دورية أكاديمية
Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process.
| العنوان: | Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process. |
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| المؤلفون: | Grimm TR; Department of Statistical Science, Baylor University, Waco, Texas 76798, United States., Branch A; Carollo Engineers, Inc., Walnut Creek, California 94598, United States., Thompson KA; Carollo Engineers, Inc., Walnut Creek, California 94598, United States., Salveson A; Carollo Engineers, Inc., Walnut Creek, California 94598, United States., Zhao J; Las Virgenes Municipal Water District, Calabasas, California 91302, United States., Johnson D; Las Virgenes Municipal Water District, Calabasas, California 91302, United States., Hering AS; Department of Statistical Science, Baylor University, Waco, Texas 76798, United States., Newhart KB; Department of Geography and Environmental Engineering, United States Military Academy, West Point, New York 10996, United States. |
| المصدر: | ACS ES&T engineering [ACS ES T Eng] 2024 May 31; Vol. 4 (6), pp. 1492-1506. Date of Electronic Publication: 2024 May 31 (Print Publication: 2024). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 9918300874706676 Publication Model: eCollection Cited Medium: Internet ISSN: 2690-0645 (Electronic) Linking ISSN: 26900645 NLM ISO Abbreviation: ACS ES T Eng Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Washington, DC : American Chemical Society, [2021]- |
| مستخلص: | As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing. Competing Interests: The authors declare no competing financial interest. (© 2024 The Authors. Published by American Chemical Society.) |
| References: | Environ Sci Pollut Res Int. 2021 Aug;28(32):43544-43566. (PMID: 33834339) Comput Methods Programs Biomed. 2020 Oct;195:105536. (PMID: 32485511) Water Res. 2004 Apr;38(7):1715-32. (PMID: 15026226) ACS ES T Water. 2023 Apr 06;4(3):913-924. (PMID: 38482339) Water Res. 2021 Oct 1;204:117556. (PMID: 34481284) Water Sci Technol. 2022 Feb;85(4):1250-1262. (PMID: 35228367) J Stat Softw. 2010;33(1):1-22. (PMID: 20808728) |
| تواريخ الأحداث: | Date Created: 20240620 Latest Revision: 20240621 |
| رمز التحديث: | 20240621 |
| مُعرف محوري في PubMed: | PMC11184555 |
| DOI: | 10.1021/acsestengg.4c00042 |
| PMID: | 38899163 |
| قاعدة البيانات: | MEDLINE |
Find this issue from the American Chemical Society | تدمد: | 2690-0645 |
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| DOI: | 10.1021/acsestengg.4c00042 |