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Implementation of Multifunction Integration Module for Water Quality Measurement


Affiliations
1 Industrial-Academic Convergence Research, 20 Wolpyeongsaetteum-ro, Seo-gu, Daejeon, 35226, Korea, Republic of
2 Department. of Business Administration, Youngdong University, 310 Daehak-ro, Youngdong-eup, Youngdong-gun, Chungcheongbuk-do, 29131, Korea, Republic of
 

Objectives: Currently, according to the industry due to home, industrial, sewage used in various types of facilities can pollute the water directly and indirectly. Thus, the new service to determine the safety of the user himself water is absolutely necessary. In this paper, we designed and implemented an integrated quality measurement module in order to develop an automatic water quality measuring system. Methods/Statistical analysis: To this end, we implement the pH sensors, dissolved oxygen sensors, conductivity sensors, four sensors is a water meter integrated entry of the temperature sensor. Findings: In this paper, we want to develop an automatic water quality measuring system with integrated sensor module. To this end, pH sensors, conductivity sensors, ORP sensors, temperature sensors developed a module integrating sensor. The basic structure of the instrument was composed motherboards to feature that in general the data sent to the smartphone as a versatile and integrated sensor module. Through this, we developed an integration module to validate all information measurement results, regardless of water quality only by a simple application of a portable meter with a smartphone in real time and place. Improvements/Applications: In this paper, we developed an integrated water quality measurement data from the sensor module to receive further technology transfer to smart devices. In addition, by utilizing the measured data, we designed an algorithm for water quality analysis, and calculation results.

Keywords

Conductivity Sensor, Integration Module, pH Sensor, ORP Sensor, Temperature Sensor Water Quality Analysis Algorithms.
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  • Implementation of Multifunction Integration Module for Water Quality Measurement

Abstract Views: 125  |  PDF Views: 0

Authors

Bong-Hyun Kim
Industrial-Academic Convergence Research, 20 Wolpyeongsaetteum-ro, Seo-gu, Daejeon, 35226, Korea, Republic of
Sang-Young Oh
Department. of Business Administration, Youngdong University, 310 Daehak-ro, Youngdong-eup, Youngdong-gun, Chungcheongbuk-do, 29131, Korea, Republic of

Abstract


Objectives: Currently, according to the industry due to home, industrial, sewage used in various types of facilities can pollute the water directly and indirectly. Thus, the new service to determine the safety of the user himself water is absolutely necessary. In this paper, we designed and implemented an integrated quality measurement module in order to develop an automatic water quality measuring system. Methods/Statistical analysis: To this end, we implement the pH sensors, dissolved oxygen sensors, conductivity sensors, four sensors is a water meter integrated entry of the temperature sensor. Findings: In this paper, we want to develop an automatic water quality measuring system with integrated sensor module. To this end, pH sensors, conductivity sensors, ORP sensors, temperature sensors developed a module integrating sensor. The basic structure of the instrument was composed motherboards to feature that in general the data sent to the smartphone as a versatile and integrated sensor module. Through this, we developed an integration module to validate all information measurement results, regardless of water quality only by a simple application of a portable meter with a smartphone in real time and place. Improvements/Applications: In this paper, we developed an integrated water quality measurement data from the sensor module to receive further technology transfer to smart devices. In addition, by utilizing the measured data, we designed an algorithm for water quality analysis, and calculation results.

Keywords


Conductivity Sensor, Integration Module, pH Sensor, ORP Sensor, Temperature Sensor Water Quality Analysis Algorithms.



DOI: https://doi.org/10.17485/ijst%2F2016%2Fv9i41%2F125089