This work aims to develop two main ideas: first, the use of the artificial neural network (ANN) approach to predict the moisture removal rate (MRR) and the dehumidifier effectiveness (ɛ) of a counter-flow liquid desiccant dehumidifier using calcium chloride as an absorption solution. Second, the Garson method is used to identify the most important working parameters influencing the performance of the packed-bed dehumidifier component. A network model was developed in a MATLAB environment based on a multilayer perceptron that included an input, a hidden and an output layer. The network input parameters were the dry and wet bulb temperatures, the air and liquid flow rates, and the liquid desiccant temperature and concentration. The network output included two variables; the MRR and the ɛ. The performances of the ANN predictions were tested using experimental data not employed in the training process. The predicted values were found to be in good agreement with the experimental values, with mean relative errors of less than 4.90% for the MRR and 3.85% for ɛ. In addition, the air wet bulb and dry bulb temperatures were the parameters with the most influence on the MRR and ɛ, with a relative importance of 35% and 25%, respectively.

这项工作旨在发展两个主要思想：首先，使用人工神经网络（ANN）方法来预测使用氯化钙作为逆流液体干燥剂除湿机的水分去除率（MRR）和除湿机效率（ɛ）。吸收溶液。其次，使用Garson方法确定影响填充床除湿机部件性能的最重要的工作参数。在MATLAB环境中基于多层感知器开发了一个网络模型，该感知器包括一个输入层，一个隐藏层和一个输出层。网络输入参数为干球和湿球温度，空气和液体流速s，以及液体干燥剂的温度和浓度。网络输出包括两个变量；MRR和ɛ。使用训练过程中未使用的实验数据来测试ANN预测的性能。发现预测值与实验值非常吻合，MRR的平均相对误差小于4.90％，ɛ的平均相对误差小于3.85％。此外，湿球温度和干球温度是对MRR和influence影响最大的参数，相对重要性分别为35％和25％。