Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors.
The genome-scale metabolic models for E. coli[ 52], S. cerevisiae[ 53], and S. oneidensis[ 44] are used to enumerate over a comprehensive set of feedstock medium compositions and single and double gene deletions.
Despite the complexities associated with the rational engineering of central metabolism to improve EFE substrate availability (i.e., central metabolism is highly interconnected) the necessary substrates for production of bioethylene using EFE, namely AKG and arginine, can, depending on the host organism, originate from a diverse set of renewable feedstocks ranging from biomass to CO2 and sunlight.
To achieve this, a wide range of production temperatures (200 650 °C) and an extensive set of diverse feedstock (n = 12) were used to calculate the sensitivity.
A set of 232 herbaceous feedstock samples, from the assembled population of 279, consisting of the six different herbaceous feedstock species was selected as the calibration set for composition.
We first present pretreatment and enzymatic hydrolysis experiments on a set of representative biomass feedstock samples (corn stover, poplar, sorghum, switchgrass) in order to put the assay in context, and then show the results of the assay applied to approximately 150 different feedstock samples covering 5 different materials.
A set of 164 to 167 feedstock samples, depending on the constituent modeled, consisting of six to seven different herbaceous and two woody feedstocks were selected as the calibration set for the carbohydrate release and yield models.
The reference data set was composed of the 'unique' plants established from the PCA metrics, and a random selection of feedstocks representative of the majority of the samples.
Over time, a variety of feedstocks are likely to emerge, depending on local conditions as well as government policy.
Investigating a range of feedstocks (forest residues, corn stover, etc).
