RESUMEN
A framework to obtain optimal operating conditions is proposed for a cryogenic air separation unit case study. The optimization problem is formulated considering three objective functions, 11 decision variables, and two constraint setups. Different optimization algorithms simultaneously evaluate the conflicting objective functions: the annualized cash flow, the efficiency at the compression stage, and capital expenditures. The framework follows a modular approach, in which the process simulator PRO/II and a Python environment are combined. The results permit us to assess the applicability of the tested algorithms and to determine optimal operational windows based on the resultant 3-D Pareto fronts.
RESUMEN
COVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than [Formula: see text]. The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.