Abstract
The biggest challenge facing in sugar-energy plants is to move towards the biorefinery concept, without threatening the environment and health. Energy cane is the state-of-the-art of smart energy crops to provide suitable whole-raw material to produce upgraded biofuels, dehydrated alcohol for transportation, refined sugar, yeast-fermented alcoholic beverages, soft drinks, silage and high-quality fodder, as well as to cogenerate heat and bioelectricity from burnt lignocellulose. We, accordingly, present fuzzy c-means (FCM) clustering algorithm interconnected with principal component analysis (PCA) as powerful exploratory data analysis tool to wisely classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity. From the orthogonally-rotated factorial map, fuzzy cluster I aggregated the hybrids VX12-0277, VX12-1191, VX12-1356 and VX12-1658 composed of higher contents of soluble solids and sucrose, and larger productive yields of fermentable sugars. These parameters correlated with the X-axis component referring to technological quality of cane juice. Fuzzy cluster III aggregated the hybrids VX12-0180 and VX12-1022 consisted of higher fiber content. This parameter correlated with the Y-axis component referring to physicochemical quality of lignocellulose. From the PCA-FCM methodology, the conclusion is, therefore, hybrids from fuzzy cluster I prove to be type I energy cane (higher sucrose to fiber ratio) and could serve as energy supply pathways to produce bioethanol, while the hybrids from fuzzy cluster III are type II energy cane (lower sucrose to fiber ratio), denoting potential as higher fiber yield biomass sources to feed cogeneration of heat and bioelectricity in high temperature and pressure furnace-boiler system.
Highlights
Brazil ranking first in the sugarcane production worldwide, followed by India, and the People’s Republic of China
We collected and assessed cane juice and lignocellulose samples for total soluble solids, sucrose, purity, reducing sugars, productive yield of fermentable sugars, fiber and productivity of dry biomass, as the methodologies detailed by Ahmed et al (2010) and Viana et al (2017)
The Euclidian metric measured how similar or dissimilar were the hybrids of energy cane, classified through fuzzy c-means (FCM) clustering algorithm and defuzzified as alternative clean energy sources for production of first-generation ethanol and/ or cogeneration of heat and bioelectricity (R Core Team, 2017)
Summary
Brazil ranking first in the sugarcane production worldwide, followed by India, and the People’s Republic of China. Exploratory data analysis (EDA) tools such as principal component analysis and fuzzy c-means clustering algorithm could accurately classify genotypes of energy cane expected to supply sugar, ethanol and electricity markets into the years. The scientific study by Gouy et al (2015) on the effects of S. spontaneum L. introgression level on the sugarcane crop yield performance and its impact on the productivity in radiation and thermal conditions proved cluster analysis can robustly discriminate energy cane from sugarcane. Present FCM-PCA approach as powerful exploratory data analysis tool to accurately classify hybrids of energy cane for production of first-generation ethanol and cogeneration of heat and bioelectricity
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