Clasificación de biomasa por análisis de clúster basado en datos de potencial biológico de metano (2020)
Currently 80% of the global energy requirement is supplied by energy obtained from fossil fuels, whose carbon footprint contributes significantly to climate change, creating unsustainable in the long term. On the other hand, the amount of waste left by agro-industrial activity is now an environmental problem, as a significant percentage of these waste ends up in landfills emitting large amounts of CO2, acidic gases and odors. This work implements cluster analysis tools to methane biochemical potential (BMP) databases, NREL waste composition, and ultimate analysis using the tools available in Python, this in order to achieve waste clusters that share different characteristics, and identify compositional correlations with BMP. Database construction is a useful and necessary tool for identifying types of waste that have a high yield of biochemical methane potential. By applying on the different elaborate databases, the Python K-media algorithm was obtained six groups with respect to organic composition, where those that presented a high content of pectin, cellulose or removable achieved a higher performance of methane, the above in experimental BMP results, as well as in theoretical BMP. While from the elementary composition, no significant correlations were found between it and the potential of experimental methane. It should be noted that to improve the results it is necessary to expand the database in future work, as well as implement new data processing algorithms.