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Combating global warming-related climate change demands prompt actions to reduce greenhouse gas emissions, particularly carbon dioxide. Biomass-based biofuels represent a promising alternative fossil energy source. To convert biomass into energy, numerous conversion processes are performed at high pressure and temperature conditions, and the design and dimensioning of such processes requires thermophysical property data, particularly thermal conductivity, which are not always available in the literature. In this paper, we proposed the application of Chemoinformatics methodologies to investigate the prediction of thermal conductivity for hydrocarbons and oxygenated compounds. A compilation of experimental data followed by a careful data curation were performed to establish a database. The support vector machine algorithm has been applied to the database leading to models with good predictive abilities. The support vector regression (SVR) model has then been applied to an external set of compounds, i.e. not considered during the training of models. It showed that our SVR model can be used for the prediction of thermal conductivity values for temperatures and/or compounds that are not covered experimentally in the literature. [ABSTRACT FROM AUTHOR]

This paper aims to solve the state's hydropower dependence, build energy security and reduce greenhouse gas emissions through a hybrid renewable energy system and its implementation in Willingdon Island. Hybrid Optimisation of Multiple Energy Resources (HOMER) is used to find the optimal solution for two grid-connected and off-grid scenarios. Of the 19 solutions, an optimal off-grid, an optimal grid-connected, a 100% variable renewable energy, and a diversified energy portfolio option are selected, analyzed, and ranked based on their techno-economic and emissions characteristics. The results suggest the combination of PV/Wind/Grid with a cost of energy of $0.044 as an optimal solution for the Island. The diversified energy solution takes the second position, having 51.4% and 38.8% solar and wind, respectively. [ABSTRACT FROM AUTHOR]