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Research on Reliability of Online Monitoring Results of Factory Discharge Pollutant SO2 Based on Regression Analysis

Concerning the SO2 pollution source monitoring, discussed and even debated all the time, is in the environmental field. Adhere to purpose of "lucid waters and lush mountains are invaluable assets", the immediate task is to establish a complete and correct QA/QC monitoring system. In China, there are a large number of online devices, for its superiority compared with the laboratory technology, that undertake tests. However, it also has to be admitted that, the online system, belonging to a non-standard, shall paid more attention to its effectiveness. In this paper, a Deming regression technique of variable error model, with unbiased correction (CSS0), constant bias correction (CSS1) and linear bias correction (CSS2) step by step, is used to fit at levels between online and its standard system. F and t, as well as χ2 distribution test are subsequently followed by for the selected CSS. Finally, under the independent identical distribution (i.i.d) condition based on the bias correction, use A* test to predict series residuals, from the correction, for its i.i.d condition. The uncertainty assessment, brought by the correction under site precision, combines the various variation to the maximum extent, and avoid the complicated correlation, is helpful for the quality assurance of the online system.

Deming’s Regression, Closeness Sum of Squares, Weighted Fitting, Residuals, sR’, A* Test

APA Style

Yang Shuo, Pan Zhiqiang, Niu Xingrong, Geng Lei, Sun Zhijing, et al. (2023). Research on Reliability of Online Monitoring Results of Factory Discharge Pollutant SO2 Based on Regression Analysis. International Journal of Mechanical Engineering and Applications, 11(3), 66-73. https://doi.org/10.11648/j.ijmea.20231103.12

ACS Style

Yang Shuo; Pan Zhiqiang; Niu Xingrong; Geng Lei; Sun Zhijing, et al. Research on Reliability of Online Monitoring Results of Factory Discharge Pollutant SO2 Based on Regression Analysis. Int. J. Mech. Eng. Appl. 2023, 11(3), 66-73. doi: 10.11648/j.ijmea.20231103.12

AMA Style

Yang Shuo, Pan Zhiqiang, Niu Xingrong, Geng Lei, Sun Zhijing, et al. Research on Reliability of Online Monitoring Results of Factory Discharge Pollutant SO2 Based on Regression Analysis. Int J Mech Eng Appl. 2023;11(3):66-73. doi: 10.11648/j.ijmea.20231103.12

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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