首页|基于ANN模型的内冷型溶液除湿器性能研究

基于ANN模型的内冷型溶液除湿器性能研究

扫码查看
溶液除湿器因可被低品位热能驱动,且具有除湿效率高等优点而受到广泛关注,但其传质性能的预测准确度还有待提高。本文搭建了单通道内冷型溶液除湿实验平台,研究了不同参数对于除湿过程中传质性能的影响,同时,建立了基于MATLAB平台的人工神经网络(ANN)模型用于预测传质性能,并用上述实验数据对该ANN模型进行了验证。结果表明,ANN模型预测得出的Sh与实验Sh平均绝对相对偏差(MARD)为4。07%。与现有经验公式相比,建立的ANN模型预测精度更高。此外,还利用ANN模型研究了不同参数变化下的Sh的变化趋势,从而分析不同参数对除湿性能的影响。
Investigation on Performance of Internally Cooled Liquid Desiccant Dehumidifier Based on ANN Model
Liquid desiccant dehumidifiers have received widespread attention due to their ability to utilize low-grade thermal energy and high dehumidification efficiency.However,the prediction accuracy of their mass transfer performance still needs to be improved.This article built up an experimental platform of a single-channel internally cooled liquid desiccant dehumidifier to study the effects of different parameters on its mass transfer efficiency.Meanwhile,an artificial neural network(ANN)model was established with MATLAB to predict the mass transfer efficiency of the dehumidifier.The ANN model was verified and validated with the above experimental data.The results indicated that the a mean absolute relative difference(MARD)between the predicted Sh of the ANN model and the experimental Sh was 4.07%.Compared with existing empirical formulas,the ANN model established in this paper had higher prediction accuracy.In addition,this article also used the ANN model to study the trend of Sh under different parameter changes,thereby investigating the impact of different parameters on the dehumidification performance.

machine learningneural networks(NN)liquid desiccant dehumidifierparameterization investi-gation

罗伊默、常亚银、李念平

展开 >

湖南大学 土木工程学院,湖南 长沙 410082

机器学习 神经网络 溶液除湿器 参数化研究

2024

湖南大学学报(自然科学版)
湖南大学

湖南大学学报(自然科学版)

CSTPCD北大核心
影响因子:0.651
ISSN:1674-2974
年,卷(期):2024.51(9)