Modeling non-Newtonian liquid viscosity: Impact of temperature and concentration on the rate of occupied area variation

Document Type : Research Paper

Authors

Department of Biosystems Engineering, School of Agriculture, Shiraz University, Shiraz, I. R. Iran

Abstract

The rheological behaviors of liquid and semi-solid foods during production processes play a critical role in the design and optimization of processing equipment. Consequently, continuous monitoring of properties such as the viscosity of non-Newtonian liquids is essential in food processing industries. To address this need, the present study explores the feasibility of using image processing techniques to estimate the apparent viscosity of non-Newtonian fluids. Both regression analysis and artificial neural network (ANN) models were developed to interpret the measured data. The regression models yielded coefficients of determination (R²) ranging from 0.96 to 1.00, with average absolute estimation errors between 0.02% and 15.27%. Additionally, two ANN architectures, multilayer perceptron (MLP) and radial basis function (RBF), were evaluated for their predictive performance. The high correlation coefficients achieved during both training and testing phases indicate the strong predictive capability of these models. Overall, the findings support the use of image processing, specifically through analysis of surface area variations, as a viable and accurate approach for estimating the apparent viscosity of non-Newtonian liquids in food processing applications.

Keywords

Main Subjects

Article Title [Persian]

مدل‌سازی گرانروی مایع غیرنیوتنی: تأثیر دما و غلظت بر نرخ تغییرات سطح اشغال‌شده

Authors [Persian]

  • سید مهدی نصیری
  • رامین رحمانی
  • محمد امین نعمت اللهی
بخش مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ج. ا. ایران
Abstract [Persian]

رفتار مواد غذایی مایع و نیمه جامد در طول فرآیندهای تولید اهمیت قابل توجهی در روند طراحی تجهیزات دارد. بنابراین، اندازه ­گیری مداوم خواصی مانند گرانروی مایعات غیرنیوتنی در صنایع تبدیلی بسیار مهم است. برای پرداختن به این چالش‌ها، این مطالعه امکان استفاده از روش­ های پردازش تصویر برای اندازه‌گیری گرانروی مایعات غیرنیوتنی را بررسی کرد. برای مدل سازی داده ­های اندازه­ گیری شده از روش رگرسیون و شبکه عصبی مصنوعی استفاده شد. دامنه تغییرات ضریب تعیین مدل‌های رگرسیونی بین 0/96 و 1 به دست آمد و درصد میانگین خطای مطلق تخمین از 0/02 درصد تا 15/27 درصد متغیر بود. نتایج به‌دست‌آمده از دو مدل شبکه عصبی، یعنی پرسپترون چندلایه و تابع پایه شعاعی، اثربخشی آن ها را در پیش‌بینی گرانروی ظاهری نشان داد. ضرایب همبستگی بالا مشاهده شده در مراحل آموزش داده و آزمایش، عملکرد موفق شبکه ­های عصبی را تایید می ­کند. این تحقیق قابل اعتماد بودن پردازش تصویر تغییرات سطح یک مایع غیرنیوتنی را برای ارزیابی گرانروی ظاهری تأیید می ­کند.

Keywords [Persian]

  • پردازش تصویر
  • خصوصیات رئولوژی
  • شبکه عصبی مصنوعی
  • قوام
  • مدل‌سازی ریاضی

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Iran Agricultural Research
Volume 44, Issue 1
Proofs
April 2025
Pages 66-77

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