The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that "on-off" policies alternating between strict social distancing and relaxing such restrictions can be effective at "flattening" the curve while likely minimizing social and economic cost. Since its first reported case in early December 2019 in Wuhan, Hubei Province, China, the novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the resulting COVID-19 disease have reached 184 countries/regions causing a total of 149,024 deaths, as of this writing. The rate of spread of the virus is substantially higher than that of similar previously reported epidemics such as SARS and MERS 1. Because of this worldwide and rapid spread, on March 11, 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a global pandemic 2. The exponential increase in the number of confirmed infectious cases and deaths has caused countries all around the world to respond with severe lock-down, quarantining, and social distancing measures to contain the spread of the disease. For example, northern Italy went into a state of emergency on March 8, 2020, imposing a complete lock-down that was expanded to the rest of the country three days later 3. Following Italy's attempts to decrease the contagion rate, Spain enforced a nationwide lock-down on March 16 4. In contrast, the response in the US was significantly slower than those of the aforementioned (and most other European) countries, delaying widespread social distancing measures and disease screening initiatives. Given the exponential growth of virus infections, policy makers face the urgent challenge of determining the appropriate response(s). For example, restrictions on population mobility, increased resource investment (e.g., personal protective equipment, ventilators, hospital beds in intensive care units), as well as improved COVID-19 screening can greatly impact the spread of infection 5,]6. Here, fundamental epidemiological models 7 , typically comprising sets of coupled, nonlinear ordinary differential equations (ODEs), are valuable tools f...

No abstract

No abstract

No abstract

Ionic liquids (ILs) are a promising class of absorbents for next-generation carbon-capture processes. In particular, aprotic heterocyclic anion ILs (AHAs) have received significant attention due to their ability to chemically absorb CO2 without a significant increase in viscosity. The development of process modeling and mathematical optimization approaches for AHA-based carbon-capture processes is essential for the evaluation of economic feasibility before industrial implementation. In this paper, we use a pseudo-transient modeling approach that enables the robust simulation and optimization of the large-scale process flowsheet. A Langmuir-type model that incorporates both the chemical and physical uptake of CO2 is used to describe the equilibrium absorption of CO2 by the IL. A rigorous rate-based model for the absorption column is employed to account for the effect of reversible chemical reactions on mass transfer. The models are then used to carry out economic optimization of a prototypical IL-based carbon-capture process. A sensitivity analysis of important IL properties is provided, revealing the key factors that affect IL choice. Finally, a comparison to an advanced amine-based process is briefly discussed.