Use of Machine Learning Tools to Assess Surface Dryout During Nucleate and Transition Boiling on Surfaces With Different Wetting and Substrate Properties

The work reported in this paper explored the use of machine learning tools to analyze quenching pool boiling data in the nucleate boiling range, near maximum heat flux range, and through the transition boiling range towards the Leidenfrost (minimum heat flux) point. It specifically explores the hypothesis that this sequence is a consequence of progressive dryout of the surface as the wall superheat increases. Machine learning tools are used with a heuristic model of the dryout parametric dependence to extract information about the magnitude of surface dryout as the superheat increases. From experimental data, the machine learning analysis provides an indication of how the dryout transition differs for different surface wetting characteristics and substrate materials. The wetting variations considered ranged from moderately wetted plain aluminum and copper surfaces to highly wetted nanostructured superhydrophilic surfaces. The data examined included aluminum and copper substrates. The results of the machine learning analysis indicate that the properties of the surface substrate can have a significant effect on the progressive surface dryout. In contrast, the surface wetting characteristics had a more limited effect for the surfaces tested. The paper concludes with an assessment of the implications of the findings for developing enhanced surfaces for boiling heat transfer performance.

Isomerization of n-C5/C6 Bioparaffins to Gasoline Components with High Octane Number

The thermal and catalytic conversion processes of alternative feedstocks (e.g., waste and biomass) to different engine fuels can result in the formation of a significant amount of light hydrocarbons as by-products in the boiling range of gasoline. The properties of these C5/C6 hydrocarbons need to be improved due to many reasons, e.g., their benzene content, and/or poor oxidation stability (high olefin content) and low octane number (<60). The aim of the research work was to increase the octane number of benzene containing C5/C6 bioparaffin fractions by catalytic isomerization. These by-products were obtained from special hydrocracking of waste cooking oil to hydrocarbons in the boiling range of aviation turbine fuels (JET fuels)/diesel fuels. Experiments were carried out in a reactor system containing down-flow tubular reactors over Pt/Al2O3/Cl and Pt/H-Mordenite/Al2O3 catalysts at 115–145 °C and 230–270 °C, respectively. Based on the results obtained at different process parameter combinations, it was concluded that the hydrogenation of benzene was complete over both catalysts, and the liquid yields were higher (ca. 98% > ca. 93 %) in the case of Pt/Al2O3/Cl. In addition, the octane number was also enhanced (ca. 32 > ca. 27 unit) in the products compared to the feedstock. This was because a higher isoparaffin content can be obtained at a lower operating temperature. Moreover, cracking side reactions take place to a lesser extent. The utilization of these isomerized bio-origin light fractions can contribute to the competitiveness of second-generation biofuels.