Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Akash Narani; Phil Coffman; James Gardner; Chenlin Li; Allison E Ray; Damon S Hartley; Allison Stettler; N V S N Murthy Konda; Blake Simmons; Todd R Pray; Deepti Tanjore

doi:10.1016/j.biortech.2017.06.156

Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply

Bioresour Technol. 2017 Nov:243:676-685. doi: 10.1016/j.biortech.2017.06.156. Epub 2017 Jun 30.

Authors

Affiliations

¹ Advanced Biofuels Process Demonstration Unit (AB-PDU), Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
² Advanced Biofuels Process Demonstration Unit (AB-PDU), Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Energy and Environmental Science and Technology, Idaho National Laboratory, Idaho Falls, ID, United States.
³ Energy and Environmental Science and Technology, Idaho National Laboratory, Idaho Falls, ID, United States.
⁴ Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States.
⁵ Biofuels and Biomaterials Science and Technology, Sandia National Laboratory, Livermore, CA, United States.
⁶ Advanced Biofuels Process Demonstration Unit (AB-PDU), Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States. Electronic address: DTanjore@lbl.gov.

PMID: 28709073
DOI: 10.1016/j.biortech.2017.06.156

Abstract

Commercial-scale bio-refineries are designed to process 2000tons/day of single lignocellulosic biomass. Several geographical areas in the United States generate diverse feedstocks that, when combined, can be substantial for bio-based manufacturing. Blending multiple feedstocks is a strategy being investigated to expand bio-based manufacturing outside Corn Belt. In this study, we developed a model to predict continuous envelopes of biomass blends that are optimal for a given pretreatment condition to achieve a predetermined sugar yield or vice versa. For example, our model predicted more than 60% glucose yield can be achieved by treating an equal part blend of energy cane, corn stover, and switchgrass with alkali pretreatment at 120°C for 14.8h. By using ionic liquid to pretreat an equal part blend of the biomass feedstocks at 160°C for 2.2h, we achieved 87.6% glucose yield. Such a predictive model can potentially overcome dependence on a single feedstock.

Keywords: Feedstock blends; Least cost formulation; Lignocellulosic biomass; Predictive model; Pretreatment and enzymatic hydrolysis.

MeSH terms

Biomass*
Carbohydrates
Hydrolysis
Lignin
Zea mays*

Substances

Carbohydrates
Lignin