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Assessing pretreatment reactor scaling through empirical analysis [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Energy Efficiency and Renewable Energy, 2016.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
13 pages : digital, PDF file
Additional Creators
National Renewable Energy Laboratory (U.S.), United States. Department of Energy. Office of Energy Efficiency and Renewable Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Pretreatment is a critical step in the biochemical conversion of lignocellulosic biomass to fuels and chemicals. Due to the complexity of the physicochemical transformations involved, predictively scaling up technology from bench- to pilot-scale is difficult. This study examines how pretreatment effectiveness under nominally similar reaction conditions is influenced by pretreatment reactor design and scale using four different pretreatment reaction systems ranging from a 3 g batch reactor to a 10 dry-ton/d continuous reactor. The reactor systems examined were an Automated Solvent Extractor (ASE), Steam Explosion Reactor (SER), ZipperClave(R) reactor (ZCR), and Large Continuous Horizontal-Screw Reactor (LHR). To our knowledge, this is the first such study performed on pretreatment reactors across a range of reaction conditions (time and temperature) and at different reactor scales. The comparative pretreatment performance results obtained for each reactor system were used to develop response surface models for total xylose yield after pretreatment and total sugar yield after pretreatment followed by enzymatic hydrolysis. Near- and very-near-optimal regions were defined as the set of conditions that the model identified as producing yields within one and two standard deviations of the optimum yield. Optimal conditions identified in the smallest-scale system (the ASE) were within the near-optimal region of the largest scale reactor system evaluated. A reaction severity factor modeling approach was shown to inadequately describe the optimal conditions in the ASE, incorrectly identifying a large set of sub-optimal conditions (as defined by the RSM) as optimal. The maximum total sugar yields for the ASE and LHR were 95%, while 89% was the optimum observed in the ZipperClave. The optimum condition identified using the automated and less costly to operate ASE system was within the very-near-optimal space for the total xylose yield of both the ZCR and the LHR, and was within the near-optimal space for total sugar yield for the LHR. This indicates that the ASE is a good tool for cost effectively finding near-optimal conditions for operating pilot-scale systems, which may be used as starting points for further optimization. Additionally, using a severity-factor approach to optimization was found to be inadequate compared to a multivariate optimization method. As a result, the ASE and the LHR were able to enable significantly higher total sugar yields after enzymatic hydrolysis relative to the ZCR, despite having similar optimal conditions and total xylose yields. This underscores the importance of incorporating mechanical disruption into pretreatment reactor designs to achieve high enzymatic digestibilities.
Report Numbers
E 1.99:nrel/ja--5100-66693
nrel/ja--5100-66693
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Note
Published through SciTech Connect.
10/10/2016.
"nrel/ja--5100-66693"
Biotechnology for Biofuels 9 1 ISSN 1754-6834 AM
James J. Lischeske; Nathan C. Crawford; Erik Kuhn; Nicholas J. Nagle; Daniel J. Schell; Melvin P. Tucker; James D. McMillan; Edward J. Wolfrum.
Funding Information
AC36-08GO28308
View MARC record | catkey: 23501937