Methodology for predicting oily mixture properties in the mathematical modeling of molecular distillation

A methodology for predicting the thermodynamic and transport properties of a multi-component oily mixture, in which the different mixture components are grouped into a small number of pseudo components is shown. This prediction of properties is used in the mathematical modeling of molecular distillation, which consists of a system of differential equations in partial derivatives, according to the principles of the Transport Phenomena and is solved by an implicit finite difference method using a computer code. The mathematical model was validated with experimental data, specifically the molecular distillation of a deodorizer distillate (DD) of sunflower oil. The results obtained were satisfactory, with errors less than 10% with respect to the experimental data in a temperature range in which it is possible to apply the proposed method.Fil: Gayol, Maria Fernanda. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Pramparo, Maria del Carmen. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Departamento de Tecnología Química; ArgentinaFil: Miro Erdmann, Silvia M.. Universidad Nacional de San Luis. Facultad de Ingeniería y Ciencias Agropecuarias; Argentin

Comparison of seven prognostic tools to identify low-risk pulmonary embolism in patients aged <50 years

In young patients with acute pulmonary embolism (PE), the predictive value of currently available prognostic tools has not been evaluated. Our objective was to compare prognostic value of 7 available tools (GPS, PESI, sPESI, Prognostic Algorithm, PREP, shock index and RIETE) in patients aged &lt;50 years. We used the RIETE database, including PE patients from 2001 to 2017. The major outcome was 30-day all-cause mortality. Of 34,651 patients with acute PE, 5,822 (17%) were aged &lt;50 years. Of these, 83 (1.4%) died during the first 30 days. Number of patients deemed low risk with tools was: PREP (95.9%), GPS (89.6%), PESI (87.2%), Shock index (70.9%), sPESI (59.4%), Prognostic algorithm (58%) and RIETE score (48.6%). The tools with a highest sensitivity were: Prognostic Algorithm (91.6%; 95% CI: 85.6\u201397.5), RIETE score (90.4%; 95%CI: 84.0\u201396.7) and sPESI (88%; 95% CI: 81\u201395). The RIETE, Prognostic Algorithm and sPESI scores obtained the highest overall sensitivity estimates for also predicting 7- and 90-day all-cause mortality, 30-day PE-related mortality, 30-day major bleeding and 30-day VTE recurrences. The proportion of low-risk patients who died within the first 30 days was lowest using the Prognostic Algorithm (0.2%), RIETE (0.3%) or sPESI (0.3%) scores. In PE patients less 50 years, 30-day mortality was low. Although sPESI, RIETE and Prognostic Algorithm scores were the most sensitive tools to identify patients at low risk to die, other tools should be evaluated in this population to obtain more efficient results