Fındık Kabuğu Kurutma Sürecinde Yapay Sinir Ağları ve Bulanık Mantık Yöntemlerinin Karşılaştırılması

Yıl 2023, Cilt: 6 Sayı: 2, 50 - 55, 31.12.2023

Mert Levent , Halil Nusret Buluş , Soner Çelen , Aytaç Moralar

https://doi.org/10.55581/ejeas.1388492

Öz

Gıda ürünlerinin kurutularak saklanması kullanım ömrünü uzatması açısından uzun yıllardır kullanılan bir tekniktir. Gıdaların kurutularak saklanması gibi gıda artıklarının da kurutulması son yıllarda çalışmalara konu olmuştur. Ürün kurutma işlemi, ürün içerisindeki nem miktarının azaltılması anlamına gelmektedir. Nem miktarının azaltılması amacıyla kurulmuş olan düzenekler maliyetli ve daha çok kuruma oranının tespitinin tecrübeye dayandığı sistemlerdir. Bu sebeple son yıllarda kurutma süreçlerinin matematiksel olarak modellenmesi, yapay zeka yöntemleri ile sistem davranışının bir modelinin oluşturulması çalışmaları ilgi çekmiştir. Bu çalışmada fındık kabuğu kurutma işleminin yapay zeka tekniklerinden olan yapay sinir ağları ve bulanık mantık yöntemleri ile modellenmesi gerçekleştirilmesi hedeflenmiş ve oluşturulan modellerin kuruma oranının deneysel sonuçlarına yakınlığı incelenmiştir.

Anahtar Kelimeler

ANFIS, Bulanık Mantık, Kurutma, Levenberg Marquardt, Tahmin, Yapay Sinir Ağları

Comparison of Artificial Neural Networks and Fuzzy Logic Methods in the Hazelnut Shell Drying Process

Öz

Drying and storing food products have been widely used techniques for extending shelf life for many years. In recent times, there has been a focus on the drying of food residues, similar to the preservation of foods by drying. The process of drying a product involves reducing the moisture content within the product. However, the devices established to reduce moisture content are often costly and rely heavily on experience-based systems for determining the drying ratio. Therefore, in recent years, there has been significant interest in the mathematical modeling of drying processes and the creation of a model for system behavior using artificial intelligence methods. This study aims to model the drying process of hazelnut shells using artificial intelligence techniques, specifically artificial neural networks and fuzzy logic methods. The proximity of the models created to the experimental results of the drying ratio is examined.

Anahtar Kelimeler

ANFIS, Artifical Neural Network, Drying, Fuzzy Logic, Levenberg Marquardt, Prediction

Kaynakça

• Akbarpour, H., Mohajeri, M. & Akbarpour, M., (2016). Pore Diameter of Nanoporous Anodic Alumina: Experimental Study and Application of ANFIS and MLR, Chemometrics and Intelligent Laboratory Systems, 153, 82-91.
• Pusat, S., Akkoyunlu, M.T., Pekel, E., Akkoyunlu, M.C., Özkan, Ç. & Kara S.S., (2016) Estimation of Coal Moisture Content in Convective Drying Process Using ANFIS, Fuel Processing Technology, 147, 12-17.
• Dolatabadi, M., Mehrabpour, M., Esfandyari, M., Davoudi, M. and Alidadi H., (2018), Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS, Chemometrics and Intelligent Laboratory Systems, 181, 72-78.
• Quej, V., Almorox, J., Arnaldo, J. & Saito, L., (2017), ANFIS, SVM and ANN Soft-Computing Techniques to Estimate Daily Global Solar Radiation in a Warm Sub-Humid Environment, Journal of Atmospheric and Solar–Terrestrial Physics, 155, 62-70.
• Kaveh, M., Sharabiani, V.R., Chayjan, R.A., Taghinezhad, E., Abbaspour-Gilandeh, Y. & Golpour, I., (2018), ANFIS and ANN Model for Prediction of Moisture Diffusivity and Specific Energy Consumption Potato Garlic and Cantaloupe Drying Under Convective Hot Air Dryer, Information Processing in Agriculture, 5(3), 372–387.
• Najafi, B. & Ardabili, S.F., (2018), Application of ANFIS, ANN, and Logistic Methods in Estimating Biogas Production from Spent Mushroom Compost (SMC), Resources, Conservation & Recycling, 133, 169–178.
• Rego, A.S., Valim, I.C., Vieira, A.A., Vilani, C. & Santos, B.F., (2018), Optimization of sugarcane bagasse pretreatment using alkaline hydrogen peroxide through ANN and ANFIS modelling, Bioresource Technology, 267, 634–641.
• Erkaymaz,O., İleri yönlü yapay sinir ağlarında küçük dünya ağ yaklaşımı ve uygulamaları, (2012), PhD, Sakarya University, Sakarya.
• Abioduna, A., Jantana, O.I., Omolara, A.E., Dada, K.V., Arshad, H. & Mohamed, N.A., (2018), State-of-the-art in artificial neural network applications: A survey, Heliyon, 4(11).
• Maladkar, K., (2018), Analyticsindiamag, [online]. Available: https://analyticsindiamag.com/6-types-of-artificial-neural-networks-currently-being-used-in-todays-technology/.
• Chopra, S., Dhiman, G., Sharma, A., Shabaz, M., Shukla, P. & Arora, M., (2021), Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences, Computational Intelligence and Neuroscience, 2021.
• Ali, O.A.M., Ali, A.Y. & Sumait, B.S., (2015), Comparison between the effects of different types of membership functions on fuzzy logic controller performance, International Journal of Emerging Engineering Research and Technology, 3(3), 76-83.
• Moralar, A. & Çelen, S., (2022), Evaluation of Thermal and Drying Characteristics of Dried Hazelnut (Corylus avellana L.) Shell Waste, PHILIPP AGRIC SCIENTIST, 105, 161-170.