Abstract An original hybrid fuzzy differential-production model of the dynamic drying of a multilayer pellet as a dispersed spherical body was developed, which represents the velocity of the localized evaporation front within the pellet being dried. The model is characterized by using a system of partial differential equations of the thermal conductivity and the drying rate with fuzzy parameters representing the interval values of the thermophysical properties of the dispersed pellet particles, by computational operations for reducing the accumulation of the errors of the applied finite-difference methods, and also by replacing the approximating multidimensional finite-difference system of equations by a set of special fuzzy production rules describing the complex dependence of the velocity of the localized evaporation front on the fuzzy thermophysical and heat-and-mass transfer parameters of the drying process. This makes it possible to perform the rapid calculation of the parameters of the process conditions of the resource-and-energy-saving drying of the multilayer pellet with preventing a decrease in the strength of the pellets and their breakability because of the thermal shock. The development of the proposed hybrid fuzzy differential-production model is based on the modern artificial intelligence methods with taking into account the physicochemical features of drying processes.

中文翻译：

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不确定条件下颗粒动态干燥的混合模糊差异生产模型

摘要 开发了一种原始的混合模糊微分生产多层球团分散球体动态干燥模型，该模型代表了被干燥球团内部局部蒸发前沿的速度。该模型的特点是使用热导率和干燥速率的偏微分方程系统，其中模糊参数代表分散颗粒的热物理特性的区间值，通过计算操作来减少应用误差的累积。有限差分法，并且还通过用一组特殊的模糊产生规则代替近似的多维有限差分方程组来描述局部蒸发前沿的速度对干燥过程的模糊热物理和传热传质参数的复杂依赖性。这使得可以快速计算多层颗粒的资源和节能干燥工艺条件的参数，同时防止由于热冲击引起的颗粒强度和破碎度的降低。所提出的混合模糊差异化生产模型的开发基于现代人工智能方法，并考虑了干燥过程的物理化学特征。这使得可以快速计算多层颗粒的资源和节能干燥工艺条件的参数，同时防止由于热冲击引起的颗粒强度和破碎度的降低。所提出的混合模糊差异化生产模型的开发基于现代人工智能方法，并考虑了干燥过程的物理化学特征。这使得可以快速计算多层颗粒的资源和节能干燥工艺条件的参数，同时防止由于热冲击引起的颗粒强度和破碎度的降低。所提出的混合模糊差异化生产模型的开发基于现代人工智能方法，并考虑了干燥过程的物理化学特征。