Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

Leow, Shijie; Witter, John R.; Vardon, Derek R.; Sharma, Brajendra K.; Guest, Jeremy S.; Strathmann, Timothy J.

doi:10.1039/C5GC00574D

Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

Journal Article · Mon May 11 04:00:00 EDT 2015 · Green Chemistry

DOI:https://doi.org/10.1039/C5GC00574D· OSTI ID:1242266

Leow, Shijie ^[1]; Witter, John R. ^[1]; Vardon, Derek R. ^[2]; Sharma, Brajendra K. ^[3]; Guest, Jeremy S. ^[1]; Strathmann, Timothy J. ^[1]

Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
Univ. of Illinois at Urbana-Champaign, Champaign, IL (United States)

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and other conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model), providing for the first time an integrated modeling framework to overcome a critical barrier to microalgae-derived HTL biofuels and enable predictive analysis of the overall microalgal-to-biofuel process.

View Accepted Manuscript (DOE)

Research Organization:: NREL (National Renewable Energy Laboratory (NREL), Golden, CO (United States))

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1242266

Report Number(s):: NREL/JA--5100-64673

Journal Information:: Green Chemistry, Journal Name: Green Chemistry Journal Issue: 6 Vol. 17; ISSN GRCHFJ; ISSN 1463-9262

Publisher:: Royal Society of ChemistryCopyright Statement

Country of Publication:: United States

Language:: English

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