Abstract The design and operational parameters of a solid desiccant wheel (DW), as the heart of a desiccant system, significantly affect the performance of systems in which it is applied. A general and fast model to predict the DW operation is needed to allow investigation of desiccant systems under various working conditions. This paper presents a comprehensive study of the performance prediction of a DW using an artificial neural network (ANN) technique. Comprehensive design parameters, namely rotational speed, channel length, hydraulic diameter, process/regeneration section area ratio, desiccant layer thickness, and the purge angle, are applied as inputs to the ANN model. Moreover, the inlet process and regeneration air temperatures, humidity ratios, and velocities are considered as inputs to confirm that the model outcomes are applicable when various DW parameters are taken into account. Here, the network outputs are the process, the purge, and the regeneration air temperatures and humidity ratios at the DW outlet. This generality in the DW ANN model that contains most of the attributed parameters as inputs and predicts various outputs has not been provided in previous works. A data bank for training the network is generated through transient equations for a wide variation range of DW associated parameters. Several ANN structures are trained in MATLAB to study their prediction accuracy and eventually to find the optimum. The best architecture, which has four hidden layers and 50 neurons, yields a mean relative percentage and a mean square error of 0.54% and 5.9 × 10−5, respectively. This structure can successfully predict DW behavior. Comparing the ANN model predictions and experimental data indicates that the selected structure permits reasonably accurate predictions of the performance of a DW.

中文翻译：

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使用人工神经网络方法对固体干燥剂轮进行综合性能评估

摘要 作为干燥剂系统核心的固体干燥剂轮（DW）的设计和运行参数对其应用系统的性能有显着影响。需要一个通用且快速的模型来预测 DW 操作，以允许在各种工作条件下研究干燥剂系统。本文使用人工神经网络 (ANN) 技术对 DW 的性能预测进行了全面的研究。综合设计参数，即转速、通道长度、水力直径、过程/再生截面面积比、干燥剂层厚度和吹扫角，被用作 ANN 模型的输入。此外，进气过程和再生空气温度、湿度比、和速度被视为输入，以确认模型结果在考虑各种 DW 参数时适用。此处，网络输出是 DW 出口处的过程、净化和再生空气温度和湿度比。包含大部分属性参数作为输入并预测各种输出的 DW ANN 模型中的这种普遍性在以前的工作中没有提供。用于训练网络的数据库是通过瞬态方程生成的，用于 DW 相关参数的广泛变化范围。在 MATLAB 中训练了几个 ANN 结构以研究它们的预测精度并最终找到最佳值。最好的架构有 4 个隐藏层和 50 个神经元，产生的平均相对百分比和均方误差分别为 0.54% 和 5.9 × 10−5。这种结构可以成功预测 DW 行为。比较 ANN 模型预测和实验数据表明所选结构允许对 DW 的性能进行合理准确的预测。