No abstract

As the pandemic of Coronavirus Disease 2019 (COVID-19) rages throughout the world, accurate modeling of the dynamics thereof is essential. However, since the availability and quality of data varies dramatically from region to region, accurate modeling directly from a global perspective is difficult, if not altogether impossible. Nevertheless, via local data collected by certain regions, it is possible to develop accurate local prediction tools, which may be coupled to develop global models.In this study, we analyze the dynamics of local outbreaks of COVID-19 via a coupled system of ordinary differential equations (ODEs). Utilizing the large amount of data available from the ebbing outbreak in Hubei, China as a testbed, we estimate the basic reproductive number, R0 of COVID-19 and predict the total cases, total deaths, and other features of the Hubei outbreak with a high level of accuracy. Through numerical experiments, we observe the effects of quarantine, social distancing, and COVID-19 testing on the dynamics of the outbreak. Using knowledge gleaned from the Hubei outbreak, we apply our model to analyze the dynamics of outbreak in Turkey. We provide forecasts for the peak of the outbreak and the total number of cases/deaths in Turkey, for varying levels of social distancing, quarantine, and COVID-19 testing.

Hydrate formation is a major flow assurance issue in the petroleum industry, and the knowledge of hydrate inhibitor distribution is essential for the economic gas transportation and processing operation. A number of measurements were made to determine the solubility of methane, the main constituent in natural gas, in methanol and ethanol aqueous solutions. The results showed that the addition of water significantly reduces the solubility in methanol and ethanol. Methanol and ethanol are two of the most commonly used gas hydrate inhibitors in the petroleum industry. The solubility data are important in developing binary interaction parameters used in predicting inhibitor distribution in multicomponent systems containing water. To fill the significant gap in the open literature data, the solubility of methane in 70 and 50 wt % methanol and ethanol solutions at 273.15–298.15 K and 0.46–44 MPa were measured. The average repeatability of the experimental results was calculated to be 2.5%. The results from this work and literature were used to optimize the interaction parameters of the CPA-SRK72 equation of state. The model calculations using a single variable binary interaction parameter was able to reproduce the new experimental results with a significant increase in accuracy.

Solubility data for CO2 in monoethylene glycol (MEG) are limited in the open literature with most of the data limited to temperatures above 298.15 K through a small number of sources. This work focused on the solubility of CO2 in pure MEG in a wider range of temperatures and pressures as experimental data in such conditions are extremely limited in the open literature. These results can be used to optimize EoS and increase prediction reliability due to the wider range. The solubility of CO2 in MEG was measured between 263.15–343.15 K and 0.2–40.3 MPa. The experimental results from this study are compared to available data from the open literature together with the CPA-SRK72 calculations. The data from this work together and open literature were used to calculate a binary interaction parameter (BIP) of 0.053 between 263.15–398.15 K to correlate the experimental data. The experimental results showed an overall absolute average deviation of 4.81% from the calculated modeling results.

As the pandemic of Coronavirus Disease 2019 (COVID-19) rages throughout the world, accurate modeling of the dynamics thereof is essential. However, since the availability and quality of data varies dramatically from region to region, accurate modeling directly from a global perspective is difficult, if not altogether impossible. Nevertheless, via local data collected by certain regions, it is possible to develop accurate local prediction tools, which may be coupled to develop global models. In this