مدل‌سازی سینتیک خشک کردن برش‌های لیموترش به روش تابش مادون قرمز با استفاده از شبکه‌های عصبی GMDH هیبریدی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی شیمی، دانشکده فنی و مهندسی، دانشگاه بناب

2 استادیار، گروه بهداشت مواد غذایی و آبزیان، دانشکده دامپزشکی، دانشگاه فردوسی مشهد

چکیده

مدل‌سازی سینتیک خشک کردن، یکی از راه‌های مناسب برای کنترل زمان و شرایط خشک کردن می‌باشد. در این تحقیق، سینتیک خشک‌ کردن برش‌های لیموترش با ضخامت‌های 5 و 10 میلی‌متر در یک خشک‌کن مادون قرمز آزمایشگاهی و در دماهای 100، 125، 150 و 175 درجه سانتی­گراد بررسی شد. برای مدل‌سازی سینتیک خشک کردن برش‌های لیموترش، از 7 مدل ریاضی رایج خشک‌کردن لایه نازک استفاده شد. هم‌چنین  برای این بررسی، از روش شبکه‌های عصبی GMDH هیبریدی چهار لایه (یک لایه ورودی، دو لایه مخفی و یک لایه خروجی) استفاده گردید. نتایج نشان داد که شبکه عصبی GMDH هیبریدی بهینه، دارای دقت بالایی در تخمین محتوای رطوبتی برش‌های لیموترش طی فرایند خشک کردن بوده که این دقت      (9909/0R2 =) حدود دقت مدل پیج به‌عنوان بهترین مدل ریاضی به‌کار برده شده بود (9950/0-9793/0R2 =). بررسی میزان حساسیت محتوای رطوبتی نسبت به متغیرهای ورودی نشان داد که مقدار رطوبت طی خشک‌شدن مادون قرمز به زمان خشک شدن بیش از سایر متغیرها حساس است (45 درصد). افزایش دما از 100 به 175 درجه سانتی‌گراد سبب افزایش ضریب نفوذ موثر (Deff) از 10-10× 90/9 به m2/s 9-10× 76/2  گردید. مقدار انرژی فعال‌سازی برای برش‌های لیموترش kJ/mol 61/87 به‌دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Infrared drying kinetics study of lime slices using hybrid GMDH-neural networks

نویسندگان [English]

  • Yousefi Ali Reza 1
  • Naser Ghasemian 1
  • Amir Salari 2
1 Department of Chemical Engineering, Faculty of Engineering, University of Bonab, Bonab, Iran
2 Department of Food Hygiene and Aquaculture, Faculty of Veterinary Medicine, Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

Modeling of drying kinetics is one of the most appropriate methods to control the time or any condition related to the drying process. In this research, the drying process of Persian lime slices with 5 and 10 mm thicknesses was conducted at four temperature levels of 100, 125, 150 and 175 °C in an experimental infrared dryer. For modeling of the drying kinetics of lime slices, 7 well-known thin-layer models were used. In addition, for this modeling, a hybrid GMDH-neural network with four layers (one input, two hidden and one output layer) was applied. The results demonstrated that the optimized GMDH model was completely efficient to predict the moisture content of lime slices during infrared drying process (R2 = 0.9909). This efficiency was almost similar to the observed efficiency for the Page model as the best mathematical model used (R2 = 0.9793-0.9950). It was found that the sensitivity to the drying time was more than other inputs, so that sensitivity of this parameter was near 45%. Increase in temperature resulted in an increase in the effective diffusivity coefficient (Deff), so that this coefficient reached to 2.76×10-9 at 175 °C from the initial value of 9.90×10-10 at 100 °C. The activation energy (Ea) calculated for the lime slices was 87.61 kJ/mol.

کلیدواژه‌ها [English]

  • Lime slices
  • GMDH-Neural network
  • Infrared drying
  • Mathematical modeling
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