The coronavirus disease 2019 (COVID-19) outbreak has burdened several countries. Its high transmissibility and mortality rate have caused devastating impacts on human lives. This has led countries to implement control strategies, such as social distancing, travel bans, and community lockdowns, with varying levels of success. However, a disease outbreak can cause significant economic disruption from business closures and risk avoidance behaviors. This paper raises policy recommendations through a system dynamics modeling approach. The developed model captures relationships, feedbacks, and delays present in a disease transmission system. The dynamics of several policies are analyzed and assessed based on effectiveness in mitigating infection and the resulting economic strain.

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Biofuel production from microalgae biomass has been considered a viable alternative to harmful fossil fuels; however, challenges are faced regarding its economic sustainability. Process integration to yield various high-value bioproducts is implemented to raise profitability and sustainability. By incorporating a circular economy outlook, recirculation of resource flows is maximized to yield economic and environmental benefits through waste minimization. However, previous modeling studies have not looked into the opportunity of integrating productivity reduction related to the continuous recirculation and reuse of resources until it reaches its end of life. In this work, a novel multi-objective optimization model is developed centered on an algal biorefinery that simultaneously optimizes cost and environmental impact, adopts the principle of resource recovery and recirculation, and incorporates the life cycle assessment methodology to properly account for the environmental impacts of the system. An algal biorefinery involving end-products such as biodiesel, glycerol, biochar, and fertilizer was used for a case study to validate the optimization model. The generated optimal results are assessed and further analyzed through scenario analysis. It was seen that demand fluctuations and process unit efficiencies have significant effect on the optimal results.

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