Modeling And Optimization Of A Novel Liquid Desiccant Cooling And Dehumidification System

In recent years,with the development of society and economy and the increase of energy consumption,the traditional vapor compression refrigeration system has been unable to meet the requirements of current society for air conditioning systems since the higher requirement on indoor air quality and system energy efficiency.Therefore,it is urgent to develop a novel more efficient and greener air conditioning system.The liquid desiccant dehumidification system(LDDS)has attracted extensive attention as the high energy efficiency,strong dehumidification ability and can utilize low grade thermal source to realize desiccant regeneration.To achieve more efficient air temperature and humidity control,a novel liquid desiccant cooling and dehumidification air-conditioning system is designed on the basis of traditional LDDS.Meanwhile,some insightful studies on system heat and mass transfer model,system cooling and dehumidification performances and system real-time optimization are carried out to verify and improve the energy efficient of the liquid desiccant cooling and dehumidification system(LDCD)system.The main work and contributions are summarized as follows:1.Designed and developed a novel LDCD system.A novel LDCD system is designed,and the system working principles is introduced in detail.A new experimental platform for liquid desiccant cooling and dehumidification air conditioning system is also built to verify the effectiveness of the designed system.2.Dynamic modeling of the LDCD system.The simplified dynamic models of cooling coil,dehumidifier and cooler were established separately from a control viewpoint based on the laws of conservation of energy and mass,then combined to obtain the model of entire system.The heat and mass transfer rate were derived by using hybrid modeling approach and e-NTU modeling approach.The unknown model parameters were identified by using Levenberg-Marquardt method and unscented Kalman filter algorithm with experimental data.The model validation results indicate that the relative error between the model predictions and the experimental measurements were within 5%.The model can be applied to future studies on system performance investigation and real-time operation optimization.3.Investigation on cooling and dehumidification performance of the LDCD system.The thermal efficiency and moisture effectiveness are adopted as the indicators to study the influences of relevant parameters on system cooling and dehumidification performance.The model predictions are compared with the corresponding experimental data.The results show that the model predictions are good in accordance with the experimental data in terms of the cooling and dehumidification performance trends with varying relevant parameters.The relative errors are less than 10%.Additionality,compared with the conventional LDDS,the feasibility of the liquid desiccant cooling and dehumidification system to reduce the energy consumption of the whole system by reducing the dilution rate of desiccant solution is verified.The desiccant solution dilution rate and energy consumption of LDCD system are decreased by 39.64% and 22.3% over the conventional LDDS,respectively.4.Performance optimization of the LDCD system.Based on the characteristics of energy consuming components in the LDCD system,the system hybrid energy model are established to accurately predict the system energy consumption under different operating conditions.The system total energy consumption and system thermal performance are selected as the performance indicators in the objective function and normalized by introducing a weight factor.The chilled water mass flow rate,desiccant solution mass flow rate and inlet temperature are chosen as the optimization variables.Then an optimization problem with reasonable constraints for LDCD system is formulated.An improved self-adaptive firefly algorithm with self-adaptation of control parameters and improved move function is proposed to solve the optimization problem.The experimental results indicate that the system energy consumption in the proposed optimization strategy is reduced by 12.87% over the conventional strategy.Meanwhile,the energy consumption of chiller system accounts for more than 70% of system total energy consumption.Therefore,it is feasible to achieve more energy efficient by reasonably setting the value of control settings to reduce cooling capacity consumption.