Thermal energy storage (TES) is extremely important in concentrated solar power (CSP) plants since it represents the main difference and advantage of CSP plants with respect to other renewable energy sources such as wind, photovoltaic, etc. CSP represents a low-carbon emission renewable source of energy, and TES allows CSP plants to have energy availability and dispatchability using available industrial technologies. Molten salts are used in CSP plants as a TES material because of their high operational temperature and stability of up to 500°C. Their main drawbacks are their relative poor thermal properties and energy storage density. A simple cost-effective way to improve thermal properties of fluids is to dope them with nanoparticles, thus obtaining the so-called salt-based nanofluids. In this work, solar salt used in CSP plants (60% NaNO₃ + 40% KNO₃) was doped with silica nanoparticles at different solid mass concentrations (from 0.5% to 2%). Specific heat was measured by means of differential scanning calorimetry (DSC). A maximum increase of 25.03% was found at an optimal concentration of 1 wt.% of nanoparticles. The size distribution of nanoparticle clusters present in the salt at each concentration was evaluated by means of scanning electron microscopy (SEM) and image processing, as well as by means of dynamic light scattering (DLS). The cluster size and the specific surface available depended on the solid content, and a relationship between the specific heat increment and the available particle surface area was obtained. It was proved that the mechanism involved in the specific heat increment is based on a surface phenomenon. Stability of samples was tested for several thermal cycles and thermogravimetric analysis at high temperature was carried out, the samples being stable. PACS 65.: Thermal properties of condensed matter; 65.20.-w: Thermal properties of liquids; 65.20.Jk: Studies of thermodynamic properties of specific liquids

Diesel engines are fuel efficient which benefits the reduction of CO₂ released to the atmosphere compared with gasoline engines, but still result in negative environmental impact related to their emissions. As new degrees of freedom are created, due to advances in technology, the complicated processes of emission formation are difficult to assess. This paper studies the feasibility of using artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to optimize the diesel engine settings. The objective of the optimization was to find settings that complied with the increasingly stringent emission regulations while also maintaining, or even reducing the fuel consumption. A large database of stationary engine tests, covering a wide range of experimental conditions was used for this analysis. The ANNs were used as a simulation tool, receiving as inputs the engine operating parameters, and producing as outputs the resulting emission levels and fuel consumption. The ANN outputs were then used to evaluate the objective function of the optimization process, which was performed with a GA approach. The combination of ANN and GA for the optimization of two different engine operating conditions was analyzed and important reductions in emissions and fuel consumption were reached, while also keeping the computational times low