Seyed Kamaleddin MASHHADI MOUSAVI, Hamid YADOLLAHI, Abbas MASHHAD MARVIAN

Design and manufacture of TDS measurement and control system for water purification in reverse osmosis by PID fuzzy logic controller with the ability to compensate effects of temperature on measurement

Measurement and control of TDS (total dissolved solids) of water has significant importance for industry, agriculture, livestock, and health. The system designed and manufactured in this paper measures, displays, and controls the TDS of water by installation on domestic and industrial purification systems. Technology of manufacturing the TDS measurement system is such that it can compensate for the adverse effect of temperature. Calibration of the measurement system will be explained here by discussion of several examinations conducted at the Research Institute of Food Science and Technology of Iran. In order to control TDS, a mathematical model for domestic water purification devices of reverse osmosis (RO) is obtained using practical experiments. Then the controller is designed by Ziegler Nichols oscillation and fuzzy logic methods and, after the comparison, a fuzzy logic controller is selected because it shows better response. This controller is implemented using regression analysis, which is a very good method for implementation of fuzzy logic controllers. Operation of controlling the TDS is done through real-time and continuous controlling of a mix valve. This valve is closed in parallel with a membrane filter or RO, so one can control TDS by adding or reducing inlet water into the outlet water of this filter. The system proposed in this research is tested in pilot and has received scientific approval from the relevant authorities

PDF

___

[1] Nollet LML. Handbook of Water Analysis. Boca Raton, FL, USA: CRC Press, 2007.
[2] Aboabboud M, Elmasallati S. Potable water production from seawater by the reverse osmosis technique in Libya. Desalination 2007; 2: 12-23.
[3] Wagner J. Membrane Filtration Handbook: Practical Tips and Hints. Minnetonka, MN, USA: Osmonics, 2001.
[4] Atekwana EA, Atekwana EA, Rowe RS, Werkema DD Jr, Legall FD. The relationship of total dissolved solids measurements to bulk electrical conductivity in an aquifer contaminated with hydrocarbon. J Appl Geophys 2004; 56: 281-294.
[5] Keramati H, Mahvi AH, Abdulnezhad L. The survey of physical and chemical quality of Gonabad drinking water in spring and summer of 1386. Quarterly of the Horizon of Medical Sciences 2007; 13: 25-32.
[6] Thirumalini S, Kurian J. Correlation between electrical conductivity and total dissolved solids in natural waters. Malaysian Journal of Science 2009; 28: 55-61.
[7] Hayashi M. Temperature-electrical conductivity relation of water for environmental monitoring and geophysical data. Environ Monit Assess 2004; 96: 119-128.
[8] ˚Astr¨om KJ, Wittenmark B. Computer Controlled Systems: Theory and Design. Upper Saddle River, NJ, USA: Prentice Hall, 1984.
[9] McFall CW, Bartman A, Christofides PD, Cohen Y. Control of a reverse osmosis desalination process at high recovery. In: American Control Conference; 2008; Seattle, WA, USA.
[10] Abbas A. Model predictive control of a reverse osmosis desalination unit. Desalination 2006; 194: 268-280.
[11] Babaei Semiromi F, Hassani AH, Torabian A, Karbassi AR, Hosseinzadeh Lotfi F. Water quality index development using fuzzy logic: a case study of the Karoon River of Iran. Afr J Biotechnol 2011; 10: 10125-10133.
[12] Li H, Zhang L, Cai K, Chen G. An improved robust fuzzy-PID controller with optimal fuzzy reasoning. IEEE T Syst Man Cyb 2005; 35: 1283-1294.
[13] Guzelkaya M, Eksin I, Yesil E. Self-tuning PID-type fuzzy logic controller coefficients via relative rate observer. Eng Appl Artif Intel 2003; 16: 227-236.