Vaccine allocation strategies for different coverage scenarios.

<p>Vaccine allocation strategies for different coverage scenarios.</p

Vaccine allocation strategies for different response time scenarios.

<p>Vaccine allocation strategies for different response time scenarios.</p

Integrating Simulation in Optimal Synthesis and Design of Natural Gas Upstream Processing Networks

A natural gas upstream processing network consists of several main processing units. Many process configurations are available for selection, and the choice of technologies can be vast. There is no single technology or process configuration that is superior in all aspects. Thus, there is a need for a mathematical model that considers different flowsheet configurations and operating mode options and selects optimally among them. In this paper, a comprehensive design and operational mixed integer programming model is presented for superstructure optimization to optimally select the most cost-effective pathway in natural gas upstream processing networks. The key processing units of the considered processing network include stabilization, acid gas removal, dehydration, sulfur recovery, natural gas liquid (NGL) recovery, and NGL fractionation. The developed optimization model considers a superstructure with all available technologies for each processing step as well as mode of operation, such as variations in temperature and pressure which impacts the product yields. These units have been simulated using ASPEN Plus to determine the yields of different units for each design alternative under different operating modes. The bilinear terms in the resulting mixed integer nonlinear programming (MINLP) model are linearized based on either input or output streams, whichever are less in number. The model has been applied to design and operate optimally the natural gas upstream processing network. Two illustrative case studies are presented to show the applicability of the overall framework and formulated models

Objective values of recommended vaccine allocation strategies.

<p>Objective values of recommended vaccine allocation strategies.</p

Input parameters of FluTe and the SEIR model.

<p>Input parameters of FluTe and the SEIR model.</p

Vaccine allocation strategies obtained by FluTe+MADS and SEIR+MADS under the base-case scenario.

<p>Vaccine allocation strategies obtained by FluTe+MADS and SEIR+MADS under the base-case scenario.</p

Vaccine allocation strategies derived by FluTe+MADS and SEIR+MADS under all objective functions for various R₀ values (30% vaccine coverage, no delay in response time).

<p>(a) SEIR+MADS with the TC objective. (b) FluTe+MADS with the TC objective. (c) SEIR+MADS with the TI objective. (d) FluTe+MADS with the TI objective. (e) SEIR+MADS with the TD objective. (f) FluTe+MADS with the TD objective. (g) SEIR+MADS with the TY objective. (h) FluTe+MADS with the TY objective.</p

Cumulative number of infections in each age group of FluTe and the SEIR model after the calibration process for <i>R</i>₀ = 1.2 without vaccination.

<p>(a) Total population. (b) Preschool children (0–4). (c) School children (5–18). (d) Young adults (19–29). (e) Adults (30–64). (f) Seniors (65+).</p