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Modeling non-Newtonian liquid viscosity: Impact of temperature and concentration on the rate of occupied area variation | ||
| Iran Agricultural Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 28 اردیبهشت 1404 اصل مقاله (767.84 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2025.51629.1648 | ||
| نویسندگان | ||
| Seyed Mehdi Nassiri* ؛ Ramin Rahmany؛ Mohammad Amin Nematollahi | ||
| Department of Biosystems Engineering, School of Agriculture, Shiraz University, Shiraz, I. R. Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژه‌ها | ||
| Artificial neural network؛ Consistency؛ Image processing؛ Mathematical modeling؛ Rheological properties | ||
| مراجع | ||
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