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The specific heat capacity propery is a critical thermal property because it is directly related to heat storage and transfer. The need for better and novel thermal energy storage materials have led to the discovery of high temperature nanofluids with molten salts (nanosalts). The utilization of novel machine learning tools in accurately estimating the specific heat capacity of nanofluids, would further pave the way for more innovative studies on nanosalt, hence the prediction of specific heat capacity of hybrid nanofluids constitute a significant research area. Despite the few researches conducted in the area of prediction of these fluids, priority with regards to the robust analysis of input variables for different machine learning algorithms have not been given due consideration. Therefore, this present study aims to develop and validate a robust package of machine learning algorithms (twenty machine learning algorithms) in estimating the specific heat capacity of hybrid nanofluids, across different feature spaces. The different algorithm hyperparameters were adjusted to give optimum prediction. A total of 600 data points were retrieved and split into training, testing, and validation data set, for which the validation dataset was not used in the training and testing process. The input variables used in the training of the machine learning models are temperature, volume fraction, the specific heat capacity of the base fluid, the specific heat capacity of individual nanoparticles, and the mixture ratio of individual nanoparticles. The Model A, which constituted the seven (7) input variables gave the best prediction accuracy with validation score of 0.999703, while the Model C, which contained the feature variables of temperature, volume fraction and specific heat capacity of the base fluid gave the least prediction accuracy. The optimal machine algorithms retrieved for the Model A, B, C and D was the Ridge, Gradient Boost regressor, Passive Aggressive regressor, and Kernel ridge regressor. The combination of ensemble tree regressors and online learning algorithms can be coded and used in future prediction analysis of SHC, as they have both shown accurate prediction quality.

The optimization of heat transfer fluids as cooling agents in solar technologies have gained significant attention, especially with the groundbreaking achievements in nanofluid synthesis and practical utilizations. The synthesis of hybrid nanofluids has shown improved thermal performance in their application as heat transfer fluids in parabolic trough systems. However, the lack of clear understanding regarding the behavior of ternary hybrid nanofluids, (especially with their unique particle type behaviors) hinders further improvement in these systems. In this study, the performance of an integrated solar parabolic trough system is investigated using eight water based-ternary nanofluids with different operational conditions. This study is also carried out to estimate the potential of designing a smaller solar collector that can produce optimal performance by using ternary nanofluids as an alternative to water. From this study, it was estimated that the maximum exergy efficiency of the ternary nanofluid 1 (ZnO-Al2O3-SiO2), 2 (Fe-Cu-Ag), 3 (Fe-Cu-ZnO), 4 (Fe-Cu-MWCNT), 5 (Ag-Al2O3-TiO2), 7 (Fe-MWCNT-TiO2), and 8 (Fe-Graphene-TiO2) gave values of 69.07%, 66.23%, 66.74%, 66.35%, 68.04%, 69.08%, 67.74% and 68.32% respectively. The MWCNT - Al2O3– TiO2(ternary fluid 6), ZnO – Al2O3– SiO2(ternary fluid 1), Fe - Graphene – TiO2(ternary fluid 8), and Ag – Al2O3– TiO2(ternary fluid 5) gave the highest useful heat measured as 15,859 kW, 15,858 kW, 15,679 kW, and 15,614 kW respectively. As compared to water, the solar collector's area can be reduced up to 30.45%, 27.66%, 28.17%, 27.76%, 29.46%, 30.48%, 29.18%, and 29.74% for the ternary fluid 1, ternary fluid 2, ternary fluid 3, ternary fluid 4, ternary fluid 5, ternary fluid 6, ternary fluid 7, and ternary fluid 8 respectively. The maximum CO2emission of 3826 kg avoided was recorded for the ternary fluid 6. The ternary fluid 6 (MWCNT-Al2O3-TiO2) also showed the optimum energy efficiency, exergy efficiency and heat storage capacity.

Liquid air energy storage (LAES) is a grid-scale energy storage technology that utilizes an air liquefaction process to store energy with the potential to solve the limitations of pumped-hydro and compressed air storage. A challenge it faces itself is its high compression work during the charging process. Thus, attempts to increase the performance of the system need to include reducing the compression work. This study investigates the possible performance improvement of a LAES system through inlet air dehumidification and cooling. A simulation is performed on a 40 MW/320 MWheLAES system which uses a desiccant wheel dehumidifier and a cooling coil for inlet air conditioning. The results show that the system can save 9521 MWhein compression work annually which will translate to $761,680 annual cost savings in charging the system. The maximum performance improvement achieved during the summer period when the ambient conditions are unfavorable is 11.7%. Financial analysis also shows that the simple payback period of the desiccant wheel dehumidifier modification is 11.5 years and the levelized cost of storage (LCOS) of the LAES system is $237/MWh which is competitive compared to other storage systems.

•Three mixture ratios of 1:1:1, 1:2:1, and 1:1:2 were synthesized at volume fraction of 0.5%, 0.75%, 1% and 1.25%.•This study looked into what the application of temperature, working with specific concentrations of volume as well as having a mixture of ratio can do to nano-fluids.•Zeta potential test and particle size analyzer were used in examining the dispersal stability of the nanofluids and nanoparticle size.•The experimental result showed a `peaking effect` in the specific heat capacity, which was measured at a mixture ratio of 1:1:1.•GA-SVR gives the most accurate SHC prediction