To download article.DOI: 10.15507/2658-4123.033.202304.558-584
Application of Fuzzy and Clear Mathematical Models in Hybrid Control of the Process of Single-Stage Mincing of Frozen Meat
Boris R. Kapovskiy
Cand.Sci. (Engr.), Researcher of the Department of Functional and Specialized Nutrition, V.M. Gorbatov Federal Research Center for Food Systems (26 Talalikhina St., Moscow 109316, Russian Federation), ORCID: https://orcid.org/0000-0003-2964-7177, Researcher ID: AER-9531-2022, This email address is being protected from spambots. You need JavaScript enabled to view it.
Viktoriya A. Pchelkina
Cand.Sci. (Engr.), Leading Researcher, Experimental Clinic-Laboratory of Biologically Active Substances of Animal Origin, V.M. Gorbatov Federal Research Center for Food Systems (26 Talalikhina St., Moscow 109316, Russian Federation), ORCID: https://orcid.org/0000-0001-8923-8661, Researcher ID: M-4413-2016, This email address is being protected from spambots. You need JavaScript enabled to view it.
Andrey S. Dydykin
Dr.Sci. (Engr.), Director of the Department of Functional and Specialized Nutrition, V.M. Gorbatov Federal Research Center for Food Systems (26 Talalikhina St., Moscow 109316, Russian Federation), ORCID: https://orcid.org/0000-0002-0208-4792, Researcher ID: G-5020-2017, This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Introduction. During one-stage mincing of frozen meat by milling, a change in the temperature of the boundary layer occurs resulting in plastic deformations of the raw meat and an increase in the size of the meat chips. The problem of regulating the operating parameters for the raw meat mincing process depending on its temperature can be solved through computer calculations of the temperature forecast of the meat boundary layer for several time intervals using fuzzy logic. The aim of the study was to develop an algorithm for hybrid control of single-stage mincing of frozen meat using fuzzy and clear mathematical control models.
Aim of the Article. The article is aimed at developing a hybrid control algorithm for singlestage grinding of frozen meat using fuzzy and clear mathematical control models.
Materials and Methods. The object of the study was the process of mincing frozen meat block (beef) with the use of a laboratory installation for a single-stage mincing with a capacity of 400 kg/hour. The E. Mamdani algorithm was used to develop a fuzzy mathematical control model. Mathematical modeling was carried out in the MATLAB, the Fuzzy Logic Toolbox package.
Results. There was developed a model for fuzzy control of the operation of an intelligent control system (ICS) when forming a task for setting the operating parameters of the meat mincing process with the use of adaptive forecasting of meat temperature. For this model, the membership functions of the input and output variables and a rule base (knowledge base) were created. There was proposed a functional scheme of temperature control, which reflects the structure of a fuzzy control model for single-stage mincing. The advantages of this control include the fact that the system is given the function of continuous automated control of the temperature regime of mincing raw meat under the control of an industrial computer.
Discussion and Conclusion. The results of temperature control can be used for further technological processing of meat. Using information about the temperature and chemical composition of raw meat, the ICS can realize the optimal mixing of minced meat ingredients. Artificial intelligence calculates all these characteristics of meat without the participation of a human operator. It makes it possible to fully automate the technological processing of meat in order to produce finished products of guaranteed high quality.
Keywords: milling, mincing mode, frozen meat, fuzzy logic, hybrid control model
Conflict of interest: The authors declare no conflict of interest.
For Citation: Kapovskiy B.R., Pchelkina V.A., Dydykin A.S. Application of Fuzzy and Clear Mathematical Models in Hybrid Control of the Process of Single-Stage Mincing of Frozen Meat. Engineering Technologies and Systems. 2023;33(4):558‒584. https://doi.org/10.15507/2658-4123.033.202304.558-584
Authors contribution:
B. R. Kapovskiy – formulating the goal and objectives of the study, conducting an experimental study, analyzing the results.
В. A. Pchelkina – writing the text of the article, material collection and processing.
А. S. Dydykin ‒ general project management, formulating the main research concept.
All authors have read and approved the final manuscript.
Submitted 03.07.2023;
revised 24.08.2023;
accepted 10.09.2023
REFERENCES
1. Orlov A.A. Feed Control Model for Volumetric Milling on CNC Machines. Vestnik Mashinostroeniya. Bulletin of Mechanical Engineering. 2019;(2):32–34. (In Russ., abstract in Eng.) EDN: ZAIAWL
2. Kuznetsov P.M., Belousov N.A., Yagopolskiy A.G. Control of the Accuracy of the Trajectory of the Movement of the Working Body of the CNC Machine. STIN (Machines and Tools). 2021;(7):2–4. (In Russ., abstract in Eng.) EDN: YREUTE
3. Ivashov V.I., Kapovsky B.R., Plyasheshnik P.I., Pchelkina V.A., Iskakova E.L., Nurmukhanbetova D.E. Mathematical Simulation of One-Stage Grinding of Products Frozen in Blocks. News of the Academy of Sciences of the Republic of Kazakhstan. Series of Geology and Technical Sciences. 2018;5(431):48–65. https://doi.org/10.32014/2018.2518-170X.9
4. Ivashov V.I., Zaharov A.N., Kapovskiy B.R., Kozhevnikova O.E., Pchelkina V.A. Statistical Analysis of the Size of Meat Chips. All about Meat. 2015;(4):28–29 (In Russ., abstract in Eng.). EDN: UFENMF
5. Kapovskiy B.R., Pchelkina V.A., Plyasheshnik P.I. Lazarev A.A., Dydykin A.S. Methods of Input Control of Block Meat on Production Lines. Meat Industry. 2017;(5):28–31. (In Russ., abstract in Eng.) EDN: YNWHFD
6. Zadeh L.A. Fuzzy Sets. Information and Control. 1965;8(3):338–353. https://doi.org/10.1016/s0019-9958(65)90241-x
7. Birle S., Hussein M.A., Becker T. Fuzzy Logic Control and Soft Sensing Applications in Food and Beverage Processes. Food Control. 2013;29(1):254–269. https://doi.org/10.1016/j.foodcont.2012.06.011
8. Perrot N., Baudrit C. Intelligent Quality Control Systems in Food Processing Based on Fuzzy Logic. Robotics and Automation in the Food Industry. 2013; p. 200–225. https://doi.org/10.1533/9780857095763.1.200
9. Alekseev G.V., Aksenova O.I., Derkanosova A.A. Optimization of Feed for Unproductive Animals with the Help of Mathematical Modeling. Proceedings of the Voronezh State University of Engineering Technologies. 2015;(1):28–35. (In Russ., abstract in Eng.) https://doi.org/10.20914/2310-1202-2015-1-28-35
10. Vivek K., Subbarao K., Routray W., Kamini N.R., Dash K.K. Application of Fuzzy Logic in Sensory Evaluation of Food Products: a Comprehensive Study. Food and Bioprocess Technology. 2020;13:1–29. https://doi.org/10.1007/s11947-019-02337-4
11. Kantorovich G.G. Time Series Analysis. Economic Journal. 2002;(1):87–110. (In Russ., abstract in Eng.) EDN: QDFPSH
12. Sunchalin A.M., Sunchalina A.L. Overview of Methods and Models for Forecasting Financial Time Series. Chronoeconomics. 2020;(1):26–30. (In Russ., abstract in Eng.) EDN: ATTSEI
13. Mamdani E.H. Application of Fuzzy Algorithms for Control of Simple Dynamic Plant. Proceedings of the Institution of Electrical Engineers. 1974;121(12):1585–1588. https://doi.org/10.1049/piee.1974.0328
14. Pislaru C., Ford D.G., Holroyd G. Hybrid Modelling and Simulation of a Computer Numerical Control Machine Tool Feed Drive. Proceedings of the Institution of Mechanical Engineers. 2004;218(2):111–120. https://doi.org/10.1177/095965180421800205
15. Tsai P.-H., Berleant D., Segall R., Aboudja H., Batthula V.J.R., Duggirala S, et al. Quantitative Technology Forecasting: A Review of Trend Extrapolation Methods. International Journal of Innovation and Technology Management. 2023;20(4):2330002. https://doi.org/10.1142/S0219877023300021
16. Armstrong J.S. Extrapolation for Time-Series and Cross-Sectional Data. International Series in Operations Research & Management Science. 2001;30:217–243. doi: https://doi.org/10.1007/978-0-306-47630-3_11
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