Abstract In this work, a hybrid (phenomenological/ANN-PSO) model has been developed to simulate the spouted bed drying of deformable solid materials, considering material shrinkage and the physical property variation during drying. Accordingly, an artificial neural network (ANN) model has been coupled to a phenomenological one to describe the heat and mass transfer during the drying of these materials, specifically of guava pieces, in a spouted bed dryer. The optimum architecture of ANN (4–7-3) has been obtained using a Particle Swarm Optimisation (PSO) algorithm. This model demonstrated higher accuracy in its ability to estimate the material physical properties (R2 = 0.99, MSE = 0.00048 and RMSE = 0.069). Furthermore, a comparison between the model results and experimental data provided high correlation. This differs from the usual approach, which neglects variation of the physical properties; the hybrid model is able to simulate the drying deformable particle process behaviour considering the transient variation of the properties obtained from the ANN-PSO model. The results differ significantly from those predicted with the assumption of constant properties.

中文翻译：

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喷射床干燥过程中可变形材料的混合现象学/ANN-PSO 建模

摘要 在这项工作中，考虑到材料收缩和干燥过程中的物理性质变化，开发了一种混合（现象学/ANN-PSO）模型来模拟可变形固体材料的喷射床干燥。因此，人工神经网络 (ANN) 模型已与现象学模型相结合，以描述在喷射床干燥器中干燥这些材料（特别是番石榴块）期间的热量和质量传递。使用粒子群优化 (PSO) 算法获得了 ANN (4-7-3) 的最佳架构。该模型在估计材料物理特性（R2 = 0.99、MSE = 0.00048 和 RMSE = 0.069）方面表现出更高的准确性。此外，模型结果与实验数据之间的比较提供了高度相关性。这与通常的方法不同，忽略了物理性质的变化；考虑到从 ANN-PSO 模型获得的特性的瞬态变化，混合模型能够模拟干燥可变形颗粒的过程行为。结果与假设属性不变的情况下预测的结果有很大不同。