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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]

Liquid desiccant dehumidification technology as an efficient way of handling moisture load has attracted much attention, while the performance of liquid desiccant dehumidifiers has characteristics of uncertainty and time-varying in actual runtime, limiting its application fields. Thus, it is necessary to develop dynamic control methods to achieve stable and efficient operation. As the liquid desiccant dehumidification process is a multi-parameter, time-varying and large delay nonlinear dynamic process, therefore it is difficult to achieve the dynamic control. In this paper, dynamic control methods of the liquid desiccant dehumidification process were studied experimentally for the first time and main difficulties in the dynamic control of the liquid desiccant dehumidification process were presented clearly, helping to learn more knowledge on dynamic control process and facilitate the development of control methods. PID control as a classic control method was applied to control the liquid desiccant dehumidification process firstly, the result showed that it requires long adjusting time to follow the command and is not suitable to control nonlinear dynamic process with large delay. Novel fuzzy logic control (FLC) as an advanced control method was implemented as well, the result demonstrated that it follows command quickly while is unable to maintain the new set point accurately. Then reasons for shortcomings of each control method were analyzed. Along with the above analysis, a new control method, combining the PID control and novel FLC was developed. The command following experiment and disturbance rejection experiment for the newly developed control method were conducted. Control performances are great and validate the feasibility of the new control method. Besides, the newly developed control method also provides a good reference for achieving the dynamic control of a multi-parameter, time-varying, nonlinear dynamic process with large delay. [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]

Liquid desiccant dehumidifiers (LDDs) has attracted much attention due to its advantages of energy-saving and environmental protection. However, dynamic characteristics of LDDs, which are essential to develop appropriate control methods, are still not clear at present. In this paper, the performance of a packed LDD was investigated under changes of desiccant inlet temperature and flow rate as well as air inlet humidity ratio, temperature and flow rate. Due to the random packings and interaction between air and desiccant, the air is normally distributed randomly during a dynamic process of LDDs and air outlet humidity ratio at the sampling point is serrated with time going. It was found that exponent function could be utilized to smooth the experimental result of air outlet humidity ratio, providing a simple way to determine the evaluation indexes like the response delay and response time as well as response rate, which could facilitate to evaluate dynamic characteristics of the dehumidifier. And through comparing those evaluation indexes corresponding to changes of different inlet parameters, it was revealed that air outlet humidity ratio was more sensitive to the change of desiccant or air flow rate, but not desiccant inlet temperature as indicated in a previous research getting from quasi-steady state experiments. Therefore, it would be more reasonable to select the desiccant or air flow rate as control variables instead of desiccant inlet temperature. Furthermore, preliminary control experiments were conducted to compare the performance of control algorithms developed based on the steady state characteristics and expert experience of dynamic characteristics. Results show that the control algorithm developed based on the expert experience of dynamic characteristics performs much better, which indicates that the obtained steady state characteristics in existing researches are unsuitable for developing control algorithms for a packed LDD. The dynamic characteristics revealed in current research paves the way for the future development of appropriate control algorithms. [ABSTRACT FROM AUTHOR]