Fast Procedure for the Analysis of Hydrothermal Liquefaction Biocrude with Stepwise Py-GC-MS and Data Interpretation Assisted by Means of Non-negative Matrix Factorization

Abstract

The phenomena occurring during hydrothermal liquefaction (HTL) of algal biomass to form biocrude are not fully understood. It is still not clear which species are optimal for a microalgae biorefinery, as well as the influence of their composition on the biocrude oil molecular composition. Moreover, the molecular characterization itself of the HTL biocrude oils is troublesome because of the presence of a huge number of different molecular constituents. In this work, a stepwise Py-GC-MS procedure, originally developed to solve certain issues of conventional GC-MS analysis, was further improved using a non-negative matrix factorization (NNMF) assisted peak resolution. This procedure not only increased the speed of GC-MS interpretation and made it more objective, it also disclosed additional information on certain HTL biocrude constituents which are usually neglected by the manual data handling and it minimized the fraction of unidentified compounds. With this novel analysis strategy, the biocrude oils produced during a screening with 8 different microalgae strains at two different process conditions (250 and 375 °C, both for 5 min) were analyzed. Main chemical constituents produced by HTL were quantified, obtaining a satisfactory chemical description of this matrix. The results revealed that the influence of process conditions was more important than the difference in the strains applied. Moreover, even if different biocrude yields were observed, the chemical composition results were similar for the different algal strains when processed at the same temperature.