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Modeling of thermodynamic properties of carrot product using ALO, GWO, and WOA algorithms under multi-stage semi-industrial continuous belt dryer

机译:多级半工业连续带式干燥机上使用ALO,GWO和WOA算法对胡萝卜产品的热力学性质进行建模

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In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2m/s) three belt linear velocities (2.5, 6.5, and 10.5mm/s), and three air temperatures (40, 55, and 70 degrees C) in triplicate. Three intelligent systems including Ant-Lion-Optimizer (ALO), Grey-Wolf-Optimizer (GWO) and Whale-Optimization-Algorithm (WOA) models were developed to predict the thermodynamic properties of carrot slices including of effective moisture diffusivity (D-eff) and specific energy consumption (SEC). The results revealed that D-eff and SEC values were in the range of 1.77-2.90x10(-9)m(2)/s and 169.77-551.19MJ/kg, respectively. The models of ALO, GWO, and WOA were able to predict the value of D-eff and SEC. The amounts of correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE) for ALO, GWO, and WOA models for predication D-eff were obtained (0.9989, 7.81x10(-12), and 1.50x10(-12)), (0.9993, 5.39x10(-12), and 1.03x10(-12)) and (0.9994, 4.95x10(-12), and 9.54x10(-13)), respectively. In addition, The amounts of R, RMSE, and MAE for ALO, GWO, and WOA model for predication SEC were obtained (0.9983, 0.6700, and 0.1289), (0.9988, 0.5274, and 0.0715) and (0.9996, 0.2566, and 0.0060), respectively. Therefore, model of WOA can be used to easily and accurately predict D-eff and SEC values.
机译:本文采用多级连续带式干燥机(MSCB)干燥胡萝卜片。实验以三种风速(1、1.5和2m / s),三种皮带线速度(2.5、6.5和10.5mm / s)以及三种空气温度(40、55和70摄氏度)进行三次。开发了三个智能系统,包括蚁狮优化器(ALO),灰狼优化器(GWO)和鲸鱼优化算法(WOA)模型,以预测胡萝卜片的热力学性质,包括有效的水分扩散率(D-eff )和比能耗(SEC)。结果显示D-eff和SEC值分别在1.77-2.90x10(-9)m(2)/ s和169.77-551.19MJ / kg之间。 ALO,GWO和WOA模型能够预测D-eff和SEC的值。获得了ALO,GWO和WOA预测D-eff模型的相关系数(R),均方根误差(RMSE)和平均绝对误差(MAE)的数量(0.9989,7.81x10(-12) ,1.50x10(-12)),(0.9993、5.39x10(-12)和1.03x10(-12))和(0.9994、4.95x10(-12)和9.54x10(-13))。此外,获得了用于预测SEC的ALO,GWO和WOA模型的R,RMSE和MAE的量(0.9983、0.6700和0.1289),(0.9988、0.5274和0.0715)和(0.9996、0.2566和0.0060) ), 分别。因此,WOA模型可用于轻松,准确地预测D-eff和SEC值。

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