Improving the efficiency of the desiccant wheel powered by renewable energy under different environmental conditions

The main advantage of using thermally driven cooling systems as a standalone system or combined with conventional systems is to reduce the amount of consumed electrical energy. Therefore, energy performance analysis is a key factor when designing, operating, and optimising thermally based air conditioning systems. A Solid Desiccant Cooling (SDC) system, powered by low or medium temperature heat from renewable energy sources, has the following benefits compared with other systems that can be used for cooling: the separation of dehumidification and cooling enables demand‐oriented air conditioning which can help to reduce peak power requirements by using desiccant‐assisted air conditioning systems, it is environmentally-friendly being Chlorofluorocarbon (CFC) free, controls humidity and temperature independently, and has a low lifetime operating cost. The SDC system has demonstrated its feasibility in different climatic conditions. The main part of the SDC system, which is responsible for the dehumidification process, is the Desiccant Wheel (DW) which has a major influence on overall system performance. Therefore, it is essential to identify the optimal operation of the DW. The operational parameters which most affect DW performance are DW rotational speed, regeneration temperature, and airflow rates. The optimal ranges of these parameters are described by previous studies under certain environmental and operational conditions. At the time of writing, there has been no study that combined the parameters into non-dimensional form. Also, there are very few studies that have considered channel geometry and design parameters, and no studies that determined the optimal non-dimensional group’s that most influence DW performance. The main goal of this study is to extend previous findings to situations not restricted to a specific design or climate. In addition, key gaps in the optimisation process for a specific design or environment will be identified and examined experimentally and computationally. The study commenced with modelling the DW. The 1-D Gas Solid Side Resistance (GSSR) model was used to model the DW. Afterwards, the model was validated with three different sets of previously published experimental data. It was found that the maximum discrepancy between the two results was 12.3%, while the maximum reported discrepancy in the open literature was 15%. An experimental apparatus was designed and built to study the DW. The DW’s parameters were divided into three categories: the process air and climate parameters, regeneration air parameters, and the DW design and operational parameters. The desiccant material parameters were assumed to be constant. The non-dimensional groups were created and tested experimentally and computationally (due to the difficulty of changing some parameters experimentally). Varying different parameters’ values in the same non-dimensional parameters’ combinations and comparing the output results was the method implemented to test the non-dimensional groups. The non-dimensional groups were valid and the number of independent parameters was reduced from 16 to 9 non-dimensional groups. The analysis of the non-dimensional groups was carried out with reference to a base value. The non-dimensional groups operating range and the effect on the performance indices were identified. Then, two of the non-dimensional groups were varied at the same time to investigate how each non-dimensional group affects others in regards to the system performance. The system performances were evaluated in terms of dehumidification effectiveness (ηdeh), Dehumidification Coefficient of Performance (DCOP), and Sensible Energy Ratio (SER). A maximum value for ηdeh and DCOP is desirable, which represents superior dehumidification performances. On the other hand, a higher value for SER means a higher temperature value of the treated air and hence a higher cooling load which must be avoided. It was found that some of the non-dimensional groups have a higher impact on the system performance than others. In addition, it was found that the best value of the ηdeh, DCOP, or SER can vary depending on the values of other non-dimensional groups. The last part of this study was optimising the non-dimensional groups. The optimisation was based on comparing the combined SDC system and VCC system with a standalone VCC system. Another performance index (ηcomp) was introduced based on comparing the energy needed to operate both systems under the same conditions. By assuming the added heat to regenerate the DW comes from a solar thermal source, the performance index becomes ηcomp,solar, which was used for the optimisation. The maximum achieved ηcomp,solar in all cases was around 2.0, which means that the standalone VCC system needs one hundred percent more power than the combined SDC system with the VCC system for the same cooling load and operating conditions. It was found that some of the non-dimensional groups had optimal values, while others could have a range of values. The percentage of the thermal energy added by solar thermal source to ensure just equality between both systems in respect of energy consumption was presented. It was found that the percentage values varied between 78.7 - 101%, while the average percentage at the optimal non-dimensional groups’ values was around 88%