(495c) Understanding Fast Pyrolysis of Microalgae Model Components Via Py–FTIR and Py–GCMS

Microalgae, fast growing aquatic plants, are considered as prospective source of renewable energy to reduce the dependency on fossil fuels. No competition with food crops is foreseen as they grow in water unlike lignocellulosic biomass. Thermochemical conversion of microalgal biomass is a potential technique to convert them into valuable fine chemicals and fuel molecules. Fast pyrolysis of microalgae proves to be a promising conversion technique to produce bio-oil. Owing to the complexity in structure of microalgae, a wide variety of compounds containing oxygen and nitrogen such as aldehydes, ketones, carboxylic acids, nitriles, amides, pyrroles and indoles are present in the bio-oil from microalgae. Microalgae consist of three key components namely proteins, lipids and carbohydrates. Tweaking the culture conditions can significantly change their composition. There is limited literature available on the interaction among the constituents under fast pyrolysis conditions. The effect of interactions between proteins-lipids, proteins-carbohydrates and lipids-starch on the formation of various products should be investigated in detail for a better understanding and implementation of large scale bio-oil production from fast pyrolysis of microalgae.

In this study, bovine serum albumin (BSA), sunflower oil (SO) and potato starch (PS) were chosen as microalgae components representing proteins, lipids and carbohydrates, respectively. Prior to fast pyrolysis experiments, BSA, SO and PS were characterized to determine their thermal stability, elemental composition, high heating value and functional groups. Fast pyrolysis of individual components and mixtures of BSA, SO and PS emulating microalgae behavior were investigated using a single shot micropyrolyzer (Frontier Laboratories) and CDS Pyroprobe® reactor at 500 °C. Pyrolysates evolved were analyzed using a Shimadzu QP 2010 plus gas chromatograph–mass spectrometer (GCMS) for the organic composition and Agilent Cary 660 Fourier transform infrared (FTIR) spectrometer for the time evolution of major functional groups. The effect of BSA-to-SO, BSA-to-PS and SO-to-PS ratio (1:0, 2:1, 1:1, 1:2, 0:1 wt./wt.), and BSA-to-SO-to-PS (1:1:1 wt./wt./wt.) on the pyrolysate composition and temporal evolution of C-H (methyl as well as aromatic), C=O, C-C, N-H and carbondioxide functional groups were evaluated. The presence of interactions among the various model components was assessed by comparing the actual selectivity of a functional group with predicted selectivity, determined using the additivity rule. Significant positive (increase in production) and negative (decrease in production) interactions were observed in the selectivity of various functional groups. Interaction of SO with BSA and PS resulted in enhanced selectivity towards long chain carbon compounds (>C15) owing to repolymerization reactions. For co-pyrolysis of BSA with SO, high selectivity (30–40%) was observed for esters as a result of possible esterification reactions between carboxylic acids and alcohol intermediates. Due to the interactions during co-pyrolysis of BSA and PS, selectivity of carboxylic acids (10–25%) increased. Interactions between BSA and PS also decreased the selectivity towards aromatic compounds by increasing the selectivity towards N- containing cyclic compounds. Mixtures of SO and PS is shown to inhibit decarboxylation reactions resulting in very high selectivity of carboxylic acids (45–48%). Selectivity of hydrocarbons was highest (30%) when three component mixture was pyrolyzed. The presence of SO and PS altered the pyrolysis mechanism of BSA as they inhibited the formation of aromatic hydrocarbons and N-containing cyclic compounds. The maximum vapor evolution time for BSA, SO and PS varied between 9–50 s. Importantly, the evolution of all the functional groups started simultaneously as soon as pyrolysis started. BSA and PS accelerated the pyrolysis of SO as maximum vapor evolution time was brought down from 50s to below 10 s for the major functional groups. This study provides a better understanding of the interactions between model compounds representing the algae components. This can in turn be useful in tailoring the composition of microalgae species to improve the yield of specific organics from algae. More interesting results will be addressed during the presentation.