Welcome to the Advanced Biofuels and Bioproducts Process Demonstration Unit. Part of the Lawrence Berkeley National Labs, we were established by the United States Department of Energy to help ramp up the bioeconomy.
Two case studies exploring conversion technologies for municipal solid waste. One is a partnership INL, and the other is a collaboration with FATER.read more
What We Offer
Meet Our Team
BETO is providing up to $40,000 per selected DOE National Laboratory proposal that focuses on one or more critical bioenergy small business challenge(s) by leveraging unique laboratory assets. Proposal submission deadline is July 31, 2016.
Bio-Malonic acid fermentation process to be validated at ABPDU and scaled at NREL
Feedstock conversions yields approach theoretical maximum
July 12-14, 2016
July 24-26, 2016
July 26-27, 2016
This paper presents two case studies on the scale-up and process integration of municipal solid waste conversion technology. In a partnership with Idaho National Labs, we successfully demonstrated 200-fold scale up of MSW blends IL acidolysis. We also developed an integrated process for ionic liquid based deconstruction technologies for MSW blends conversion. The scale up attempt will leverage the opportunity towards a cost-effective MSW blends conversion technology.
Under a DOE Work-For-Others agreement with FATER, ABPDU has been developing and validating an integrated waste-to-energy process. Key outcomes indicate that post-consumer absorbent hygiene products (AHPs) can be readily and economically converted — without using harsh or expensive pretreatment routes — to sugars and fuel intermediates.
Dry matter loss (DML) occurs in high-moisture storage conditions; it remains unclear how storage conditions and degradation impact sugar release and fermentation inhibitor production during conversion. In collaboration with Idaho National Labs, two feedstocks, switchgrass and corn stover, were compared using compositional analysis, alkaline pretreatment, and enzymatic saccharification. Under the tested conditions, switchgrass with 10% and 20% DML and corn stover with 30% DML achieved higher sugar yields compared to samples before storage.
In a collaborative effort with INL, SNL, and JBEI, predictive modeling was used to evaluate and optimize traditional pretreatment methods for biomass mixture compositions to maximize sugar yield and minimize furfural production. The collaboration encompassed compositional analysis of feedstocks, solids loading during pretreatment for mixed feedstock, enzymatic hydrolysis on unwashed solids, and sugar and furfural analysis. Predictive modeling could effectively identify the pretreatment catalyst and treatment conditions for an “optimal” biomass mixture and the optimal biomass mixture for a particular pretreatment system.