Integrating Genome-Scale and Superstructure Optimization Models in Techno-Economic Studies of Biorefineries
Name
processes-07-00286.pdf
Description
Published version
Size
991.89 KB
Format
Adobe PDF
Checksum (MD5)
3a68e5880d330785f0ec2dc0fc02d5e9
Author(s)
Akbari, Amir
Barton, Paul I
Date Issued
2019
Journal
Processes
Publisher
MDPI AG
Version
Final published version
Abstract
© 2019 by the authors. Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity.
MIT Department
Massachusetts Institute of Technology. Process Systems Engineering Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/pr7050286
