پیش بینی سینتیک خشک کردن توت سفید در خشک کن مایکرویو- هوای داغ: مقایسه بین مدل های ریاضی، شبکه های عصبی مصنوعی و انفیس

نویسندگان
1 دانشجوی دکتری، گروه مهندسی مکانیک بیوسیستم، دانشگاه محقق اردبیلی، اردبیل، ایران.
2 دانشجوی دکتری رشته مکانیک بیوسیستم دانشگاه ارومیه
3 دانشیار گروه مهندسی مکانیک بیوسیستم، دانشگاه محقق اردبیلی، اردبیل،
4 استاد گروه مکانیک بیوسیستم دانشگاه شهید محقق اردبیل
5 بخش علوم کشاورزی، دانشگاه پیام نور، تهران، ایران
چکیده
هدف از این پژوهش، تعیین سینتیک، ضریب پخش رطوبت موثر، انرژی فعال‌سازی، انرژی مصرفی ویژه و همچنین پیش بینی نسبت رطوبت توت سفید در طی فرآیند خشک‌کردن با خشک‌کن مایکرویو- هوای داغ به کمک مدل های ریاضی، شبکه‌های عصبی مصنوعی (ANN) و انفیس (ANFIS) است. خشک‌کردن محصول در سه دمای 40، 55 و°C 70، در سه سرعت جریان هوای 5/0،1 و m/s 5/1 و سه توان مایکرویوو 270 ،450 و W 630 انجام شد. برای انتخاب یک منحنی خشک‌کردن مناسب، ده مدل لایه نازک خشک شدن، شبکه‌های عصبی مصنوعی و انفیس به داده‌های آزمایشگاهی برازش شد. نتایج نشان داد که بیشترین و کمترین مقدار ضریب پخش رطوبت موثر به ترتیب (m2/s 9-10×56/3 و کمترین مقدار m2/s10-10×86/3) به دست آمد. همچنین کمترین و بیشترین مقدار انرژی مصرفی ویژه به ترتیب 54/48 وMj/kg 88/1380 محاسبه شد. در میان مدل‌های ریاضی مورد تحقیق مدلPage بهترین مدل برای تشریح رفتار خشک‌شدن لایه نازک توت را داشت. نتایج نشان داد مقادیر خطای میانگین مربعات ( )، برای مدل‌های ریاضی، ANN، و ANFISبه ترتیب 0059/0، 0052/0 و 0044/0 به دست آمد. بنابراین مدل ANFIS با بیشترین مقدار ضریب همبستگی (99995/0= )، کمترین درصد میانگین خطای نسبی (84/1= ) و خطای میانگین مربعات (0044/0= ) برای ارزیابی نسبت رطوبت در مقایسه با سایر روش‌های اجرا شده در این پژوهش به عنوان بهترین مدل انتخاب شد
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Prediction of white mulberry drying kinetics in microwave- convective dryer: A comparative study between mathematical model, artificial neural network and ANFIS

نویسندگان English

Mohammad Kaveh 1
Ahmad Jahanbakhshi 1
Iman golpour 2
Trahom Mesri Gandshmin 3
Yousef Abbaspour-Gilandeh 4
Shahpour Jahedi Rad 5
1 Ph.D. student, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
2 Department of Biosystems Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
3 Associate Professor, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
4 Professor, Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran.
5 Department of Agricultural Sciences, University of Payam Noor, Tehran, Iran.
چکیده English

The aim of this study was to determine the kinetics, effective moisture diffusivity, activation energy, specific energy consumption, and also predict the moisture content of white mulberry during the drying process with microwave-hot air dryer using mathematical models, Artificial Neural Networks (ANN) and Neuro-Fuzzy Inference System (ANFIS). Drying process was accomplished in three temperature levels (40, 55, and 70°C), three inlet air velocity levels (0.5, 1 and 1.5 m/s) and three microwave power levels (270, 450 and 630 W). To estimate the moisture ratio of white mulberry, 10 mathematical models, ANN and ANFIS were used to fit the experimental data of thin-layer drying. The results showed, the maximum and minimum effective moisture diffusivity during drying was calculated 3.56×10-9 and 3.86×10-10 m2/s, respectively. Also, the minimum and maximum effective moisture diffusivity during drying was achieved 48.54 and 1380.88 Mj/kg, respectively. Among the mathematical models under study, the Page model was the best model for describing the behavior of the thin layer of white mulberry drying. The mean square error (MSE) values for the mathematical models, ANN, and ANFIS were 0.00059, 0.0052 and 0.0044, respectively. Therefore, the ANFIS model with the highest Correlation Coefficient (R2=0.99995), the least percentage of mean relative error (ε=1.84) and mean square error (MSE=0.0044) were used to evaluate the moisture ratio in comparison with other methods implemented in this research Selected as the best model

کلیدواژه‌ها English

White mulberry
Moisture ratio
Effective moisture diffusivity
artificial neural network
ANFIS
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