Modeling the Process of Sawn Timber Drying

Complete text of the article:

Download article (pdf, 1MB )

UDС

674.047.3

DOI:

10.37482/0536-1036-2020-1-154-166

Abstract

he article considers the problem of sawn timber drying. The relevance of research is driven by the need to improve the automated process control system for drying sawn timber on the basis of modern information technologies, as well as to reduce the energy intensity of these technologies, which is enshrined in the program documents of forest industry development at the government level. The functional purpose of the automated control system is to provide a set level of the wood moisture content with the required quality of wood (zero ruptures). It is of immense complexity to obtain such functional dependency on the heating temperature in the chamber and the equilibrium air humidity by traditional statistical methods due to the uncertainty conditions of parameters, therefore, the apparatus of the fuzzy set theory should be used. Thus, the research purpose included generation of functional dependences of the sawn timber moisture content and drying time on the heating temperature and the equilibrium air humidity in the drying chamber based on fuzzy inference. The methodological basis of the theoretical studies was as follows: the theory of drying; the provisions of mathematical and fuzzy modeling; and, in terms of checking the adequacy of the proposed fuzzy model, the methods of mathematical statistics and experimental theory. The research results are the obtained dependences of the moisture content of sawn timber and drying time on the heating temperature and the equilibrium air humidity in the drying chamber, the synthesis of which is performed by means of Fuzzy Logic Toolbox of MATLAB application. The proposed functions of the moisture content and sawn timber drying time take into account the basic process configurations (the heating temperature and the equilibrium air humidity) and are experimentally verified for adequacy. The practical applicability of the results lies in the possibility of creating an intelligent system of automatic control of the sawn timber drying process.

Authors

A.G. Gorokhovsky, Doctor of Engineering, Prof.; ResearcherID: O-6030-2018,ORCID: 0000-0001-8847-8217
V.V. Pobedinsky, Doctor of Engineering, Prof.; ResearcherID: G-3245-2018, ORCID: 0000-0001-6318-3447
E.E. Shishkina, Doctor of Engineering, Assoc. Prof.; ResearcherID: O-6021-2018, ORCID: 0000-0002-2584-4897
E.V. Pobedinskiy, Postgraduate Student; ResearcherID: R-2080-2018, ORCID: 0000-0003-1838-309

Affiliation

Ural State Forest Engineering University, ul. Sibirskiy trakt, 37, Yekaterinburg, 620100, Russian Federation; e-mail: pobed@e1.ru

Keywords

wood drying, wood moisture content, equilibrium air humidity, fuzzy modeling, fuzzy inference

For citation

Gorokhovsky A.G., Pobedinsky V.V., Shishkina E.E., Pobedinskiy E.V. Modeling the Process of Sawn Timber Drying. Lesnoy Zhurnal [Russian Forestry Journal], 2020, no. 1, pp. 154–166. DOI: 10.37482/0536-1036-2020-1-154-166

References

1. Vasil’yev V.I., Il’yasov B.G. Intelligent Control Systems. Theory and Practice. Moscow, Radiotekhnika Publ., 2009. 393 p.
2. Gorokhovskiy A.G. Sawn Timber Drying Technology Based on Modeling and Optimization of Heat and Mass Transfer Processes in Wood: Dr. Eng. Sci. Diss. Yekaterinburg, 2008. 290 р.
3. Gorokhovskiy A.G., Shishkina E.Ye., Chernyshev O.N. Optimum Control of Processes of Thermo-Mass Transfer at Convection to Wood Drying. Sovremennyye problemy nauki i obrazovaniya [Modern problems of science and education], 2014, no. 6.
4. Leonenkov A.V. Fuzzy Modeling in the Environment of MATLAB and FuzzyTECH. Saint Petersburg, BHV-Petersburg Publ., 2005. 736 p.
5. Lykov A.V. Drying Theory. Moscow, Energiya Publ., 1968. 470 p.
6. Lykov A.V. On Systems of Differential Equations of Heat and Mass Transfer in Capillary-Porous Bodies. Inzhenerno-Fizicheskii Zhurnal [Journal of Engineering Physics and Thermophysics], 1974, vol. ХХVI, no. 1, pp. 18–25.
7. Nikitenko N.I. Investigation of Non-Stationary Processes of Heat and Mass Exchange by the Grid Method. Kiev, Naukova dumka Publ., 1971. 266 p.
8. Decree of the Government of the Russian Federation of September 26, 2013 No. 1724-r “Fundamentals of State Policy in the Field of Use, Protection and Reproduction of Forests in the Russian Federation for the Period up to 2030. 
9. Pobedinsky V.V., Gazizov A.M., Sannikov S.P., Pobedinskiy A.A. Dielectric Permeability of Forestry Depending on Environmental Parameters in Radio-Frequency Monitoring. Vestnik Mordovskogo universiteta [Mordovia University Bulletin], 2018, vol. 28, no. 2, pp. 148–163. DOI: 10.15507/0236-2910.028.201802.148-163
10. Sannikov S.P., Pobedinskiy V.V., Borodulin I.V., Chernitsyn M.A., Kuz’minov N.S. The Dependence of the Fall of the Signal Power on the Parameters of the Forest Environment when the Radio Frequency of Forest Monitoring. Izmereniye. Monitoring. Upravleniye. Kontrol’ [Measurement. Monitoring. Management. Control], 2016, no. 3(17), pp. 23–29.
11. Sannikov S.P., Pobedinsky V.V., Gazizov A.M., Borodulin I.V., Chernitsyn M.A., Kuzminov N.S. Dependence of the Signal Power Loss on the Forest Ambience Parameters Under Radio-Frequency Monitoring of Forest Fund. Sistemy. Metody. Tekhnologii [System. Methods. Technologies], 2016, no. 4(32), pp. 181–187.
12. Smirnov M.S. On the System of Differential Equations of the Drying Process. Inzhenerno-Fizicheskii Zhurnal [Journal of Engineering Physics and Thermophysics], 1961, vol. IV, no. 9, pp. 40–44.
13. Tsoy P.V. Methods for Heat and Mass Transfer Problem Solving. Moscow, Energoatomizdat Publ., 1984. 416 p.
14. Shishkina E.E. Energy-Saving Technology for Convective Drying of Sawn Timber Based on Controlled Moisture Transfer in Wood: Dr. Eng. Sci. Diss. Yekaterinburg, 2016. 336 p.
15. Shubin G.S. Drying and Heat Treatment of Wood (Issues of Theory, Methods for Process Calculation, Technology Improvement): Dr. Eng. Sci. Diss. Moscow, M, 1985. 381 p.
16. Shubin G.S. Generalized System of Heat and Mass Transfer Equations under Variable Environmental Conditions and Its Implementation on a Computer for Calculating the Drying Processes of Wood. Lesnoy Zhurnal [Russian Forestry Journal], 1988, no. 3, pp. 49–56. URL: http://lesnoizhurnal.ru/ apxiv/1988/%E2%84%96%203.pdf 
17. Shubin G.S. Drying and Heat Treatment of Wood. Moscow, Lesnaya promyshlennost’ Publ., 1990. 336 p.
18. Alguliyev R., Abdullayeva F. Development of Fuzzy Risk Calculation Method
for a Dynamic Federation of Clouds. Intelligent Information Management, 2015, vol. 7, no. 4, pp. 230–241. DOI: 10.4236/iim.2015.74018
19. Ali A., El-Serafi K., Mostafa S.A.K., El-Sheimy N. Frequency Features Based Fuzzy System for Rotating Machinery Vibration Analysis Using Smartphones Low-Cost MEMS Sensors. Journal of Sensor Technology, 2016, vol. 6, no. 3, pp. 56–74. DOI: 10.4236/jst.2016.63005
20. Azzouz S., Dhib K.B., Bahar R., Ouertani S., Elaieb M.T., Elcafsi A. Mass Diffusivity of Different Species of Wood in Convective Drying. European Journal of Wood and Wood Products, 2018, vol. 76, iss. 2, pp. 573–582. DOI: 10.1007/s00107-017-1212-9
21. Carneiro A.L.G., Porto Jr. A.C.S. An Integrated Approach for Process Control Valves Diagnosis Using Fuzzy Logic. World Journal of Nuclear Science and Technology, 2014, vol. 4, no. 3, pp. 148–157. DOI: 10.4236/wjnst.2014.43019
22. Da Silva W.P., da Silva L.D., e Silva C.M.D.P.S., Nascimento P.L. Optimization and Simulation of Drying Processes Using Diffusion Models: Application to Wood Drying Using Forced Air at Low Temperature. Wood Science and Technology, 2011, vol. 45, iss. 4, pp. 787–800. DOI: 10.1007/s00226-010-0391-x 
23. Da Silva W.P., e Silva C.M.D.P.S., Rodrigues A.F., de Figueirêdo R.M.F. OneDimensional Numerical Solution of the Diffusion Equation to Describe Wood Drying: Comparison with Two- and Three-Dimensional Solutions. Journal of Wood Science, 2015, vol. 61, iss. 4, pp. 364–371. DOI: 10.1007/s10086-015-1479-6
24. Espinoza O., Bond B. Vacuum Drying of Wood – State of the Art. Current Forestry Reports, 2016, vol. 2, iss. 4, pp. 223–235. DOI: 10.1007/s40725-016-0045-9
25. Garg H. A Linear Programming Method Based on an Improved Score Function for Interval-Valued Pythagorean Fuzzy Numbers and Its Application to Decision-Making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018, vol. 26, no. 01, pp. 67–80. DOI: 10.1142/S0218488518500046
26. Gour A., Pardasani K.R. Statistical and Soft Fuzzy Set Based Analysis of Amino Acid Association Patterns in Peptide Sequence of Swine Influenza Virus. Advanced Science, Engineering and Medicine, 2018, vol. 10, nо. 2, pp. 137–144. DOI: 10.1166/asem.2018.2118
27. Jia X., Hayashi K., Zhan J., Cai Y. The Moisture Transfer Mechanism and Influencing Factors in Wood during Radio-Frequency/Vacuum Drying. European Journal of Wood and Wood Products, 2016, vol. 74, iss. 2, pp. 203–210. DOI: 10.1007/s00107-015-0985-y
28. Lin J.-J., Chuang C.-J., Ko C.-F. Applying GA and Fuzzy Logic to Breakdown Diagnosis for Spinning Process. Intelligent Information Management, 2017, vol. 9, no. 1, pp. 21–38. DOI: 10.4236/iim.2017.91002
29. Mamdani E.H. Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis. IEEE Transactions on Computers, 1977, vol. 26, no. 12, рр. 1182–1191. DOI: 10.1109/TC.1977.1674779
30. MATLAB Release Notes for R2008a. MathWorks. Available at: https://www.mathworks.com/help/matlab/release-notes.Highlight (accessed 15.02.19).
31. Miranda G.H.B., Felipe J.C. Computer-Aided Diagnosis System Based on Fuzzy Logic for Breast Cancer Categorization. Computers in Biology and Medicine, 2015, vol. 64, pp. 334–346. DOI: 10.1016/j.compbiomed.2014.10.006
32. Moises S.A., Pereira S. do L. Dealing with Empty and Overabundant Answers to Flexible Queries. Journal of Data Analysis and Information Processing, 2014, vol. 2, no. 1, pp. 12–18. DOI: 10.4236/jdaip.2014.21003
33. Nakagawa K., Tamura A., Adachi S. Optimization of Food Dye (Betanin) Retention during Hot Air Drying: Design Space Calculation with Consideration of Reaction and Substrate Transfer Kinetics. Drying Technology, 2018, vol. 36, iss. 15, pp. 1920–1929. DOI: 10.1080/07373937.2018.1463538 
34. Ntaganda J.M., Haggar M.S.D., Mampassi B. Fuzzy Logic Strategy for Solving an Optimal Control Problem of Therapeutic Hepatitis C Virus Dynamics. Open Journal of Applied Sciences, 2015, vol. 5, no. 9, pp. 527–541. DOI: 10.4236/ojapps.2015.59051
35. Obataya E., Higashihara T. Reversible and Irreversible Dimensional Changes of Heat-Treated Wood during Alternate Wetting and Drying. Wood Science and Technology, 2017, vol. 51, iss. 4, pp. 739–749. DOI: 10.1007/s00226-017-0918-5
36. Ouertani S., Koubaa A., Azzouz S., Hassini L., Dhib K.B., Belghith A. Vacuum Contact Drying Kinetics of Jack Pine Wood and Its Influence on Mechanical Properties: Industrial Applications. Heat and Mass Transfer, 2015, vol. 51, iss. 7, pp. 1029–1039. DOI: 10.1007/s00231-014-1476-0
37. Ouertani S., Koubaa A., Soufien A., Rim B., Lamine H., Belghith A. Microwave Drying Kinetics of Jack Pine Wood: Determination of Phytosanitary Efficacy, Energy Consumption, and Mechanical Properties. European Journal of Wood and Wood Products, 2018, vol. 76, iss. 4, pp. 1101–1111. DOI: 10.1007/s00107-018-1316-x
38. Perez O. Fuzzy Law: A Theory of Quasi-Legal Systems. Canadian Journal of Law & Jurisprudence, 2015, vol. 28, iss. 2, pp. 343–370. DOI: 10.1017/cjlj.2015.31
39. Phonetip K., Ozarska B., Brodie G.I. Comparing Two Internal Check Measurement Methods for Wood Drying Quality Assessment. European Journal of Wood and Wood Products, 2017, vol. 75, iss. 1, pp. 139–142. DOI: 10.1007/s00107-016-1115-1
40. Piegat A. Fuzzy Modeling and Control. Heidelberg, Physica-Verlag, 2001. 760 p. DOI: 10.1007/978-3-7908-1824-6
41. Prakash O., Ranjan S., Kumar A., Tripathy P.P. Applications of Soft Computing in Solar Drying Systems. Solar Drying Technology. Ed. by O. Prakash, A. Kumar. Singapore, Springer, 2016, рр. 419–430. DOI: 10.1007/978-981-10-3833-4_14
42. Safin R.R., Khasanshin R.R., Khakimzyanov I.F., Mukhametzyanov Sh.R., Kainov P.A. Increasing the Energy Efficiency of the Process of Oscillating VacuumConductive Drying of Wood by Means of a Heat Pump. Journal of Engineering Physics and Thermophysics, 2017, vol. 90, iss. 2, pp. 310–317. DOI: 10.1007/s10891-017-1569-y
43. Schneider J., Urban R. A Proof of Donsker’s Invariance Principle Based on Support Functions of Fuzzy Random Vectors. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018, vol. 26, no. 01, pp. 27–42. DOI: 10.1142/S0218488518500022
44. Shtovba S., Rotshtein A., Pankevich O. Fuzzy Rule Based System for Diagnosis of Stone Construction Cracks of Buildings. Advances in Computational Intelligence and Learning. Ed. by H.J. Zimmermann, G. Tselentis, M. van Someren, G. Dounias. Dordrecht, Springer, 2002, pp. 401–412. DOI: 10.1007/978-94-010-0324-7_28
45. Simulink Release Notes for R2008a. MathWorks. Available at: https://www.mathworks.com/help/simulink/release-notes.Highlight (accessed 15.02.19).
46. Situmorang Z., Situmorang J.A. Intelligent Fuzzy Controller for a Solar Energy Wood Dry Kiln Process. 2015 International Conference on Technology, Informatics, Management, Engineering & Environment (TIME-E), September 7–9, 2015. Samosir, Indonesia, 2015, pp. 152–157. DOI: 10.1109/TIME-E.2015.7389765
47. Situmorang Z., Wardoyo R., Hartati S., Istiyanto J.E. The Schedule of Optimal Fuzzy Controller Gain with Multi Model Concept for a Solar Energy Wood Drying Process Kiln. International Journal of Operations and Quantitative Management, 2009, vol. 15, no. 2, pp. 137–151.
48. Sychevskii V.A. Heat and Mass Transfer in Convective Wood-Drying Plants. Journal of Engineering Physics and Thermophysics, 2018, vol. 91, iss. 3, pp. 705–771. DOI: 10.1007/s10891-018-1793-0
49. Takagi T., Sugeno M. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man, and Cybernetics, 1985, vol. SMC-15, iss. 1, рр. 116–132. DOI: 10.1109/TSMC.1985.6313399
50. Uraon K.K., Kumar S. Analysis of Defuzzification Method for Rainfall Event. International Journal of Computer Science and Mobile Computing, 2016, vol. 5, iss. 1, pp. 341–354.
51. Xu W., Liu G., Yu X. A Binomial Tree Approach to Pricing Vulnerable Option in a Vague World. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018, vol. 26, no. 01, pp. 143–162. DOI: 10.1142/S0218488518500083 
52. Yue H., Li J., Shi J., Yang W. Adaptive Fuzzy Tracking Control for Stochastic Nonlinear Systems with Time-Varying Input Delays Using the Quadratic Functions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018, vol. 26, no. 01, pp. 109–142. DOI: 10.1142/S0218488518500071
53. Zadeh L.A. Fuzzy Sets. Information and Control, 1965, vol. 8, iss. 3, рр. 338– 353. DOI: 10.1016/S0019-9958(65)90241-X
54. Zadeh L.A. Fuzzy Logic. Computer, 1988, vol. 21, no. 4, рр. 83–93. DOI: 10.1109/2.53
55. Zhao Y., Zhihui W., Iida I., Huang R., Lu J., Jiang J. Studies on Pre-Treatment by Compression for Wood Drying I: Effects of Compression Ratio, Compression Direction and Compression Speed on the Reduction of Moisture Content in Wood. Journal of Wood Science, 2015, vol. 61, iss. 2, pp. 113–119. DOI: 10.1007/s10086-014-1451-x
56. Zhou Z., Wang K. Sliding Mode Controller Design for Wood Drying Process. Wood Science and Technology, 2018, vol. 52, iss. 4, pp. 1039–1048. DOI: 10.1007/s00226-018-1006-1 

Received on February 2, 2019