(343f) Superstructure Optimization of Bio-Refineries Using Metabolic-Network Models

Major environmental concerns such as global warming and climate change are attributed to increased atmospheric carbon dioxide due to excessive use of fossil fuels in the past century. Thus, attention has shifted to clean energy resources and renewable fuels. Biomass-derived fuels have proven effective in reducing greenhouse gas emissions [1]. Algal biofuels are among the most promising renewable energy resources due to unique properties of algal species. The resilience of algae to grow in harsh environments is highly desirable. Moreover, algae can grow in wastewater or seawater, reducing the dependence on freshwater recourses. Green algae are highly suitable for second-generation biofuels because their cultivation does not rely on terrestrial and food feedstocks [2]. Since algae are CO₂-consuming photosynthetic organisms, algal biorefineries are usually designed and constructed within carbon sequestration networks (CSNs). CSNs comprise several processing sections, including carbon capture, cultivation, harvesting, extraction, upgrading, and remnant treatment. Technologies used in the cultivation section mostly control the performance of CSNs. However, current cultivation technologies still do not have desirable productivities. Nevertheless, previous studies on the optimization of carbon-sequestration plants showed that a competitive price-per-gallon for biofuel can be achieved if environmental considerations such as global warming and CO2-mitigation are accounted for [3]. However, optimization models were based on approximate empirical correlations for the cultivation section.

Recent advances in genomics have led to the development of metabolic-network-reconstruction techniques, which have proven effective in studies of microbial communities. Using these techniques, quantitative models have been constructed to describe the behaviour of algal and microbial communities under various environmental conditions, such as light intensity and substrate concentrations [4]. In this study, we undertake a superstructure optimization of an algal CSN, including five major processing sections. This model leads to a mixed-integer nonlinear programming problem where the net present value is maximized with respect to design parameters and mass flowrates of the network streams. The genome-scale metabolic network of C. reinhardtii is used to model algae growth in the cultivation section. Closed photobioreactors are less susceptible to contamination, offering better control and higher growth rates then open ponds; however, compared to closed reactors, open ponds are easy to construct, inexpensive, and require minimal maintenance, making them desirable choices for large-scale production [3]. Therefore, we adopt open ponds as the technology of choice for the cultivation section. Here, open ponds are modelled as CSTRs, where the consumption rate of CO₂ and production rate of biomass are directly obtained from the metabolic-network model. This furnishes a reliable basis for the coupled optimization of design parameters and flowrates, which, in turn, leads to more realistic economic assessments under various operating conditions, such as light intensity and substrate concentrations.

 [1] Williams, P. J. L. B., & Laurens, L. M. L. (2010). Microalgae as biodiesel & biomass feedstocks: Review & analysis of the biochemistry, energetics & economics. Energy & Environmental Science, 3(5), 554.

[2] Yue, D., You, F., & Snyder, S. W. (2014). Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges. Computers & Chemical Engineering, 66, 36–56.

[3] Gebreslassie, B. H., Waymire, R., & You, F. (2013). Sustainable design and synthesis of algae-based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration. AIChE Journal, 59(5), 1599–1621.

[4] Chang, R. L., Ghamsari, L., Manichaikul, A., Hom, E. F., Balaji, S., Fu, W., Papin, J. A. (2011). Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol Syst Biol, 7(518), 518.