(139e) Systems Design Approach for Dynamic Optimization of Algae Based Ccus Process

Carbon neutrality has become one of the key initiatives that both governments and industries are striving to achieve. Currently, the energy sector is one of the biggest sources of bulk carbon generation and thus decarbonizing the energy sector is pivotal to achieve net carbon neutrality. To this end, various carbon capture and utilization (CCUS) technologies have been retrofitted in extant processes to capture and convert CO₂ emissions into valuable products [1]. Algae based CCUS process is a novel technology that has shown great promise to potentially feature in future energy systems. Algae as a bio-fuel source has several advantages compared to other traditional plant based biomass, including- i) high biomass growth rate ii) presence of strains that are relatively agnostic to water source type i.e. brackish, freshwater etc. iii) does not directly compete with the FEWN for arable land and fresh water. However, questions do arise on the economical and commercial viability of the algae production process. Harvesting of algae is one of the most energy intensive processes accounting for ~70% of total energy consumption. Optimizing the harvesting schedule is key to maximizing the productivity of algae as high optical density can result in algae cell death which is economically unfavorable. This begs the need for high fidelity process modeling [2] that captures the dynamics of the process and helps in optimizing the process conditions.

In this presentation, we present our progress towards developing a dynamic model to ameliorate the technical feasibility of the process, and identify the optimal process conditions to maximize the production of high value products. For novel CCUS technologies to reach the required technological maturity level for deployment at scale, it is necessary to optimize the process conditions that maximize the economic viability of the process. To this end, development of high fidelity process models that accurately capture the dynamics of the process are necessary. These models can be highly non-linear in nature and accurately predict the dynamics of the system under different process conditions. The algae based CCUS process is an ordinary differential equation (ODE) model that incorporates the Monod equations for describing reaction kinetics. The model is dependent on various factors including the concentration of nutrients and CO₂ in the medium, light intensity and factors that are inherent to the strain of the algae. The process is modeled using the pyomo.dae toolbox [3,4], and optimized using a nonlinear programming solver.

References

[1] J. F. D. Tapia, J.-Y. Lee, R. E. Ooi, D. C. Foo, and R. R. Tan, “A review of optimization and decision-making models for the planning of co2 capture, utilization and storage (ccus) systems,” Sustainable Production and Consumption, vol. 13, pp. 1–15, 2018.

[2] E. Lee, M. Jalalizadeh, and Q. Zhang, “Growth kinetic models for microalgae cultivation: A review,” Algal research, vol. 12, pp. 497–512, 2015.

[3] M. L. Bynum, G. A. Hackebeil, W. E. Hart, C. D. Laird, B. L. Nicholson, J. D. Siirola, J.-P. Watson, and D. L. Woodruff, Pyomo–optimization modeling in python, vol. 67. Springer Science & Business Media, third ed., 2021.

[4] W. E. Hart, J.-P. Watson, and D. L. Woodruff, “Pyomo: modeling and solving mathematical programs in python,” Mathematical Programming Computation, vol. 3, no. 3, pp. 219–260, 2011.