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基于机器学习算法的城镇污水处理出水COD预测
Alternative TitleEffluent COD Prediction of Urban Sewage Treatment Based on Machine Learning Algorithm
李健1; 刘坚1; 于广平1,2
Department广州中国科学院沈阳自动化研究所分所
Conference Name31th Chinese Process Control Conference (CPCC 2020)
Conference DateJuly 30 - August 1, 2020
Conference PlaceXuzhou, China
Source Publication31th Chinese Process Control Conference (CPCC 2020)
2020
Pages1
Contribution Rank1
Keyword城镇污水 机器学习 支持向量机 COD预测
Abstract出水COD是衡量污水处理效果的核心指标,由于污水处理大滞后特征,其检测值对于处理过程动态调控的作用有限,需要建立快速预测出水COD的方法来指导处理过程优化控制。本文通过机器学习的方式,采用支持向量机、K近邻、决策树等算法建立回归模型,以城镇污水处理厂进水COD、pH、氨氮及曝气池DO值为基础,预测污水厂出水COD。并在广东省某城镇污水处理厂进行预测验证,通过对不同算法及相同算法不同参数预测结果的对比,得到一种有效预测城镇污水处理出水COD的方法。
Other AbstractEffluent COD is the key index to measure the effect of sewage treatment. Due to the large lag of sewage treatment, its detection value has limited effect on the dynamic control of the treatment process. Therefore, it is necessary to establish a fast prediction method of the effluent COD to guide the optimal control of the treatment process. In this paper, through machine learning, support vector machine, k-nearest neighbour, decision tree and other algorithms are used to build regression model, based on the influent COD, pH, ammonia nitrogen and DO value of aeration tank of municipal sewage treatment plant, predict effluent COD of sewage treatment plant. The prediction was carried out in a municipal sewage treatment plant in Guangdong Province, by comparing the prediction results of different algorithms and different parameters of the same algorithm, an effective prediction method of COD in municipal sewage treatment effluent is obtained.
Language中文
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/28113
Collection广州中国科学院沈阳自动化研究所分所
Corresponding Author李健
Affiliation1.广州中国科学院沈阳自动化研究所分所
2.中国科学院沈阳自动化研究所
Recommended Citation
GB/T 7714
李健,刘坚,于广平. 基于机器学习算法的城镇污水处理出水COD预测[C],2020:1.
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