Climate change is a major issue facing the world today and a factor in this is the use of non-renewable, greenhouse gas producing energy sources. To combat this renewable and cleaner solutions are being sought. Heating and cooling systems around the globe are a large energy consumer and with the ever-increasing population and the increasing development of many countries around the world the need for heating and cooling systems is increasing. This lead to much of the recent research produced on the subject being into developing cleaner systems for this purpose. Solar assisted desiccant cooling is a method that uses solar energy, an abundant renewable energy, to facilitate cooling as well as avoiding the use of harmful chemicals sometimes found in conventional air conditioning.
Desiccant cooling systems consist of a dehumidifier and a regenerator to handle the latent load and a sperate part to handle the sensible load. The desiccant material inside the dehumidifier and regenerator absorbs the water vapour from the air to control the humidity. After this the desiccant is heated using additional heat sources. Typically known as reactivating the desiccant. One of the advantages of using desiccants is that low quality heat sources can be used to regenerate it for example solar energy is frequently used. There are typically two types of desiccant, solid and liquid. Solid desiccant cooling typically uses a rotary wheel as a dehumidifier/regenerator whereas liquid desiccant uses membranes and pumps to move the desiccant around. In the cooling system to handle the sensible load including evaporative cooling both indirect and direct, adsorption cooling and the traditional vapour compression method.
To analyse the system, it is frequently modelled in simulation software packages which can then be validated against experimental results and numerical methods. The two software packages, TRNSYS and MatLab, can both be used to model the desiccant cooling system individually. When combined into a co-simulator they can offer greater flexibility, expand on TRNSYS modelling capabilities and allow the system to be effectively controlled.
Recent reviews on desiccant cooling systems have shown their potential when compared to other methods but have also shown the need for further development and testing of the system and simulation software [7-9]. Extensive research has been done into different types of desiccant cooling systems and techniques as discussed below. Crofoot et al conducted a study on the performance of an LDAC system while Mohaisen and Ma developed a LDAC system with an integrated evaporative cooler and cooling tower. Studies have done on integrating a Maisotsenko cooling cycle in an LDAC. Gadalla and Sahafifar Bourdoukan et al investigated a solar desiccant system using solely solar energy for regeneration. There are several types of LDAC with membrane LDAC systems used to eliminate the problem of desiccant droplet carry over and have been widely studied.
Another variation is the use of two stage and multi stage desiccant cooling systems to raise the COP with Tu and colleagues studying the performance of a two-stage system. Crofoot et al performed a study on a modular multi stage system while also considering the affect cooling water temperature had. Gadalla and Saghafifar optimized the air precooling in a multi stage solid desiccant system. A hybrid system using the traditional vapour compression is also feasible and is compared to other systems. Finocchiaro et al studied the use of air to air wet heat exchangers to maximise evaporative cooling. The use of desiccant coated heat exchangers has also been studied . Gao and colleagues performed a numerical study on solid desiccant system. A review into rotary desiccant wheel cooling systems showed there was a gap in the research on how to handle unpredictable solar energy. Thermal storage is used to help overcome this problem by providing energy in low solar radiation periods. Kabeel and colleagues investigated how to increase solar efficiency with thermal storage and Kabeel and Abdelgaied studied using PCM as thermal storage medium in desiccant cooling systems. Thermal storage is used to increase the solar efficiency by storing thermal energy in times of high solar exposure. Kabeel and colleagues performed a study using a liquid desiccant air conditioning system on how changing thermal storage size affects solar fraction. It was found that to increase solar fraction from 90% to 100% the tube array needed to be increased by 20%. It was also found that using a 90% solar fraction was more economically beneficial. Kabeel and Abdelgaied investigated the potential savings from using phase change material and solar energy on a desiccant system. Using both in the regeneration was found to have savings of 75.82% in comparison to an electrical heater.
There are two common types of desiccant solid and liquids. Gomez-Castro performed a review into the indirect and direct regeneration of liquid desiccants and Fu and Liu reviewed the impact liquid desiccants have on air quality. Mujahid Rafique et al performed a review of liquid desiccant materials and Mohammad et al performed a review on the history of LDAC technology . Solid desiccants have also been studied extensively with Sultan et al performed an overview of solid desiccants for dehumidification. Mandegari and Pahlavanzadeh studied solid desiccants in rotary wheels.
The objective of this review is to study the applications of a TRNSYS-MatLab co-simulator within a desiccant cooling system. Also, to study the advantages of using them in conjunction compared to using each software on its own, in addition any advantages that may exist over other available software packages.
Both TRNSYS and MatLab can be used on their own to model and simulate a solar assisted desiccant cooling system. The software TRNSYS has been used to study the effectiveness of solar cooling systems in many different locations. Baniyounes and colleagues developed a TRNSYS simulation for a solar desiccant cooling system to show its potential for use in Queensland, Australia. It was found that the system could deliver comfort conditions with lower energy usage and emissions. In Egypt a solar liquid desiccant air conditioning (SLDAC) system was investigated using a TRNSYS simulation for the changes that occur with the variation of the solar collector area. The findings show that as the area increases the regenerator COP and the rate of water in the desorbed also increases. A study of a solar assisted cooling system in Pakistan using an absorption chiller was conducted using TRNSYS and it was found that with a hot water storage tank and evacuated tube collector of 12m2 the temperature of typical house could be maintained within comfort levels. A study by Fong et al was done on solar assisted desiccant air conditioning (SADC) systems in a typical office setting to show its feasibility in Hong Kong. The system developed was found to be a greener solution to conventional refrigeration systems based on the performance indicators of solar fraction and COP. Using TRNSYS the effect of dehumidification capacity on the effectiveness of SADC systems in Malaysia. After four configurations were simulated it was concluded that high dehumidification capacity leads to an increase in the performance of a SADC system and improves its ability to provide thermal comfort conditions in residential buildings. Zendehboudi et al studied a SADC system in different areas of Iran using TRNSYS simulator. They found that the performance of the system was linked to the ambient air humidity ratio with the higher the ambient air humidity ratio the lower the COP.
MatLab software is used to model the solar desiccant cooling system or just individual sections of it. Aly et al used MatLab to model a solar powered desiccant regenerator to investigate the effect of different conditions including operating parameters and ambient conditions on the performance of the system. A finite different approach was used for development and wo desiccant materials were observed during the experiment. Parmar and Hindoliya developed a model using a neural network, MatLab simulator and experimental data to evaluate a desiccant wheel from the cooling system. The model can then be used to predict specific humidity and the temperature at the outlet of the system. Zeidan and colleagues modelled and solved a system of equations for a solar powered liquid desiccant regenerator using MatLab. They showed the effect of parameters such as regenerator length and flow rate and concentration of desiccant solution as well as investigating the maximum coefficient of performance for the system at different operating conditions. Li et al conducted a study on a cross flow liquid desiccant regenerator using MatLab to solve the control equations. The finite difference method was used, and experimental results were introduced to the model and it was found that was an optimal number of mass transfer units depending on the operating conditions. The modelling of a desiccant wheel was carried out using MatLab by Nia et al. Experimental results from published works were used to validate the model and the model was used to develop correlations between outlet conditions and input variables. MatLab is also regularly used to solve systems of equations from numerical models as Islam and colleagues used to solve ordinary differential equations when studying mass and heat transfer in liquid desiccants in relation to cooling and dehumidification. They also studied the energy performance enhancement potential of te system, found to be up to 25%.
As TRNSYS is a modular software MatLab can be easily integrated with it. Also, there is a history of the two being used together within traditional Heating, Ventilation and air conditioning systems. A MATLAB-TRNSYS co-simulator can be used in many situations. One of them is to allow control systems to be simulated for the system usually in MatLab.
To ensure the system runs as efficiently as possible a suitable control system should be used. In liquid desiccant systems the flowrate of desiccant can be controlled to maintain comfort conditions and optimal operating parameters. In solid desiccant systems with a desiccant wheel the rotation speed can be controlled for a similar effect. Studies into suitable control systems have included fuzzy-PID. Panaras et al proposed a control strategy for desiccant systems and Ge and colleagues studied several strategies. Ha et al modelled and optimised an energy efficient hybrid solar air conditioning system and Aprilea and colleagues optimised the control of desiccant cooling system with an electrical heat pump. One method of control is predictive control where past readings are used to predict the future and adjust the system accordingly. Bosschaerts et al developed a model predictive control system for heating buildings, Sohani and colleagues used predictive models for performance analysis of desiccant cooling systems. Herrera et al used a hybrid predictive control method to manage the production and consumption of solar absorption cooling systems for thermal comfort.
Alibabaei et al used a MATLAB-TRNSYS co-simulator to control the HVAC system for a residential house and comparing the effectiveness of different methods to highlight the effectiveness of the co-simulator. The three methods were load shifting, smart dual fuel switching system and a combination with each having different saving with fuel switching having the greatest saving over the simulation period. Bava and Furbo used a MATLAB-TRNSYS co-simulator to compare with a large simulator collector field and the simulator was found to be consistent with real life readings in different weather conditions. Fernandes and colleagues developed a co-simulator of a thermal energy adsorption storage system using GenOpt to optimise the performance of the system. It was found that increasing the heat transfer area and absorbent mass improves savings by up to 16% compared with a similar conventional storage system. A MATLAB-TRNSYS co-simulator was used to simulate the control of a heating system using model predictive control (MPC). Two different types of MPC were used traditional and Laguerre functions to show that the controllers were feasible for the system. Reddy et al used a TRNSYS-MatLab link for controlling the system. An MPC controller is used to control the flow rate of desiccant to produce the optimal absorption efficiency.
A TRNSYS MatLab co-simulator is also used to allow MatLab to simulate specific models within the system. A common component to be modelled in MatLab is the desiccant wheel for example Li et al modelled a desiccant wheel for a two-stage system based on their previous research [66-69]. Safizadeh and colleagues used a TRNSYS-MatLab co-simulator to model a two-stage air-dehumidification system with a membrane unit and a desiccant adsorption unit. The membrane unit was modelled in MatLab based on a finite difference approach and coupled with the TRNSYS model. Safizadeh et al also when optimizing a heat assisted air conditioning system used a co-simulator. Detailed models for the membrane unit and other parts were developed in MatLab and coupled with TRNSYS. The optimized system was found to save up to 50% of the lifetime cost. Eldeeb et al studied the applicability of a heat and moisture transfer panel in an office building using the co-simulator. Matlab was used to model the moisture gain from the system and then integrated into the TRNSYS system and the system was found to be energy efficient. In another instance a model of several configurations of solar desiccant and evaporative cooling systems were developed and then produced in MatLab. These were then validated by comparison with the models from the standard TRNSYS library and with experimental results.
Another potential use of a MatLab-TRNSYS co-simulator is that Matlab expands on the potential of TRNSYS to solve some of the more complicated optimization problems, in addition it allows for a much greater degree of flexibility. Using a previously developed artificial neural network to predict the system performance Rasouli et al to assess a run-around membrane energy exchanger using desiccant within a HVAC system. The artificial neural network was developed in Matlab and data was exchanged between it and the HVAC system simulated in TRNSYS. This was done to find the optimal operating conditions as the environmental conditions varies. Al-Alili and colleagues used a co-simulator to optimize a hybrid solar air conditioner using a desiccant wheel. Two optimization problems were expressed using a variety of design variables. To optimize them both simultaneously a Multi-Objective Genetic Algorithm was used that both satisfied the constraints and kept within the design boundaries with results presented with a pareto front.
Other software packages include ESP-r which is modelling tool for building performance simulation (BPS). ESP-r uses a partitioned solution approach to model buildings including parameters such as heat, air flow, moisture and light. ESP-r is best used for detailed and accurate building modelling. This is because its strengths are in predicting the indoor and outdoor environment interactions as well as modelling the building fabric. Although it has its own HVAC models integrated into the software it is frequently paired with TRNSYS as the model library is lacking and development of new models is complicated and requires professional expertise. The co-simulator runs by sharing information at each time step in the simulation used a third program Harmonizer. ESP-r is best used when the building envelope needs to be modelled more rigorously than possible in TRNSYS and fulfils a different function to when a MatLab co-simulator is used.
Another BPS is Energy Plus which can be used as a replacement to TRNSYS with both containing many similar assets. The main asset of Energy Plus over TRNSYS is its ability to link with the AutoCad Software tool. Sousa performed a review and comparison between BPS software and found TRNSYS to be the most complete although depending on the project and the user others could be found to be more suitable.
Engineering Equation Solver (EES) is also used alongside TRNSYS in a similar role to MatLab as it is a powerful mathematical program. One of EES advantages is it has a large database of thermophysical and mathematical properties. EES is used to model components that do not have a model in TRNSYS’ library, for example Tashtoush et al used EES to model ejector cooling in a solar collector subsystem. The disadvantage of EES is that it has a narrower purview than some other similar software.
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