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

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.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.

► A full statistical data processing methodology is applied on Py–GC/MS experiments. ► Composition changes during co-pyrolysis in various ratios of willow waste and PHB. ► Synergy is proved in flash co-pyrolysis of willow waste and PHB in various ratios.