(13g) Superstructure Optimization of Algae Based Biorefinery for Sustainable Production of Value Added Products through Carbon Sequestration

The energy sector is a major contributor of green house gases (GHGs). It has been suggested that fossil fuel power generation can be retrofitted with novel capture, utilization and sequestration of carbon(CCUS) technologies as a means to diminish carbon emissions. However, the technology readiness in terms of economic viability and efficiency need to be assessed quantitatively to determine the impact of such technologies at scale. One such technology, algae-based CCUS augurs well to potentially feature in future energy systems. Amongst biofuel sources, algae has shown great promise owing to: (i) higher biomass growth rates (ii) no direct competition with agriculture for arable land and fresh water (iii) certain engineered strains being agnostic to the water source type i.e. brackish, freshwater etc. (iv) potential oil yields from certain algae strains potentially being considerably higher than other biofuel sources such as soybeans, jatropha, oil palm, etc. [1]. Nevertheless, comprehensive economic and environmental feasibility analysis needs to be carried out to assess the commercial viability of algae based CCUS and benchmark it against extant CCUS processes.
In this presentation, we present our progress in developing an optimization framework to model an algae based biorefinery that uses captured carbon dioxide and converts it into value added products such as biofuels, limonene etc. This framework is applied on an ongoing DOE project (DE FE-0032108) where a novel algae based CCUS technology is being developed. This technology uses an engineered fast growing strain of algae which utilizes carbon dioxide captured through a novel adsorbent technology, and nutrients extracted from wastewater through a novel hydrogel technology. Both the adsorbent and the hydrogel are economically competitive, and show a high selectivity towards carbon dioxide and nutrients respectively. The overall algae biorefinery has various processing steps including: (i) carbon capture, (ii) extraction of nutrients from wastewater, (iii) algae cultivation, (iv) dewatering of biomass, (v) production of value added products such as biofuels and/or limonene, (vi) power and utility generation. Some of the processing steps have competing technologies with varying economic costs and process parameters associated with them. The generated modeling and optimization framework is mixed integer linear in nature and is used to evaluate the economic feasibility of the biorefinery by finding the optimal processing pathways that minimize the annualized cost of production subject to technology selection constraints, mass and energy balance constraints and economic evaluation constraints. The framework also has the capability to assess the tradeoffs that arise with multiple competing objectives such as economic and environmental objectives. The multiscale framework simultaneously provides network planning and operational decisions such as capacity sizing and production levels respectively. Moreover, the sensitivity to considered parameters is evaluated to understand the impact of uncertainty on the model objective. This can then be used for Model Based Design of Experiments (MBDoE) to guide experimentalists towards bottlenecks and opportunities to improve the process in terms of cost competitiveness or perfomance. The network superstructure is modeled using the pyomo python package [2, 3] and optimized using the Gurobi solver [4].