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In this study, the physical and chemical characteristics of Bambusa vulgaris ex J.C. Wendl. var.vulgaris (Bambusa vulgaris) aged 1, 2 and 3 years were evaluated. The objective was to train, validate and evaluate the efficiency of artificial neural networks (ANNs) as predictive tools to estimate bamboo stem energy density grown commercially in northeastern Brazil. For that, samples were collected in a commercial plantation and managed for energy production, determining the energy properties. Among all the characteristics analyzed, basic apparent density was the one with major correlation with bamboo stem energy density. This factor has a great advantage because it is easy to estimate, determined both by dry mass at 0% moisture, and at saturated mass. Also, the precision of ANNs was verified when associated with basic density, as a predictor of bamboo stem energy density, showing low standard error (Syx%, 1.52) and high coefficient of determination (R2 = 0.98). ANN-estimated values had no statistical difference (tcal 0.58 ≤ ttab 2.08) with energy density estimated in the laboratory. Therefore, this tool was efficient, being recommended to predict the energetic density of the species under study, with basic density as the only predictive variable.

Artificial neural networks in predicting energy density of Bambusa vulgaris in Brazil