Abstract
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.
Highlights
Carbon dioxide concentration in the atmosphere has been on the rise in the past few centuries as a result of the excessive use of fossil fuels
Algae is cultivated in an open pond, which we model as a continuous stirred-tank reactor (CSTR) [35]
Reaction rates of species involved in mass balance of open pond. This optimization problem can be formulated as a disjunctive mixed-integer nonlinear programming (MINLP), where disjunctions are associated with the critical regions (CRs) of the metabolic network
Summary
Carbon dioxide concentration in the atmosphere has been on the rise in the past few centuries as a result of the excessive use of fossil fuels. Achieving selling prices that are comparable to those of petroleum-derived fuels is a major challenge facing large-scale production of algal biofuels This has motivated several superstructure optimization studies to maximize the profit and improve the efficiency of biorefineries. Gebreslassie et al [7] considered a superstructure of a CSN with five processing sections (cultivation, carbon capture, harvesting and dewatering, lipid extraction, upgrading, and remnant treatment), maximizing NPV and minimizing global-warming potential (GWP) to identify optimal processing pathways. Of note is the LCA of O’Connell et al [28], where it was shown that the harvesting and dewatering section alone accounts for more than 50% of the total emissions in algal biorefineries Their results indicate that lipid production from algae yields higher GWP than from several terrestrial plants due to their high water contents. We propose an optimization strategy to handle hourly fluctuations in the growth rate during the day due to light-intensity changes, which can appreciably affect the optimization results
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