农村污水处理设施软传感器的设计与应用

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Bing Li, Siyuan Mao, Tuo Tian, Huaibin Bi, Yuxin Tian, Xueyan Ma, Yong Qiu
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引用次数: 0

摘要

近年来,随着人们对水质监测需求的不断增长,软测量传感器逐渐受到人们的关注,它克服了传统测量方法成本高、时间长等缺点。本研究开发了一种基于机器学习的软监测传感器,可同时监测COD、NH4+-N、NO3—N、PO43—P四项水质指标。首先,开发了专门的实验设备和校准方法,生成了收集了94,000多个数据点的匹配数据集。其次,构建多元线性回归、Ridge回归、AdaBoost回归、决策树回归和Bagging回归5种回归模型并进行比较。模型的学习精度在0.8860 ~ 0.9999之间,其中Bagging回归的预测值与真实值拟合度较高。随后,采用模糊分级法减少预测误差,在效率和精度之间取得平衡。最后,将所设计的软传感器于2020年9月至10月在中国常州的三个监测点进行了实时监测,结果证明了软传感器在实际应用中的可行性。本研究提供了一种快速、准确的水质测量方法,对农村污水处理设施的管理具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design and application of soft sensors in rural sewage treatment facilities
Abstract Recently with the growing demand for water quality monitoring, soft measurement sensors have drawn public attention, which can overcome the drawbacks of high cost and long time needed in traditional measurement methods. In this study, a machine learning-based soft monitoring sensor was developed to simultaneously monitor four water quality indicators including COD, NH4+-N, NO3--N, PO43--P. Firstly, specialized experimental equipment and calibration methods were developed to generate a matching dataset that collected over 94,000 data points. Secondly, five models including Multiple Linear Regression, Ridge Regression, AdaBoost, Decision Tree Regression, and Bagging Regression were constructed and compared. The learning accuracy of the models ranged from 0.8860 to 0.9999, among which the predicted value of Bagging Regression is highly fit to the true value. Subsequently, the fuzzy grade method was adopted to reduce the prediction error and strike a balance between efficiency and accuracy. Finally, the designed soft sensor was used for real-time monitoring at three monitoring points in Changzhou, China from September to October 2020, and the results proved the feasibility of the soft sensor in practical application. This study provided a fast and accurate method for water quality measurement, which is of great significance for the management of rural sewage treatment facilities.
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来源期刊
CiteScore
4.10
自引率
21.10%
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审稿时长
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