Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

Soft-Sensor for Class Prediction of the Percentage of Pentanes in Butane at a Debutanizer Column

Refineries are complex industrial systems that transform crude oil into more valuable subproducts. Due to the advances in sensors, easily measurable variables are continuously monitored and several data-driven soft-sensors are proposed to control the distillation process and the quality of the resultant subproducts. However, data preprocessing and soft-sensor modelling are still complex and time-consuming tasks that are expected to be automatised in the context of Industry 4.0. Although recently several automated learning (autoML) approaches have been proposed, these rely on model configuration and hyper-parameters optimisation. This paper advances the state-ofthe- art by proposing an autoML approach that selects, among different normalisation and feature weighting preprocessing techniques and various well-known Machine Learning (ML) algorithms, the best configuration to create a reliable soft-sensor for the problem at hand. As proven in this research, each normalisation method transforms a given dataset differently, which ultimately affects the ML algorithm performance. The presented autoML approach considers the features preprocessing importance, including it, and the algorithm selection and configuration, as a fundamental stage of the methodology. The proposed autoML approach is applied to real data from a refinery in the Basque Country to create a soft-sensor in order to complement the operators’ decision-making that, based on the operational variables of a distillation process, detects 400 min in advance with 98.925% precision if the resultant product does not reach the quality standards.This research received no external funding

Desempeño laboral de las enfermeras que trabajan en las instituciones privadas de salud de la ciudad de Ibarra, de enero a junio del 2008

El trabajo de enfermería quizá por su naturaleza es el que más explotación laboral permite. El personal de Enfermeras/os trabajan en turnos intensivos y extensivos de trabajo lo que ocasiona que estén sometidos a condiciones laborales inadecuadas, impactando todo esto en su calidad de vida en lo de los miembros de su familia. Muchas enfermeras ejercen jefatura femenina de hogar y son responsables de la crianza y manutención de los hijos, lo cual agrava su calidad de vida y hacen que acepten ser explotadas en el ámbito laboral para poder cumplir con esta grave responsabilidad.El trabajo.- Modalidades especiales de trabajo en salud.- Trabajadores del sector público.- Contratación por horas.- Ambiente estructurales del trabajo.- Definición y elementos esenciales del contrato individual de trabajo.- Clases de contratos individuales de trabajo.- Contrato escrito y contrato verbal.- Obligaciones de las partes.- Ejecución del contrato individual de trabajo.- Sueldos y salrios.- Remuneraciones adicionales.- Participaciones de las utilidades de la empresa.- Jornadas de trabajo y periodo de descanso.- Periodos de descanso.- Vacaciones anuales

Fichas de aprendizaje de Comunicación 4

Proponemos trabajar las seis fichas de forma flexible y libre, con el compromiso de que tú y tu docente planifiquen y desarrollen las actividades propuestas. Al interior de las fichas, hallarás una situación de la vida real o simulada que implica resolver un reto que te acercará a diversos contextos del Perú y del mundo. En ese sentido, ponemos a tu disposición diversos tipos y géneros textuales que, mediante la realización de actividades, elaboración de productos y evidencias, potenciarán el desarrollo de tus competencias comunicativas. En cada ficha se propone la siguiente secuencia: primero, te presentamos una situación con preguntas a modo de reto; seguidamente, te invitamos a leer la diversidad de textos propuestos, los cuales te permitirán involucrarte en los temas planteados para comprenderlos e interpretarlos, y con los que podrás desarrollar las actividades de lectura —por medio de las cuales queremos que profundices tu comprensión—, y otras dirigidas a la elaboración de los productos orales y escritos

Museos y exposiciones en Madrid como oportunidad de trabajar conceptos-clave de las Ciencias Sociales con estudiantes Erasmus+ Incoming en colaboración con estudiantes UCM, asociaciones de estudiantes, oficina de relaciones internacionales y PDI

Proyecto de Innovación que pone en contacto los recursos museísticos y expositivos de Madrid con los estudiantes internacionales para trabajar conceptos-clave en las ciencias sociales, como vulnerabilidad y comunicació

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Multi-output chemometrics model for gasoline compounding

Computational models may help to reduce research cost by predicting properties of alternative blends. Nowadays, most efforts focus on prediction of a few properties for sets of gasoline samples. However, there are no reports of models able for classification of gasoline samples with multiple output properties measured in real life refinery plants. In this work, Information Fusion (IF), Perturbation Theory (PT), and Machine Learning (ML) algorithm (IFPTML) was used to model real production data with >230,000 outcomes gathered from a petroleum refinery plant. IF-pre-processing phase assembled the working dataset with 44 physicochemical output properties vs. 574 input variables of 4 production lines distributed in 26 data blocks including 14 different streams and 23 operations carried out in the plant. PT-calculation phase quantifies the effect of perturbations (deviations) in all input variables using PT Operators. Last, in ML-analysis phase involved Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) models training. IFPTML-LDA model presented AUROC = 0.936 with overall Sensitivity Sn and Specificity Sp ≈ 84–91% for training and validation sets. In internal control experiment we obtained an IFPTML-FT-NIR model with similar Sn and Sp ≈ 86–97%, for >25,000 values of 16 properties measured FT-NIR technique; demonstrating the robustness of the algorithm to changes on the experimental techniques used. This model could be useful for the design of new alternatives blends (biofuels, refuse-derived fuels, etc.) with lower environmental impact.The authors acknowledge financial support from Basque government SPRI ELKARTEK program grant (KK-2019/00037). The authors also acknowledge partial financial support from research grants of Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P) and Basque Government (Eusko Jaurlaritza) consolidation groups grant (IT1045-16). G.D.H. personally acknowledges the support of IKERBASQUE, Basque Foundation for Science. J.P.-M. thanks Petronor S.A. for their support.Peer reviewe

Ansiedad en niños y cuidadores que acuden a un servicio de urgencias pediátricas hospitalarias

Introducción: La ansiedad, que en su origen tiene una función adaptativa, es una reacción emocional ante una situación que pone en peligro la integridad del individuo, sea esta amenaza real o no. Partiendo de la hipótesis de que acudir a un servicio de urgencia puede ser secundario a niveles elevados de ansiedad basal en los padres, proponemos un estudio en el que medimos la ansiedad estado y rasgo tanto de los niños como del progenitor y otros factores que pudieran influir en la frecuentación a este servicio.Objetivos: Determinar los niveles de ansiedad en los niños y acompañantes que demandan atención sanitaria en un servicio de urgencias hospitalarias, así como correlacionar otras variables que puedan influir o se asocien a los niveles de ansiedad de los mismos.Material y métodos: Se realiza entrevista individual a todos los niños entre 9 y 14 años y sus acompañantes, tras ser atendidos en el servicio de urgencias de pediatría y traumatología en horario de tarde. Se obtiene una muestra compuesta de un total de 105 casos. Además del cuestionario socio–demográfico, se aplican el Cuestionario de Autoevaluación ansiedad estado–rasgo en niños (STAIC) y el Inventario de ansiedad estado–rasgo (STAI) de adultos a los acompañantes.Resultados: No se han hallado niveles elevados de ansiedad estadísticamente significativos ni en los niños ni en sus cuidadores durante su paso por la urgencia ni en ansiedad–rasgo ni en ansiedad–estado. Un nivel de ansiedad elevado como rasgo de personalidad, no está relacionado con la visita a urgencias ni en los niños ni en sus cuidadores ante situaciones de no urgencia. Ni tampoco se encuentra un nivel de ansiedad–estado elevado que justifique la visita al servicio de urgencia debido a una situación percibida como urgente.Conclusión: Ninguna de las variables estudiadas ha demostrado una relación estadísticamente significativa con niveles elevados de ansiedad ni en los niños ni en sus cuidadores cuando acuden a un servicio de urgencias

Real-world analysis of main clinical outcomes in patients with polycythemia vera treated with ruxolitinib or best available therapy after developing resistance/intolerance to hydroxyurea.

Ruxolitinib is approved for patients with polycythemia vera (PV) who are resistant/intolerant to hydroxyurea, but its impact on preventing thrombosis or disease-progression is unknown. A retrospective, real-world analysis was performed on the outcomes of 377 patients with resistance/intolerance to hydroxyurea from the Spanish Registry of Polycythemia Vera according to subsequent treatment with ruxolitinib (n = 105) or the best available therapy (BAT; n = 272). Survival probabilities and rates of thrombosis, hemorrhage, acute myeloid leukemia, myelofibrosis, and second primary cancers were calculated according to treatment. To minimize biases in treatment allocation, all results were adjusted by a propensity score for receiving ruxolitinib or BAT. Patients receiving ruxolitinib had a significantly lower rate of arterial thrombosis than those on BAT (0.4% vs 2.3% per year; P = .03), and this persisted as a trend after adjustment for the propensity to have received the drug (incidence rate ratio, 0.18; 95% confidence interval, 0.02-1.3; P = .09). There were no significant differences in the rates of venous thrombosis (0.8% and 1.1% for ruxolitinib and BAT, respectively; P = .7) and major bleeding (0.8% and 0.9%, respectively; P = .9). Ruxolitinib exposure was not associated with a higher rate of second primary cancers, including all types of neoplasia, noncutaneous cancers, and nonmelanoma skin cancers. After a median follow-up of 3.5 years, there were no differences in survival or progression to acute leukemia or myelofibrosis between the 2 groups. The results suggest that ruxolitinib treatment for PV patients with resistance/intolerance to hydroxyurea may reduce the incidence of arterial thrombosis. Ruxolitinib is better than other available therapies in achieving hematocrit control and symptom relief in patients with polycythemia vera who are resistant/intolerant to hydroxyurea, but we still do not know whether ruxolitinib provides an additional benefit in preventing thrombosis or disease progression. We retrospectively studied the outcomes of 377 patients with resistance/intolerance to hydroxyurea from the Spanish Registry of Polycythemia Vera according to whether they subsequently received ruxolitinib (n = 105) or the best available therapy (n = 272). Our findings suggest that ruxolitinib could reduce the incidence of arterial thrombosis, but a disease-modifying effect could not be demonstrated for ruxolitinib in this patient population