TY - GEN
T1 - Image Processing Method to Estimate Water Quality Parameter
AU - Navarro, José Alonso Ruiz
AU - Santos López, Félix Melchor
AU - Portella Delgado, Jhon Manuel
AU - Santos de la Cruz, Eulogio Guillermo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Human settlements in rural areas face multiple sanitary challenges including water quality, the control of which is usually deemed as secondary due to the lack of materials or appropriate equipment. Hence, the measurement of water quality parameters, including the concentration of free chlorine, is extremely important. This paper proposes a sensing system for the measurement of free chlorine concentration based on the traditional colorimetric method, with N,N-diethyl-p-phenylenediamine as the reagent. The major hardware components are a dark room and a stationary digital camera. Using the proposed image processing method, the algorithm quantifies the pigmentation change and subsequently estimates the free chlorine concentration of the sample. Following this, a regression model is derived from the compiled data, which enables the evaluation of new samples in terms of free chlorine concentration. This model has an estimation error of less than 7% and a working range of 0.26 ppm to 1.50 ppm.
AB - Human settlements in rural areas face multiple sanitary challenges including water quality, the control of which is usually deemed as secondary due to the lack of materials or appropriate equipment. Hence, the measurement of water quality parameters, including the concentration of free chlorine, is extremely important. This paper proposes a sensing system for the measurement of free chlorine concentration based on the traditional colorimetric method, with N,N-diethyl-p-phenylenediamine as the reagent. The major hardware components are a dark room and a stationary digital camera. Using the proposed image processing method, the algorithm quantifies the pigmentation change and subsequently estimates the free chlorine concentration of the sample. Following this, a regression model is derived from the compiled data, which enables the evaluation of new samples in terms of free chlorine concentration. This model has an estimation error of less than 7% and a working range of 0.26 ppm to 1.50 ppm.
KW - Chlorine
KW - Image processing
KW - Model
KW - Quality
KW - Water
UR - http://www.scopus.com/inward/record.url?scp=85148037754&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-24985-3_20
DO - 10.1007/978-3-031-24985-3_20
M3 - Conference contribution
AN - SCOPUS:85148037754
SN - 9783031249846
T3 - Communications in Computer and Information Science
SP - 271
EP - 282
BT - Applied Technologies - 4th International Conference, ICAT 2022, Revised Selected Papers
A2 - Botto-Tobar, Miguel
A2 - Zambrano Vizuete, Marcelo
A2 - Montes León, Sergio
A2 - Torres-Carrión, Pablo
A2 - Durakovic, Benjamin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Applied Technologies, ICAT 2022
Y2 - 23 November 2022 through 25 November 2022
ER -