fault rules, so thatthe sewage treatment plant can take corresponding measures to improve operationefficiency.

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CLC: > Environmental science, safety science > Environmental Quality Assessment and Environmental Monitoring > Environmental monitoring > Water quality monitoring
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