Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

作     者：S.M.Atiqure Rahman Hegazy Rezk Mohammad Ali Abdelkareem M.Enamul Hoque Tariq Mahbub Sheikh Khaleduzzaman Shah Ahmed M.Nassef 

作者机构：Department of Sustainable and Renewable Energy EngineeringUniversity of SharjahSharjahUAE College of Engineering at Wadi AddawaserPrince Sattam Bin Abdulaziz University11911Al-KharjSaudi Arabia Electrical Engineering DepartmentFaculty of EngineeringMinia University61517MiniaEgypt Chemical Engineering DepartmentFaculty of EngineeringMinia University61517MiniaEgypt Department of Biomedical EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh Department of Mechanical EngineeringMilitary Institute of Science and TechnologyDhakaBangladesh Department of Infrastructure EngineeringMelbourne School of EngineeringThe University of MelbourneAustralia Computers and Automatic Control Engineering DepartmentFaculty of EngineeringTanta UniversityEgypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第6期

页      面：3143-3160页

核心收录：

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

主　　题：Artificial neural network prediction modeling osmotic drying kinetics 

摘      要：The main goal of this research is to develop and apply a robust Artificial Neural Networks(ANNs)model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact *** salt and sugar solutions were used as the osmotic solutions at 27◦*** of experiments were performed at various temperatures of 35◦C,40◦C,and 55◦C for conduction heat input under vacuum(−760 mm Hg)*** experiments were also performed in a pure vacuum without heat *** moisture content(DMC),effective moisture diffusivity,and mass flux were considered as the performance parameters in this *** revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87%compared to 5.3%in the salt ***,a significant enhancement of drying performance of about 27%in DMC was observed for both samples at vacuum and 40◦C compared to pure vacuum drying *** the experimental data,a robust artificial neural network(ANN)was proposed to describe the osmotic dehydration’s behavior on the drying *** ANN model outputs are the dimensionless moisture contents(DMC),the diffusivity,and the mass *** the ANN inputs were the drying time,the percent of sugar solution,and the percent of salt *** the ANN apple’s model,the minimum root mean square error(RMSE)values were 0.0261,0.0349 and 0.0406,for DMC,diffusivity,and mass flux,*** the best correlation coefficients of the above three parameters’determination values were 0.9909,0.9867 and 0.9744,*** the ANN potato’s model,the minimum RMSE values were 0.0124,0.0140 and 0.0333,for DMC,diffusivity,and mass flux,*** the best correlation coefficients of the parameters’values were found 0.9969,0.9968 and 0.9736,***,the ANN model’s prediction has a perfect agreement with th