A statistical data-processing methodology of Py-GC/MS data for the simulation of flash co-pyrolysis reactor experiments

作者：Cornelissen Tom; Molenberghs Geert; Jans Maarten; Yperman Jan^*; Schreurs Sonja; Carleer Robert

来源：Chemometrics and Intelligent Laboratory Systems, 2012, 110(1): 123-128.

DOI：10.1016/j.chemolab.2011.10.011

• Statistical data processing
• Pattern recognition
• Py-GC/MS
• Flash co-pyrolysis
• Willow
• Polyhydroxybutyrate
• PHB

Practically it is extremely difficult to collect observations following a fully sound statistical design, typically encompassing a high number of repetitions, of an intensive and elaborate experimental procedure such as flash pyrolysis reactor experiments. Pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) is an extremely useful analytical technique in order to simulate a high number of repetitive pyrolysis experiments in an acceptable time span. Combining Py-GC/MS experiments and statistical data processing, conclusions can be drawn on the pyrolysis behaviour of any input material, supplying crucial information on its respective behaviour during the actual flash pyrolysis experiments. %26lt;br%26gt;In this research Py-GC/MS experiments combined with a tailored statistical data processing methodology strongly indicate the occurrence of synergetic interactions during the flash co-pyrolysis of willow/polyhydroxybutyrate (PHB) blends. Such interactions are also indicated by pattern recognition and by the analysis of the condensable and noncondensable pyrolytic gases obtained from Py-GC/MS. Accordingly, the actual influence of the flash co-pyrolysis of willow and PHB, executed with a semi-continuous pyrolysis reactor, on the pyrolytic oil features is investigated by GC/MS. Based on these experiments an explanation for the observed synergy during flash co-pyrolysis of willow and PHB is proposed.

• 出版日期2012-1-15