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Liquid desiccant dehumidification, a clean energy technique for creating comfortable environments has received a lot of interest. Several dynamic models have been proposed to predict the outlet air humidity ratio and temperature, however, a study on presenting variations of heat and mass transfer coefficients and thermal mass of desiccant during the dynamic dehumidification process was not encountered according to authors' best knowledge. This gap is primarily due to the complex nature of the coupled heat and mass transfer processes, which make it challenging to capture the evolution of heat and mass transfer coefficients over time. In this study, variations of these parameters are analyzed at first. A theoretical study is conducted to show the thermal mass of the desiccant can be considered constant when changing the inlet air humidity ratio or desiccant temperature, simplifying the dynamic modeling process. While the variation of heat and mass transfer coefficients show significant impact in such conditions. Subsequently, a new method based on mass conservation is provided to determine the thermal mass of desiccant. By examining the cause-and-effect relationships between heat and mass transfer coefficients and outlet air humidity ratio, the evolution of heat and mass transfer coefficients following exponential laws is deduced by inversion. Then a dynamic model of packed dehumidifiers can be completed easily based on above derivations, and its viability is confirmed by experimental results. Compared to the existing method, the prediction accuracy can improve by 50% based on the proposed methods. This study characterizes evolution rules of heat and mass transfer coefficients over time, and with this, evolutions of outlet air parameters during the dynamic dehumidification process can be acquired conveniently based on the existing steady dehumidification model. • Variations of heat and mass transfer coefficients and thermal mass of desiccant are analyzed. • Simple methods are proposed to figure out the evolutions of thermal masses, and heat and mass transfer coefficients. • The prediction accuracy of air outlet humidity ratio could improve by 50% based on the proposed methods. [ABSTRACT FROM AUTHOR]

Researches on packed liquid desiccant dehumidifiers are abundant. However, only a few works concentrate on modeling the dynamic dehumidification process as it requires detailed heat and mass transfer coefficients along the dehumidifier, which are impacted by many factors such as flow pattern of liquid desiccant and hard to be obtained, hindering the development of dynamic models. Data driven modeling method could eliminate the requirement for detailed heat and mass transfer coefficient along the dehumidifier. A time delay neural network (TDNN) model is a general way to describe the dynamic process without the requirement of much knowledge on physical mechanism, yet the number of time steps for each inlet parameter is hard to be determined. In this paper, an improved time delay neural network (iTDNN) model was proposed. Through analyzing characteristics of dehumidification process, model inputs were simplified, which is the core of improvement of TDNN model. Besides, a new combined algorithm including back propagation (BP) algorithm and improved genetic algorithm (GA) was proposed to acquire the optimal weight values globally. The predicted results agree well with the experimental data for validation part. The method presented in the paper provides a reference for modeling multi-variables and large delay nonlinear dynamic systems. [ABSTRACT FROM AUTHOR]