Application of Multivariate Statistical Analysis for Pyrolysis Process Optimization

Abstract

The identification of the most efficient biomass valorization paths is vital for reaching the target of Renewable Energy Sources consumption by 2030. In this context, within a National project named ‘Biofeedstock’, the applicability of multivariate statistical analysis, i.e. Canonical Correlation Analysis (CCA), is implemented for the definition of specific correlations describing quantitatively and qualitatively the fast pyrolysis process outputs. The database used for the CCA contains 59 observations and it has been built up using literature data specifically on fluidized bed fast pyrolysis without any catalyst, in the temperature range of 450-550°C. The results show that the CCA correctly describes the process analysed with a discrete degree of confidence. However, it shows two main drawbacks, firstly the dataset constitution, and secondly possibility to individuate only linear correlations between inputs and outputs.