## 3 Results and Discussion  
Figure 4 presents a comparison of the thermal conductivity values of the H\({}_{2}\)O-LiBr-Al\({}_{2}\)O\({}_{3}\) nanofluid predicted using correlations shown in Table 3. The same experimental conditions used by [51] are considered (a salt mass fraction of 50% and a temperature of 22.3 \({}^{\circ}\)C), because this is the unique experimental work available in the open literature about thermal conductivity of this nanofluid. The results obtained using the correlation of [44] are not shown as these are identical to those of [45]. From the comparison, it is concluded that the correlation of [43] is the one that best fits the experimental data of [51]. Regarding the H\({}_{2}\)O-LiBr-CNT nanofluid, Figure 5 includes a comparison of the values obtained using the different correlations. In this case, the correlation of [44] is not used because it is only valid for spherical particles. Now, the only experimental data available for this nanofluid [27] are used (a salt mass fraction of 60% and a temperature of 25 \({}^{\circ}\)C). Comparison between experimental and predicted data shows that, in spite of closer data being obtained using the correlation of [46], a more similar trend is provided by the correlation of [43]. Taking into account that much larger volume fractions of  
Figure 3: Solar radiation, ambient temperature, and relative humidity. nanoparticles will be simulated in this work than those shown in Figure 5, a smaller error is expected to occur using the correlation of [43]. The correlation of [43] is presented in Equation (10) and allows predicting the ratio between the nanofluid thermal conductivity and that of the base fluid (H\({}_{2}\)O-LiBr). \[r_{k}=1+8.733\alpha_{\mathrm{d}} \tag{10}\]  
Regarding the density, the equation provided by [47] is considered adequate. It is based on the conventional mixing theory:  
\[\rho_{nf}=(1-\alpha_{\mathrm{d}})\rho_{bf}+\alpha_{\mathrm{d}}\rho_{d} \tag{11}\]  
Figure 4: Comparison between correlations to predict the nanofluid thermal conductivity using Al\({}_{2}\)O\({}_{3}\). Figure 5: Comparison between correlations to predict the nanofluid thermal conductivity using carbon nanotubes (CNTs). To predict the viscosity, the correlation of [48] was selected. This equation was broadly used in absorption-related processes and gives the ratio between the viscosity of the nanofluid and that of the base fluid. \[r_{\mu}=\frac{1}{\left(1-\alpha_{\mathrm{d}}\right)^{2.5}} \tag{12}\]  
Finally, the nanofluid specific heat is obtained using the mixture model presented by [47]:  
\[Cp_{nf}=(1-\alpha_{\mathrm{d}})Cp_{bf}+\alpha_{\mathrm{d}}Cp_{d} \tag{13}\]  
Previous selected correlations were included in the model of the membrane-based desorber to predict properties of the H\({}_{2}\)O-LiBr-nanoparticles working fluid. Simulations results, indicating the increase in the average desorption rate along the channels with respect to the case when only H\({}_{2}\)O-LiBr is used, are presented in Figure 6. Data obtained for the three different nanoparticles and volume fractions up to 5% are shown. It can be observed that the highest increment in the desorption rate is obtained using CNT and the maximum simulated concentration of nanoparticles. The maximum desorption rate increase, with respect to the case without nanoparticles, reaches 7.9%. Additionally, Al\({}_{2}\)O\({}_{3}\) provides a better performance than CuO nanoparticles. Once the best type and concentration of nanoparticles are identified, the combined system is simulated, including the solar thermal collectors connected to the desorber of the absorption chiller. In this case, only one solar collector is considered, and it is able to provide, throughout the day, the cooling power shown in Figure 7. In this analysis, six hot water mass flow rates through the channels are simulated and the number of desorber modules varies accordingly (Table 5), in order to meet the recommended flow rate through the solar collector.