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Maintaining human thermal comfort and extensive power consumption of the conventional heating ventilating and air-conditioning systems drive the researchers to look for an alternative dehumidification system. Among the potential alternatives, the liquid desiccant dehumidification system has evolved as a long-term renewable and less power consuming system due to its effective controlling capability of humidity. In the present study, an experimental investigation is carried out to demonstrate the performance of lithium bromide (LiBr) and potassium formate (HCOOK) desiccant solution mixture in a spray type counterflow-packed bed adiabatic dehumidifier. Subsequently, ternary plots and 3D-contour trade-off paradigms are patterned to assess the performance parameters using an optimal mixture design methodology. Additionally, empirical correlations are developed to pattern the performance parameters (moisture removal rate (MRR), enthalpy effectiveness (ξh) and moisture effectiveness (ξm)) as a function of blend combinations. Further, the present study endeavours to explore the application of offline calibration methods based on a balanced optimum mixture design-centred desirability approach for optimizing the mixture proportion to obtain the optimal performance parameters (i.e., MRR, ξh and ξm). The wettability area is explained based on the static contact angle on a plane glass. The results showed that the contact angle was decreased from 47.3° to 44.1° for the optimum mixture of LiBr solution (37.2%-LiBr +17.8%-HCOOK), which further confirmed the higher MRR. Lastly, the tuning effect of the liquid to gas flow rate on the dehumidifier performance and economic viability of LiBr-HCOOK blends are studied.

Current research in dehumidification systems is mainly focused on thermally driven liquid desiccant systems. However, the selection of proper desiccant solution has a greater significance on the performance of a dehumidifier. In this perspective, current work is presented with governing equations of energy, mass and species balance to study the performance of a dehumidifier. The governing equations are solved using finite difference method coupled with Jacobi’s model for an air–desiccant contact system of a cross-flow packed bed adiabatic dehumidifier operating with different desiccant blends of lithium chloride (LiCl) and calcium chloride (CaCl2) solution. The effect of variation in blend proportion on outlet parameters of a packed bed dehumidifier is patterned under various desiccant to air (L/G) mass flow rate ratios. It is observed that the addition of CaCl2 desiccant solution into LiCl changes the nature of thermo-physical properties of blends‚ which are predicted using non-random two liquid (NRTL) equation. Further, a trade-off analysis among the performance parameters is presented. It demonstrates that dehumidifier performance paradox such as enthalpy effectiveness (ɛh), moisture removal rate (MRR) and moisture effectiveness (ɛm) have a tendency to be optimal for 35% LiCl with 5% CaCl2 blend (BL1) among all blends at L/G ratio of 2. The variation of thermodynamic properties (air temperature, humidity, desiccant temperature and desiccant concentration) inside the dehumidifier module is visualized in terms of contour plots. Furthermore, the irreversibility in heat and mass transfer operation for the optimal blend is investigated in terms of physical and chemical exergy destruction paradigm. The result of such investigation elucidated that the maximum chemical exergy destruction is 0.88 kW, whereas the physical exergy destruction is 0.06 kW.

Recently, sorption technology has taken significant attention as an alternative process to mechanical air conditioning systems for dehumidification of air without lowering the air dry-bulb temperature below its dew point temperature. In this perspective, an aqueous lithium chloride (LiCl) is considered as a more effective desiccant in desiccant sorption systems due to its higher moisture absorption capability. In the present study, an effort is made to develop a simple correlation to predict the vapour pressure of LiCl solution as a function of solution temperature and concentration. Based on the developed correlation, an Othmer chart is constructed for LiCl solution in the concentration range of 20–40% and 15–50 °C temperature range. A trade-off analysis has also been carried out among the performance parameters. It is shown that venture of optimum outcome is observed at solution to air flow rate ratio of 8.5 with a condensation rate, moisture and enthalpy effectiveness of 0.234 g/s, 71.5% and 72.5%, respectively. Subsequently, empirical regression models are developed for predicting the performance parameters, which reveals that the developed models are robust for surrogate modelling in dehumidifier testing paradigms.

In last few decades, a substantial advancement has been observed in dehumidifier modules for their energy-conservative and environment-friendly nature; however, still uncertainty in decision-making is of concern in selection of optimum input parameters to attain an effective performance. In this perspective, an experimental study is performed in an adiabatic packed bed, counter-flow liquid desiccant dehumidifier fuelled with the blend of lithium bromide and calcium chloride solution at various operating conditions with different control settings. It is observed that air mass flux rate (Fa), solution concentration (ζ) and air specific humidity (ωa) have significant impact on condensation rate (CR), whereas, air mass flux rate (Fa) and solution temperature (Ts) are the most effective parameters for moisture effectiveness and latent heat factor, respectively. The experimentally obtained results for multiple response performance characteristics are represented based on inlet process parameters using gene expression programming (GEP). The proficiency of developed GEP meta-models is provided by statistical parameters, Taylor diagram and Theil uncertainty. Subsequently, GEP meta-model based fuzzy logic is developed for optimising dehumidifier inlet process parameters in terms of multi-responsive performance characteristics using Genetic Algorithm. The optimisation results showed that dehumidifier performance characteristics have a tendency to become optimal at Fa = 0.766 kg/m2-s, Ta = 30.745 °C, ωa = 0.023 kgwv/kgda, Fs = 1.812 kg/m2-s, Ts = 24.01 °C, and ζ = 48.1%. Finally, experiments were conducted at optimum conditions and observed proximity between the predicted and experimental outcomes, which further validates the current optimisation approach.