A detailed analysis on the thermal degradation of nitrate based molten salts evaluating the influence of different impurities and heating rates in their maximum working temperature is presented. Determining the maximum operating temperature is of interest when searching for new heat transfer fluids (HTF) for high temperature solar thermal applications as it limits the thermodynamic efficiency of the power block. Thermogravimetric analysis is performed on potassium nitrate, sodium nitrate, sodium nitrite, the binary system Solar Salt, and the ternary Hitec. The kinetics of the thermal decomposition reactions are investigated through iso-conversional analysis. The effect of adding some common impurities such us NaCl and Na2CO3 on the multi-component nitrate salts is evaluated. It was found that impurities such as Na2CO3 can enhance the thermal stability of Hitec salt, leading to higher thermal decomposition temperatures. For solar salt, impurities such as NaCl can enhance the thermal stability of solar salt at 10K·min-1, while adding Na2CO3 can have the opposite effect. For nitrate based molten salts used in TES and HTF systems in CSP a reduction on the purity required for the materials can present some operational advantages besides cost reduction

Wildland and wildland/urban interface fires are a serious problem in many areas of the world. It is expected that with global warming the wildfire and wildland/urban interface fire problem will only intensify. The ignition of natural combustible material by hot metal particles or embers is an important fire ignition pathway by which wildland and urban spot fires are started. There are numerous cases reported of wild fires started by hot metal particles from clashing power lines, or from sparks generated by machines or engines. Similarly there are many cases reported of industrial fires caused by grinding and welding sparks. Despite the importance of the subject, the topic remains relatively unstudied. The senior author of this article and his collaborators have been working for the past few years on this problem. In this article, we provide a comprehensive summary of that work to date. The work includes experimental and theoretical modeling of the ability of hot metal particles and embers to cause the ignition of cellulosic fuel beds. The metal particles studied are representative of clashing conductors (aluminum and copper) and those produced by machine friction and hot work such as welding (stainless steel and brass). In addition glowing and flaming wood embers are considered, as they represent an important source of fire spotting in wildfires. The overall results show a hyperbolic relationship between particle size and temperature, with the larger particles requiring lower temperature to ignite the fuel bed than the smaller particles. An important finding is that although particle energy is important in the capability of the particle to ignite the fuel, both energy and temperature are determining factors of the particle ignition capabilities. The thermal properties of the metal play a lesser role with the exception of the energy of melting if it occurs. It also appears that the controlling ignition mechanisms by large particles are different than those from the small particles. The former appear to be determined primarily by the particle surface temperature while the latter by the particle energy and surface temperature. Sparks are a specific type of particles with very small sizes and very high temperatures. Because of the small sizes, their energy is small and it is postulated that the sparks must accumulate for ignition of a fuel bed to occur. The results with embers indicate that the smoldering is the easier form of ignition, although flaming ignition can occur if the ember is flaming and the air velocities are moderate. To provide further information about the fire spot ignition process, both analytical and numerical modeling are used and compared with the experimental results. Although the models provide qualitative predictions further development is necessary to reach quantitative predictive capabilities.