Document Type
Article
Publication Date
9-23-2021
Publisher
Institute of Electrical and Electronics Engineers
Source Publication
IEEE Transactions on Instrumentation and Measurement
Source ISSN
0018-9456
Abstract
Microwave measurements and machine learning algorithms are presented to estimate metal ion concentrations in drinking water. A novel block loop gap resonator (BLGR) as a microwave probe is designed and fabricated to estimate Pb ion concentrations in city water as low as 1 ppb with an rms error of 0.18 ppb. No physical contact between the BLGR probe and the water sample allows on-site/in situ continuous detection of ppb-level metal ion concentrations. The S11 raw data (amplitude and phase) from the BLGR are used to classify and estimate metal ion concentrations using a support vector regression algorithm. The performance of the proposed method to estimate Pb concentrations in the presence of interfering metal ions (Cu 2+ , Fe 3+ , and Zn 2+ ) is also evaluated, and it is found that the average measurement error remains less than 13%.
Recommended Citation
Oh, Sangmin; Hossen, Imtiaz; Luglio, Juan R.; Justin, Gusphyl; Richie, James; Medeiros, Henry; and Lee, Chung-Hoon, "On-Site/In Situ Continuous Detecting ppb-Level Metal Ions in Drinking Water Using Block Loop-Gap Resonators and Machine Learning" (2021). Electrical and Computer Engineering Faculty Research and Publications. 675.
https://epublications.marquette.edu/electric_fac/675
ADA Accessible Version
Comments
Accepted version. IEEE Transactions on Instrumentation and Measurement, Vol. 70 (September 23, 2021): 9513909. DOI. © 2021 Institute of Electrical and Electronics Engineers. Used with permission.