Optimizing waste-to-energy conversion: Unveiling the potential of unsupervised clustering through the new HOM classification system

Abstract

The correct and informative classification of a given fuel is essential in optimizing the utilization of the fuel. While the physical properties of the fuels provide more complete information about the fuel, conventional fuel classification systems (CSs) are based on the fuels' source or origin. In this study, the novel HOM CS is proposed from the data of fuels' ultimate and proximate analysis and HHVs by utilizing k-means clustering where the number of clusters are optimized. The obtained clusters are investigated in comparison with the conventional CSs by using several methods such as contingency matrices, PCAs, ternary plots, and Van-Krevelen diagrams. Finally, the clusters are appropriately named and the HOM CS is distinctly defined. The proposed CS can be used in informative class assignment of any kind of fuel including fuel mixtures or fuels of unknown source or origin which are generally categorized vaguely as composite-streams, wastes, or others in conventional CSs. Thus, by the implementation of the new CS, these materials can now be categorized in a manner that provides insight into their specific application possibilities. This categorization can assist researchers in determining the most efficient conversion methods especially for the waste-to-energy processes including combustion, gasification, and biogas plants.