АВТОМАТИЗАЦІЯ КЕРУВАННЯ ЕЛЕКТРИЧНИМ АВТОКЛАВОМ

Maryna Yuriivna Savchenko; Oleh Volodymyrovych Radchuk; Ihor Viktorovych Golovach

doi:10.32782/msnau.2024.1.2

Maryna Yuriivna Savchenko https://orcid.org/0000-0002-8498-3272
Oleh Volodymyrovych Radchuk https://orcid.org/0000-0002-8228-2499
Ihor Viktorovych Golovach https://orcid.org/0009-0009-5245-0963

DOI: https://doi.org/10.32782/msnau.2024.1.2

Keywords: technological parameters, electrical equipment, operating modes of sterilization of canned meat, technological process, PLC automation, food product

Abstract

The article analyzes the use of an electric autoclave with the proposed system of automated control of the technological process in the conditions of a small enterprise or a small workshop of a restaurant establishment. An analysis of the implementation of the technological process of sterilization of canned goods in an electric autoclave was carried out on the created automated stand for controlling the electric autoclave under the name "Stand for automatic control and management of technological parameters of thermal equipment". Option of the modes of operation of the programmable logic controller OWEN PR200 with an electric autoclave are considered. The principle of operation of an electric autoclave in an electric circuit diagram with a programmable logic controller is given. The procedure for connecting sensors and their types, which participate in determining the parameters of a given technological process are considered. It was determined that the initial option, which is responsible for the beginning of research, is option №1, and the number of options is 18, which are programmed for specific values of the given technological process in the memory of the programmable logic controller. Methods of controlling the electric autoclave were defined directly through the display of the programmable logic controller or through the installed informative SCADA program on a personal computer. It has been previously established that the SIMP Light ENT SCADA system provides access via a local network or via the Internet to current and archived data of the technological process. It has been investigated that procedure for starting the SIMP Light ENT SCADA program is given the channel editor, checking the channel settings, selecting the desired COM port, starting test channels, the mnemonic editor, and starting the monitor. It has been experimentally proven that the heating of an electric autoclave is started either directly by a programmable logic controller or by a personal computer with a SCADA program. It has been investigated that the electric autoclave is heated during the time specified by the technological process to the specified parameters. It was determined that the parameters set by the technological process correspond to the values of the resistance thermometer and the pressure sensor for the corresponding technological process, to according option is No. 13 − sterilization of canned meat. Voltage fluctuations and non-stationarity of technological parameters within 2% were analyzed, which is associated with typical features of a programmable logic controller regulator, but the indicated indicators are within the norm.

References

1. Alvarenga, V.O., Campagnollo, F.B., do Prado-Silva, L., Horita, C.N., Caturla, M.Y.R., Pereira, E.P.R., Crucello, A., & Sant'Ana, A.S. (2018). Impact of Unit Operations From Farm to Fork on Microbial Safety and Quality of Foods. Adv FoodNutr Res., v (85), pp.131-175. doi: 10.1016/bs.afnr.2018.02.004.
2. Chaari, R., Ellouze, F., Qureshi, B., Pereira, N., Yousbet, H., & Tover, E. (2016). Cyber – physical system clouds: a survey computer networks, v (108), pp.260 – 278. doi: 10.1016/j.comnet.2016.08.017.
3. Chandrasiri, G.S.M., Wijenayake, A.K.I. & Udara, S.P.R. (2022). Development of automated systems for the implementation of food processing. J. Res. Technol. Eng., v 3(1), pp.8-18.
4. Chen, R.-Y. (2017). An intelligent value structure based approach to collaboration of food traceability cyber physicalsystem by fog computing, Food Control, v (71), pp.124 – 136. doi . org / 10 . 1016 / j .foodcont.2016.06.042.
5. Chen, R.-Y. (2016). An intelligent value stream-based approach tocollaboration of food traceability cyber physical system by fogcomputing. Food Control, v (71), pp.124-136. doi.org/10.1016/j.foodcont.2016.06.042.
6. Fauziah, R., Malaka, R., & Yuliati, F. N. (2020). Titratable acidity and pH changes of pasteurized milk by addition of Roselle flower extract in the refrigerator storage. In IOP Conference Series: Earth and Environmental Science, v 492(1), p.012057. doi: 10.1088/1755-1315/492/1/012057.
7. Featherstone, S. (2015). Spoilage of canned foods. A Complete Course in Canning and Related Processes (Fourteenth Edition), v (2), pp.27-42. doi: 10.1016/B978-0-85709-678-4.00002-6.
8. Ghoshal, G. (2018). Emerging Food Processing Technologies. Food Processing for Increased Quality and Consumption, v (18), pp.29-65. doi: 10.1016/B978-0-12-811447-6.00002-3.
9. Gordon, A. (2020). Food safety and quality systems in developing countries, Academic Press. ISBN 9780128142738.
10. Hartulistiyoso, E., & Akmal, M. (2020). Energy Usage Analysis on The Production Process of Canned Crab Meat (Portunus pelagicus). IOP Conf. Ser.: Earth Environ. Sci., v (542), p.012048. doi 10.1088/1755-1315/542/1/012048.
11. Iqbal, J., Khan, Z.H., & Khalid, A. (2017). Prospects of robotics in food industry, Food Sci. Technol (Campinas), v 37(2), pp. 159-165. doi: 10.1590/1678-457X.14616.
12. Jijo, K.J., & Ramesh, K.K.R. (2014). Process automation for a food processing plant, International Journal of Science, Engineering and Technology Research (IJSETR), v 3(6), pp.1744-1750.
13. Kalantzis, F., & Revoltella, D. (2019). Do energy audits help SMEs to realize energy-efficiency opportunities? Energy Economics, v (83), pp.229-239. doi: 10.1016/j.eneco.2019.07.005.
14. Ko M.J., Nam H.H., & Chung, M.S. (2020). Subcritical water extraction of bioactive compounds from Orostachys japonicus A. Berger (Crassulaceae): Sci Rep, v (10), p.10890. doi: https://doi.org/10.1038/s41598-020-67508-2.
15. Kumar, Mr.TRaj, Pranav, A., Venkatesan, G., Shanthosh, S., & Arjunm, D.K. (2020). International Journal for Research in Applied Science & Engineering Technology (IJRASET), v 8(5), pp. 2647-2650. doi: http://doi.org/10.22214/ijraset.2020.5442.
16. Lau, W. L., Reizes, J., Timchenko, V., Kara, S., & Kornfeld, B. (2015). Heat and mass transfer model to predict the operational performance of a steam sterilisation autoclave including products. International Journal of Heat and Mass Transfer, v (90), pp.800–811. doi: 10.1016/j.ijheatmasstransfer.2015.06.089.
17. Malaka, R., & Maruddin, F. (2020). Antioxidant activity of milk pasteurization by addition of Matoa leaf extract (Pometia pinnata). In IOP Conference Series: Earth and Environmental Science, v 492(1), p.012046. doi: 10.1088/1755-1315/492/1/012046.
18. Meshram, A., Singhal, G.S., Bagyawant, S., & Srivastav, N. (2019). Plant−Derived enzymes; A Treasure for Foodbiotechnology, Enzymes in Food Biotechnology, pp.483-502. doi:10.1016/B978-0-12-813280-7.00028-1.
19. Mishachev, N.M., Shmyrin, A. M., & Suprunov, I.I. (2020). Simulation of sequential processing of a moving extended object. International Transaction Journal of Engineering, Management and Applied Sciences and Technologies, v 11(7), p. 11A07T. doi: 10.14456/ITJEMAST.2020.140.
20. Mokrushin, S.A. (2022). Selection of a system for automatic control of the sterilization process of canned food in an industrial autoclave. IOP Conf. Ser.: Earth Environ. Sci., v 1052(1), p. 012136. doi: 10.1088/1755-1315/1052/1/012136.
21. Muller, M., Kulenkotter, B., & Nassmacher, R. (2014). Robots in food industry : challenges and chances. In proceeding of the international symposium on robotics (ISR)/German conference on robotics (robotic) Germany.
22. Munirah, M., Malaka, R. & Maruddin, F. (2021). Organoleptic quality of pasteurization milk by matoa (Pometia pinnata) leaf extract supplementation. IOP Conf. Ser.: Earth Environ. Sci., v (788), p. 012098. doi: 10.1088/1755-1315/788/1/012098.
23. Pan, L., Pouyanfar, S., Chen, H., Qin, J. & Chen, S.-C. (2017). Deep Food: Automatic Multi-Class Classification ofFood Ingredients Using Deep Learning, IEEE 3rd International Conference on Collaboration and InternetComputing (CIC), pp.181-189. doi:10.1109/CIC.2017.00033.
24. Pierson, H. A., & Gashler, M. S. (2017). Deep learning in robotics: a review of recent research. Advanced Robotics, v (31), pp. 821-835. doi:10.1080/01691864.2017.1365009.
25. Piramuthu, S., & Zhou, W. (2016). RFID and sensory network automation in the food industry: ensuring quakity and safetythrough supply chain visibility. John Wiley & Sons. doi:10.1002/9781118967423.
26. Radchuk, O.V. & Savchenko-Pererva, M.Yu. (2023). Automated control system of devices for extraction of vegetable raw materials with subcritical liquid. Scientific bulletin of Tavriyya State Agro-Technological University. – Melitopol: TDATU, v 2(13), pp. 34-43. doi: 10.31388/2220-8674-2023-2-34.
27. Saldaña, E., Siche, R., Luján, M., & Quevedo, R. (2013). Review: Computer vision applied to the inspection and qualitycontrol of fruits and vegetables. Brazilian Journal of Food Technology, v 16(4), pp. 254-272. doi: http://dx.doi.org/10.1590/S1981-67232013005000031.
28. Sandey, K.K., Qureshi, M.A., Meshram, B.D., Agrawal, A.K. & Uprit, S. (2017). Robotics – An Emerging Technology inDairy Industry, International Journal of Engineering Trends and Technology (IJETT), v 43(1), pp. 58-62. doi: 10.14445/22315381/IJETT-V43P210.
29. Saravacos, G., & Kostaropoulos, A.E. (2016). Equipment for novel food processes handbook of food processing equipment. Springer, pp. 605-643.
30. Savchenko-Pererva, M.Yu. (2022). Innovative engineering solution in hotel and restaurant industry. Modern engineering and innovative technologies, Germany, v 20(1), pp. 3-7. DOI: 10.30890/2567-5273.2022-20-01-010.
31. Savchenko-Pererva, M. & Radchuk, O. (2021). Improvement of dust collectors for implementation in the food industry. Bulletin of the Sumy National Agrarian University. Series "Mechanization and automation of production processes", v 4(46), pp. 50-54. doi: https://doi.org/10.32845/msnau.2021.4.7.
32. Stier, R. F. (2019). Technical and quality management of canning. Swainson's Handbook of Technical and Quality Management for the Food Manufacturing Sector, pp. 505-527.
33. Triana, A., Maruddin, F., & Malaka, R. (2020). Supplementation of matoa (Pometia pinnata) leaf extract and alginate suppressed the growth of Staphylococcus aureus and Escherichia coli in pasteurized milk. In IOP Conference Series: Earth and Environmental Science, v 492(1), p. 012044. doi: 10.1088/1755-1315/492/1/012044.
34. Triana, A., Maruddin, F., Malaka, R., & Taufik, M. (2020). Matoa pasteurized milk quality subjected to the different levels of alginate. In IOP Conference Series: Earth and Environmental Science, v 575(1), p. 012029. doi: 10.1088/1755-1315/575/1/012029.
35. Wang, Y., Ke, L., Yang, Qi, Peng, Yu., Hu, Ya., Dai, L., Jiang, L., Wu, Q., Liu, Yu., Ruan, R., Fuet, G. (2019). Biorefinery process for production of bioactive compounds and bio-oil from Camellia oleifera shell: Int. J. Agric. Biol. Eng., v (12), pp. 190–194. doi: 10.25165/j.ijabe.20191205.4593.

AUTOMATION OF ELECTRIC AUTOCLAVE CONTROL

Abstract

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