DOI: https://doi.org/10.15587/2312-8372.2018.128543

Simulation of water purification machine for vending cyber physical systems

Andrii Salo

Abstract


The object of research is a water purification machine for self-service systems. The need for purified water is at the self-service washers, coffee vending machines, and water wending machines. As a rule, such systems are located in geographically scattered places. One of the most problematic places is the selection of the correct configuration of the machine to the location. Another problematic place is high maintenance costs. Most of the existing water purification machines, which are produced today, do not have a monitoring system in their composition, results in an inefficient operation of the service department. These problems lead to a decrease in the number of users of self-service systems.

To solve these problems, it is proposed to design a water purification machine that will operate as part of a 5-level vending cyber-physical system.

The structure and operating principles of the water purification machine based on the reverse osmosis membrane are described. In the course of the study, Monte Carlo simulation methods were used, which allowed to select the configuration parameters of the machine in accordance with the users' requests. Critical parameters of the equipment influencing the performance of the water purification machine are determined. Based on the simulation results, two typical configurations of the TW30-1812-100 and XLE4040 membrane-based machine are selected.

In addition, the software model of the water purification machine is integrated into the analytical system, which generates recommendatory solutions for the service department. The analytical system recommends not only the current replacement of functional units (filters, membranes), but also is able to predict the need for changing the configuration of the machine. This approach allows to optimize service routes and increase the efficiency of the service.


Keywords


vending cyber-physical system; simulation; reverse osmosis membrane; analytical system

References


Salo, A. M. (2013). Pryntsyp pobudovy vendinhovoi merezhi z monitorynhom. Visnyk NU «Lvivska politekhnika». Kompiuterni systemy ta merezhi, 773, 112–118.

Salo, A. M. (2016). Vending cyber physical systems architecture. Advances in Cyber-Physical Systems «ACPS», 1, 61–65.

Lee, J., Bagheri, B., Kao, H.-A. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. doi:10.1016/j.mfglet.2014.12.001

Kolberg, D., Zuhlke, D. (2015). Lean Automation enabled by Industry 4.0 Technologies. IFAC-PapersOnLine, 48 (3), 1870–1875. doi:10.1016/j.ifacol.2015.06.359

Melnyk, A. O. (2015). Multilevel basic cyber physical system platform. Cyber physical systems: achievements and challenges. Lviv, 5–15.

Lee, E. A., Seshia, S. A. (2017). Introduction to Embedded Systems – A Cyber-Physical Systems Approach. MIT Press, 565.

Panteleev, A. A., Ryabchikov, B. E., Khoruzhiy, O. V., Gromov, S. L., Sidorov, A. R. (2012). Tekhnologii membrannogo razdeleniya v promyshlenoy vodopodgotovke. Moscow: DeLi plyus, 429.

Wimalawansa, S. J. (2013). Purification of Contaminated Water with Reverse Osmosis: Effective Solution of Providing Clean Water for Human Needs in Developing Countries. International Journal of Emerging Technology and Advanced Engineering, 3 (12), 75–89.

Abdelwahed, S., Wu, J., Biswas, G., Ramirez, J., Manders, E.-J. (2005). Online fault adaptive control for efficient resource management in advanced life support systems. Habitation, 10 (2), 105–115. doi:10.3727/154296605774791214

Biswas, G., Mahadevan, S. (2007). A hierarchical model – based approach to systems health management. IEEE Aerospace conference. Big Sky. doi:10.1109/aero.2007.352943

Jewel vending company. Available at: http://home.ubalt.edu/ntsbarsh/ECON/Simulation.ppt. Last accessed: 01.03.2018.

Martz, E. (2017). Making the World a Little Brighter with Monte Carlo Simulation. Available at: http://blog.minitab.com/blog/understanding-statistics/making-the-world-a-little-brighter-with-monte-carlo-simulation. Last accessed: 05.03.2018.

Martz, E. (2017). Making Steel Even Stronger with Monte Carlo Simulation. Available at: http://blog.minitab.com/blog/understanding-statistics/making-steel-even-stronger-with-monte-carlo-simulation. Last accessed: 05.03.2018.

The Dow Chemical Company. Available at: https://www.dow.com/. Last accessed: 01.03.2018.

Melnyk, A., Salo, A. (2017). Cyber physical system of parking lot operation. Automatic Control and Information Technology (ICACIT’17). Cracow, 184–197.


GOST Style Citations


Salo A. M. Pryntsyp pobudovy vendinhovoi merezhi z monitorynhom // Visnyk NU «Lvivska politekhnika». Kompiuterni systemy ta merezhi. 2013. Vol. 773. P. 112–118.

Salo A. M. Vending cyber physical systems architecture // Advances in Cyber-Physical Systems «ACPS». 2016. Vol. 1. P. 61–65.

Lee J., Bagheri B., Kao H.-A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems // Manufacturing Letters. 2015. Vol. 3. P. 18–23. doi:10.1016/j.mfglet.2014.12.001 

Kolberg D., Zuhlke D. Lean Automation enabled by Industry 4.0 Technologies // IFAC-PapersOnLine. 2015. Vol. 48, No. 3. P. 1870–1875. doi:10.1016/j.ifacol.2015.06.359 

Melnyk A. O. Multilevel basic cyber physical system platform: proceedings // Cyber physical systems: achievements and challenges. Lviv, 2015. P. 5–15.

Lee E. A., Seshia S. A. Introduction to Embedded Systems – A Cyber-Physical Systems Approach. MIT Press, 2017. 565 p.

Tekhnologii membrannogo razdeleniya v promyshlenoy vodopodgotovke / Panteleev A. A. et al. Moscow: DeLi plyus, 2012. 429 p.

Wimalawansa S. J. Purification of Contaminated Water with Reverse Osmosis: Effective Solution of Providing Clean Water for Human Needs in Developing Countries // International Journal of Emerging Technology and Advanced Engineering. 2013. Vol. 3, No. 12. P. 75–89.

Online fault adaptive control for efficient resource management in advanced life support systems / Abdelwahed S. et al. // Habitation. 2005. Vol. 10, No. 2. P. 105–115. doi:10.3727/154296605774791214 

Biswas G., Mahadevan S. A hierarchical model – based approach to systems health management: proceedings // IEEE Aerospace conference. Big Sky, 2007. doi:10.1109/aero.2007.352943 

Jewel vending company. URL: http://home.ubalt.edu/ntsbarsh/ECON/Simulation.ppt (Last accessed: 01.03.2018).

Martz E. Making the World a Little Brighter with Monte Carlo Simulation. 2017. URL: http://blog.minitab.com/blog/understanding-statistics/making-the-world-a-little-brighter-with-monte-carlo-simulation (Last accessed: 05.03.2018).

Martz E. Making Steel Even Stronger with Monte Carlo Simulation. 2017. URL: http://blog.minitab.com/blog/understanding-statistics/making-steel-even-stronger-with-monte-carlo-simulation (Last accessed: 05.03.2018).

The Dow Chemical Company. URL: https://www.dow.com/ (Last accessed: 01.03.2018).

Melnyk A., Salo A. Cyber physical system of parking lot operation: proceedings // Automatic Control and Information Technology (ICACIT’17). Cracow, 2017. P. 184–197.







Copyright (c) 2018 Andrii Salo

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ISSN (print) 2664-9969, ISSN (on-line) 2706-5448