In this paper, we report an intelligent model based on ANN to optimize the performance of an internally cooled membrane-based liquid desiccant dehumidifier (IMLDD). IMLDD can effectively mitigate dehumidification deterioration caused by changes in the temperature of the desiccant solution. The mediums of desiccant solution and air are isolated by means of a semi-permeable membrane on both sides in the IMLDD. The temperature of the desiccant solution is reduced by the cooling media that flows through the tubes placed within the solution channels. Generally, many fluid flow parameters like air, cooling water, desiccant solution, etc., play a critical role in controlling the performance of an IMLDD. For our study, inlet air temperature (T_ai), inlet concentration of the desiccant solution (C_dsi), flow rate of the desiccant solution at the inlet $\left({\dot{\mathrm{m}}}_{\mathrm{dsi}}\right),$ and inlet cooling temperature of water (T_cwi) have been considered as the operating parameters/conditions. The outputs or responses namely dehumidification efficiency (η_dh), Exergy efficiency (η_ex), and unmatched coefficient (ξ_um) analyze the performance of the IMLDD. The data comprising of massive input-output was achieved using the response surface methodology (RSM) based central composite design (CCD). Back propagation algorithm (BP), artificial bee colony (ABC), and genetic algorithm (GA) models were used to train the neural network (NN) parameters using the data collected from the CCD based response equation. Forward and reverse mapping models were developed using the trained ANNs. Forward modeling predicts the performance parameters of the IMLDD (i.e., η_dh, η_ex, and ξ_uc) for known combinations of operating parameters (i.e., T_ai, C_dsi, ${\dot{\mathrm{m}}}_{\mathrm{dsi}},$ T_cwi). Similarly, reverse modeling aims at predicting the operating conditions for a known set of performance parameters. The performances of the employed NN models were tested using fifteen arbitrarily generated test cases. The experimental and neural network predicted results were found to be in line with each other for both forward and reverse models. The forward modeling results could assist engineers with off-line tracking, by predicting the response without executing experiments. The reverse modeling prediction will aid in dynamically adjusting the operating parameters to achieve the optimal thermodynamic output characteristics.

在本文中，我们报告了一种基于人工神经网络的智能模型，用于优化内部冷却的基于膜的液体干燥剂除湿器 (IMLDD) 的性能。IMLDD可以有效缓解因干燥剂溶液温度变化而引起的除湿恶化。干燥剂溶液和空气的介质通过 IMLDD 两侧的半透膜进行隔离。流经放置在溶液通道内的管子的冷却介质降低了干燥剂溶液的温度。通常，许多流体流动参数，如空气、冷却水、干燥剂溶液等，在控制 IMLDD 的性能方面发挥着关键作用。在我们的研究中，入口空气温度 (T _ai )、干燥剂溶液的入口浓度 (C _dsi)，入口处干燥剂溶液的流速$\left({\dot{\mathrm{米}}}_{\mathrm{dsi}}\right),$水的入口冷却温度（T _cwi）已被视为运行参数/条件。输出或响应即除湿效率 (η _dh )、火用效率 (η _ex ) 和不匹配系数 (ξ _um) 分析 IMLDD 的性能。包含大量输入输出的数据是使用基于响应面方法 (RSM) 的中心复合设计 (CCD) 获得的。使用从基于 CCD 的响应方程收集的数据，使用反向传播算法 (BP)、人工蜂群 (ABC) 和遗传算法 (GA) 模型来训练神经网络 (NN) 参数。使用经过训练的人工神经网络开发了正向和反向映射模型。正向建模预测 IMLDD 的性能参数（即 η _dh、 η _ex和 ξ _uc）对于已知的操作参数组合（即_Tai、C _dsi、${\dot{\mathrm{米}}}_{\mathrm{dsi}},$T _cwi )。类似地，逆向建模旨在预测一组已知性能参数的运行条件。使用 15 个任意生成的测试用例测试了所使用的 NN 模型的性能。对于正向和反向模型，实验结果和神经网络预测结果被发现是一致的。正向建模结果可以通过在不执行实验的情况下预测响应来帮助工程师进行离线跟踪。逆向建模预测将有助于动态调整操作参数以实现最佳热力学输出特性。