Determining The Drying Rates of Fabrics with Different Knit Structures by Fuzzy Logic Method

Yıl 2023, Cilt: 9 Sayı: 2, 191 - 196, 30.06.2023

Feyza Akarslan Kodaloğlu , Murat Kodaloğlu

https://doi.org/10.22399/ijcesen.1261946

Öz

Drying; It is a process applied to reduce the amount of water in a product or to reduce it to very low levels. The constantly changing conditions during the drying process make it difficult to determine the most suitable operating conditions to perform the drying process, such as drying time, energy consumption and product structural characteristics.
In terms of a suitable drying, it is important to be able to control the factors affecting the drying depending on the characteristics of the product. Among these factors, the drying method and the pre-treatments that can be applied are also effective on drying. The high temperatures applied during drying and other conditions that are not chosen correctly can cause negative results in both the appearance of the product.
The drying process in the textile industry is an expensive and laborious process that requires a lot of energy. The main purpose of the drying process is to provide maximum energy saving and energy efficiency at minimum time and cost without compromising the quality and structural properties of the material used. In this study, four fuzzy models were created depending on fabric fiber blend ratios and knitting structure in order to determine the effect on drying speed depending on time and temperature by using fuzzy logic method, which is one of the artificial intelligence methods.

Anahtar Kelimeler

Fabrics, Drying, Knit structures, Fuzzy logic, Artificial intelligence

Kaynakça

• [1]Şen, Z. (2010). Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling. Expert systems with Applications, 37(8); 5653-5660.
• [2]Şen, Z. (2011). Supervised fuzzy logic modeling for building earthquake hazard assessment. Expert systems with applications, 38(12);14564-14573.
• [3]Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on systems, Man and Cybernetics, (1), 28-44. https://doi.org/10.1109/TSMC.1973.5408575
• [4]Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-III. Information Siences, 9(1);43-80. https://doi.org/10.1016/0020-0255(75)90036-5
• [5]Mamdani, E. H. (1976). Advances in the linguistic synthesis of fuzzy controllers. International Journal of Man-Machine Studies, 8(6); 669-678.
• [6]Hosseinpour, S., & Martynenko, A. (2022). Application of fuzzy logic in drying: A review. Drying Technology, 40(5); 797-826.
• [7]Arief, U. M., Nugroho, F., Purbawanto, S.,Setyaningsih, D. N., Suryono, N. (2018). Analysis of Maizena Drying System Using Temperature Control Based Fuzzy Logic Method. AIP Conference Proceedings; AIP Publishing LLC, , March; 1941(1);020005.
• [8]Heriansyah, H.; Istiqphara, I.; Adliani, N. (2019). Optimization of Herbal Dryer System Based on Smart Fuzzy and Internet of Thing (IOT). Int. J.Eng. Sci. Appl., 6; 104–110
• [9]Nadian, M. H.; Abbaspour-Fard, M. H.;Martynenko, A.; Golzarian, M. R. (2017). An Intelligent Integrated Control of Hybrid Hot Air-Infrared Dryer Based on Fuzzy Logic and Computer VisionSystem. Comput. Electron. Agric.,137;138–149.
• [10]Sourveloudis, N. C.; Kiralakis, L. (2005). Rotary Drying ofOlive Stones: Fuzzy Modeling and Control.WSEASTrans. Syst.,4; 2361–2368.
• [11]Atthajariyakul, S.; Leephakpreeda, T. (2006). Fluidized Bed Paddy Drying in Optimal Conditions via Adaptive Fuzzy Logic Control. J. Food Eng.,75;104–114.
• [12]Khodabakhsh Aghdam, S. H.; Yousefi, A. R.;Mohebbi, M.; Razavi, S. M. A.; Orooji, A.;Akbarzadeh-Totonchi, M. R. (2015). Modeling for Drying Kinetics of Papaya Fruit Using Fuzzy Logic Table Look-up Scheme.Int. Food Res. 22;1234–1239.
• [13]Bagheri, N.; Nazilla, T.; Javadikia, H. (2015). Developmentand Evaluation of an Adaptive Neuro FuzzyInterface Models to Predict Performance of a SolarDryer.Agric. Eng. Int. CIGR, 17;112–121.
• [14]Jafari, S. M.; Ganje, M.; Dehnad, D. (2016). Ghanbari, V.Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion. J. Food Process. Preserv.,40;329–339.
• [15]Abdenouri, N., Zoukit, A., Salhi, I., & Doubabi, S. (2022). Model identification and fuzzy control of the temperature inside an active hybrid solar indirect dryer. Solar Energy, 231; 328-342.
• [16]Júnior, M. P., da Silva, M. T., Guimarães, F. G., & Euzébio, T. A. (2022). Energy savings in a rotary dryer due to a fuzzy multivariable control application. Drying Technology, 40(6); 1196-1209.
• [17]Majumdar, A., & Ghosh, A. (2008). Yarn strength modelling using fuzzy expert system. Journal of Engineered Fibers and Fabrics, 3(4); 61-68.
• [18]Paul, T. K., Jalil, T. I., Parvez, M. S., Repon, M. R., Hossain, I., Alim, M. A., ... & Jalil, M. A. (2022). A Prognostic Based Fuzzy Logic Method to Speculate Yarn Quality Ratio in Jute Spinning Industry. Textiles, 2(3); 422-435.