Design and Computational Analysis of Liquid Desiccant Dehumidification Systems

Liquid desiccant dehumidification systems represent a widely adopted air treatment technology extensively applied in air conditioning and humidity control domains. Lithium chloride (LiCl) solution serves as a common desiccant due to its exceptional moisture absorption properties. When designing such systems, mastering key parameters including LiCl solution specific heat, air specific heat, and air enthalpy changes is critical, as these directly influence the system's thermodynamic performance and operational efficiency.

LiCl Specific Heat The specific heat of LiCl solution determines its thermal load during moisture absorption/desorption processes. This value typically varies with solution concentration and temperature, requiring appropriate selection based on actual operating conditions during design calculations. Higher specific heat enables the solution to store more thermal energy during moisture absorption, impacting subsequent regeneration energy consumption. In code implementation, this parameter would be modeled as a function of concentration and temperature using interpolation methods or empirical correlations.

Air Specific Heat Air specific heat primarily calculates sensible heat changes during dehumidification/humidification processes. Since air humidity affects its heat capacity, system simulations must account for humid air's specific heat characteristics to ensure accurate thermal balance computations. Programming implementations often use psychrometric functions to dynamically calculate this parameter based on temperature and humidity ratios.

Air Enthalpy Air enthalpy measures total thermal energy content, encompassing both sensible and latent heat components. In liquid desiccant systems, enthalpy changes during air-LiCl solution contact reflect energy exchange dynamics, directly influencing dehumidification efficiency and cooling load. Algorithm development typically involves psychrometric calculations using temperature and humidity inputs to determine enthalpy values.

System Design and Programmatic Calculations Efficient liquid desiccant system design requires mathematical modeling of heat and mass transfer between air and solution. Programmatic calculation tools facilitate optimization of solution concentration, flow rates, and temperatures to balance dehumidification performance with energy consumption. Through iterative computations, these tools predict system behavior under varying conditions, guiding practical engineering designs. Key functions would include mass balance equations, energy conservation algorithms, and convergence checks for numerical stability.

Understanding these core parameters and their interactions provides theoretical foundation for system optimization, ultimately enhancing operational stability and energy efficiency performance.