Науковий вісник НГУ

Experimental research on muffle furnace dynamic properties

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №3 2023

Last Updated on 27 June 2023

Published on 30 November -0001

Hits: 2423

Authors:

M.I.Horbiychuk*, orcid.org/0000-0002-8586-1883, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

N.T.Lazoriv, orcid.org/0000-0001-7334-9308, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

M.I.Kohutyk, orcid.org/0000-0003-0026-7744, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

I.Z.Manuliak, orcid.org/0000-0002-0072-1532, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (3): 144 - 150

https://doi.org/10.33271/nvngu/2023-3/144

Abstract:

Purpose. To develop the method and software for building empirical models of muffle furnaces with two heating elements.

Methodology. The method for processing experimental data according to the improved algorithm, which is based on the areas method, is used for constructing empirical models of the muffle furnace. Experimental research of the muffle furnace dynamic is conducted using the following methodology. The muffle furnace is cooled to the room temperature and then the lower heater is turned on and temperatures at the furnace output are fixed using the experimental two-channel temperature controller MIC-344. The second cycle of the experimental research began by cooling the furnace to the room temperature with turning on the upper heater. Observations based on the results of the experiment are conducted until the temperature is stabilized at the two outputs of the muffle furnace. Archiving of temperature trends is carried out using the RS485/Ethernet interface and the SmartReview software (a product of private limited company “Microl”).

Findings. The developed software can be used for iteratively selection of the optimal empirical model based on the criterion of the root mean square deviation of the calculated and experimental data. The calculating method for the acceleration curve based on the transfer functions coefficients with a variable discreteness step is improved in order to compare the experimental data with the empirical modeling results. Twelve empirical models were tested for four signal transmission channels in the process of experimental research. It has been established that only four of them are stable (have left-hand roots of the characteristic equation). Among the selected sustainable models, the model with the numerator polynomial equaled to two and denominator polynomial equaled to three has been established as the best model (according to the established criterion). A structural diagram is created based on the synthesized empirical model of the muffle furnace, which includes four transfer functions and cross connections.

Originality. For the first time, the empirical model of a muffle furnace with two electrical energy sources has been developed, which describes its dynamic properties as the object of automatic control with high accuracy, which enables to reveal the presence of internal cross-connections, that significantly complicate the controlling process of the object.

Practical value. The created empirical models with two electric sources (tens) can be used for the synthesis of the high-precision automatic temperature control system of the muffle furnace.

Keywords: muffle furnace, temperature, control system, dynamics, model, transfer function

References.

1. Muffle furnaces: device, characteristics and purpose (2022). Retrieved from https://analit-pribor.com.ua/uk/developments/mufelni-pechi-prystrij-harakterystyky-i-pryznachennya/.

2. Vestfálová, M. (2015). Thermodynamic properties of real gases and BWR equation of state. EPJ Web of Conferences, 92, 106-109. Liberec, Grech Republic. https://doi.org/10.1051/epjconf/20159202106.

3. Md. Tahmid Wara Ucchas, Mechrab Mustafiz Nuhas, Md. Toufiguzzaman, AI Jaber Mahmud, & Md. Fokhrul Islam (2022). Performance and Comparative Analysis of PI and PID Controller-based Single Phase PWM Inverter Using Matlab Simulink for Variable Voltage. Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, (pp. 112-134). India. Retrieved from https://ieeexplore.ieee.org/document/9807857.

4. Karvatskyi, A., Pulinets, I., & Shilovch, I. (2012). Mathematical model of the thermal-hydrodynamic state of a multi-chamber furnace during firing of electrode blanks. Eastern European journal of advanced technologies, 1/4(55), 33-37.

5. Zakharov, A., Shayakhmetov, U., & Akhmetshina, H. (2017). Calculation of a high-temperature muffle furnace with new heat-insulating materials. Bulletin of Bashkir University, 22(4), 996-999. Retrieved from https://cyberleninka.ru/article/n/raschet-vysokotemperaturnoy-mufelnoy-pechi-s-novymi-teploizolyatsionnymi-materialami/viewer.

6. Gorbiychuk, M., Lazoriv, N., Chyhur, L., & Chyhur, I. (2021). Determining configuration parameters for proportion-ally integrated differentiating controllers by arranging the poles of the transfer function on the complex plane. Eastern-European Journal of Enterprise Technologies, 2(113), 80-93. https://doi.org/10.15587/1729-4061.2021.242869.

7. Demetriou, M. A., & Fahroo, F. (2013). Synchronization of a class of second order distributed parameter systems. IFAC Proceedings Volumes, 46(26), 73-78. https://doi.org/10.3182/20130925-3-FR-4043.00057.

8. Kropyvnytska, V., Kopystynskyy, L., & Sementsov, G. (2017). Development of a set of methods for preforecasting fractal time series analysis to determine the level of persistence. Eastern-European Journal of Enterprise Technologies, 3/4(87), 10-17. https://doi.org/10.15587/1729-4061.2017.104425.

9. Gorbiychuk, M., Zamikhovska, O., Zikratyi, S., Zamikhovskiy, L., & Shtaier, L. (2019). Evaluation of dynamic roperties of gas pumping units according to the results of experimental Researches. Earsten-European Journal of Enterprise Technologies, 2/2(98), 73-81.

10. Shengzhong, H. (2011). Immune Genetic Evolutionary Algorithm of Wavelet Neural Network to Predict the Performance in the Centrifugal Compressor and Research. Journal of Software, 6(5), 908-914. Retrieved from http://www.jsoftware.us/vol6/jsw0605-20.pdf.

• < Prev
• Next >