During the coronavirus disease 2019 (COVID-19) pandemic, many countries opted for strict public health measures, including closing schools. After some time, they have started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using a computer simulation based on a stochastic compartmental model that includes a heterogeneous and dynamic network, we analyse different strategies to reopen schools in the São Paulo Metropolitan Area, including one similar to the official reopening plan. Our model allows us to describe different types of relations between people, each type with a different infectiousness. Based on our simulations and model assumptions, our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, our results also show that a controlled school reopening could possibly avoid the collapse of the health system, depending on how people follow sanitary measures. We estimate that postponing the schools' return date for after a vaccine becomes available may save tens of thousands of lives just in the São Paulo Metropolitan Area compared to a controlled reopening considering a worst-case scenario. We also discuss our model constraints and the uncertainty of its parameters.
This paper encompasses the development of energy balance models to determine temperature dynamic behavior in single- and two-stage anaerobic digestion systems. Modeling assumptions were made according to the reactor type, its operation, energy exchanges, thermodynamics, and kinetics, as well as involved processes, substances, and phases. Stirring, heating, stream enthalpies, and reaction energies from biochemical processes were taken as the main external inputs. The temperature effect on specific growth constants was determined with a cardinal model. A general energy balance was obtained for a non-adiabatic, non-isothermic, triphasic single stage continuous stirred tank reactor digester, and then it was adapted to the case of a two-stage biohydrogen and biomethane mass balance model to obtain a multi-stage energy balance. The model was implemented and simulated in Modelica, then compared with data from a real-life experiment consisting of the digestion of a 1% glucose solution. Concentration and temperature evolution in both stages were analyzed. Temperature changes due to reaction enthalpies were observed mainly in the first stage, hydrolysis being the predominant process at the startup, followed by sugar acidogenesis. The main mechanics of the proposed model were demonstrated, and a reasonable approximation of the expected results was obtained.
This paper presents the development of ADMLib, a new high-productivity and efficient Modelica package to model and simulate anaerobic digestion systems inside the structured modeling framework. Library components were organized into subpackages to encompass growth kinetics, non-biochemical reaction kinetics, acid-base, heat transfer, and inhibition processes, as well as the characteristics of substances and gas phase. A validation of the dynamic behavior response was performed where the implemented functions were used to simulate different bibliographic models. A brief performance analysis was carried out, in order to evaluate the component-based approach of ADMLib against the traditional differential algebraic equation (DAE) systems. The implementation testing demonstrated that the developed package was reliable, usable, and performant.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.