30 research outputs found
Modeling the reserve osmosis processes performance using artificial neural networks
Una de las aplicaciones m谩s importante de los procesos de filtraci贸n por membrana es en el 谩rea de tratamiento de agua por ultrafiltraci贸n, nanofiltraci贸n u 贸smosis inversa. Entre los problemas m谩s serios encontrados en estos procesos destaca la aparici贸n de los fen贸menos de ensuciamiento y envejecimiento de las membranas que limitan la eficacia de la operaci贸n tanto en la separaci贸n de los solutos, como en el flujo de permeado, afectando tambi茅n el ciclo de vida de las membranas.Para reducir el coste de la producci贸n y mejorar la robustez y eficacia de estos procesos es imprescindible disponer de modelos capaces de representar y predecir la eficiencia y el comportamiento de las membranas durante la operaci贸n. Una alternativa viable a los modelos te贸ricos, que presentan varias particularidades que dificultan su postulado, la constituyen los modelos basados en el an谩lisis de los datos experimentales, entre cuales destaca el uso de las redes neuronales. Dos metodolog铆as han sido evaluadas e investigadas, una constando en la caracterizaci贸n de las interacciones entre las membranas y los compuestos org谩nicos presentes en el agua de alimentaci贸n, y la segunda basada en el modelado de la din谩mica de operaci贸n de las plantas de desalinizaci贸n por 贸smosis inversa.Relaciones cuantitativas estructura‐propiedad se han derivado usando redes neuronales de tipo back‐propagation, para establecer correlaciones entre los descriptores moleculares de 50 compuestos org谩nicos de preocupaci贸n para la salud p煤blica y su comportamiento frente a 5 membranas comerciales de 贸smosis inversa, en t茅rminos de permeaci贸n, absorci贸n y rechazo. Para reducir la dimensi贸n del espacio de entrada, y para evitar el uso de la informaci贸n redundante en el entrenamiento de los modelos, se han usado tres m茅todos para seleccionar el menor n煤mero de los descriptores moleculares relevantes entre un total de 45 que caracterizan cada mol茅cula. Los modelos obtenidos se han validado utilizando un m茅todo basado en el balance de materia, aplicado no solo a los 50 compuestos utilizados para el desarrollo de los modelos, sino que tambi茅n a un conjunto de 143 compuestos org谩nicos nuevos. La calidad de los modelos obtenidos es prometedora para la extensi贸n de la presente metodolog铆a para disponer de una herramienta comprensiva para entender, determinar y evaluar el comportamiento de los solutos org谩nicos en el proceso de 贸smosis inversa. Esto servir铆a tambi茅n para el dise帽o de nuevas y m谩s eficaces membranas que se usan en este tipo de procesos.En la segunda parte, se ha desarrollado una metodolog铆a para modelar la din谩mica de los procesos de 贸smosis inversa, usando redes neuronales de tipo backpropagation y Fuzzy ARTMAP y datos experimentales que proceden de una planta de desalinizaci贸n de agua salobre Los modelos desarrollados son capaces de evaluar los efectos de los par谩metros de proceso, la calidad del agua de alimentaci贸n y la aparici贸n de los fen贸menos de ensuciamiento sobre la din谩mica de operaci贸n de las plantas de desalinizaci贸n por osmosis inversa. Se ha demostrado que estos modelos se pueden usar para predecir el funcionamiento del proceso a corto tiempo, permitiendo de esta manera la identificaci贸n de posibles problemas de operaci贸n debidas a los fen贸menos de ensuciamiento y envejecimiento de las membranas. Los resultados obtenidos son prometedores para el desarrollo de estrategias de optimizaci贸n, monitorizaci贸n y control de plantas de desalinizaci贸n de agua salobre. Asimismo, pueden constituir la base del dise帽o de sistemas de supervis贸n capaces de predecir y advertir etapas de operaci贸n incorrecta del proceso por fallos en el mismo, y actuar en consecuencia para evitar estos inconvenientes.One of the more serious problems encountered in reverse osmosis (RO) water treatment processes is the occurrence of membrane fouling, which limits both operation efficiency (separation performances, water permeate flux, salt rejection) and membrane life‐time. The development of general deterministic models for studying and predicting the development of fouling in full‐scale reverse osmosis plants is burden due to the complexity and temporal variability of feed composition, diurnal variations, inability to realistically quantify the real‐time variability of feed fouling propensity, lack of understanding of both membrane‐foulants interactions and of the interplay of various fouling mechanisms. A viable alternative to the theoretical approaches is constituted by models developed based on direct analysis of experimental data for predicting process operation performance. In this regard, the use of artificial neural networks (ANN) seems to be a reliable option. Two approaches were considered; one based on characterizing the organic compounds passage through RO membranes, and a second one based on modeling the dynamics of permeate flow and separation performances for a full‐scale RO desalination plant.Organic solute sorption, permeation and rejection by RO membranes from aqueous solutions were studied via artificial neural network based quantitative structure‐property relationships (QSPR) for a set of 50 organic compounds for polyamide and cellulose acetate membranes. The separation performance for the organic molecules was modeled based on available experimental data achieved by radioactivity measurements to determine the solute quantity in feed, permeate and sorbed by the membrane. Solute rejection was determined from a mass balance on the permeated solution volume. ANN based QSPR models were developed for the measured organic sorbed (M) and permeated (P) fractions with the most appropriate set of molecular descriptors and membrane properties selected using three different feature selection methods. Principal component analysis and self‐organizing maps pre‐screening of all 50 organic compounds defined by 45 considered chemical descriptors were used to identify the models applicability domain and chemical similarities between the organic molecules. The ANN‐based QSPRs were validated by means of a mass balance test applied not only to the 50 organic compounds used to develop the models, but also to a set of 143 new compounds. The quality of the QSPR/NN models developed suggests that there is merit in extending the present compound database and extending the present approach to develop a comprehensive tool for assessing organic solute behavior in RO water treatment processes. This would allow also the design and manufacture of new and more performing membranes used in such processes.The dynamics of permeate flow rate and salt passage for a RO brackish water desalination pilot plant were captured by ANN based models. The effects of operating parameters, feed water quality and fouling occurrence over the time evolution of the process performance were successfully modeled by a back‐propagation neural network. In an alternative approach, the prediction of process performance parameters based on previous values was achieved using a Fuzzy ARTMAP analysis. The neural network models built are able to capture changes in RO process performance and can successfully be used for interpolation, as well as for extrapolation prediction, fact that can allow reasonable short time forecasting of the process time evolution. It was shown that using real‐time measurements for various process and feed water quality variables, it is possible to build neural network models that allow better understanding of the onset of fouling. This is very encouraging for further development of optimization and control strategies. The present methodology can be the basis of development of soft sensors able to anticipate process upsets
Abordando el acoso escolar en las clases de ingl茅s: una propuesta de ense帽anza-aprendizaje por proyectos
This dissertation conforms a unit plan based on the methodology of project-based learning, that responds to a series of needs detected in my placement period. The unit plan blends together the principles of cooperative learning, motivational components, and the motif to foster communication among the learners in order to tackle an issue of social concern, namely bullying. The proposal attends the legal framework, the learners' needs for more active participation in the learning process and the opportunity to link the classroom learning environment with the social development
Textilbeton szerkezeti elemek
A textilbeton olyan l煤g谩ll贸 h谩l贸val meger艖s铆tett beton, amely ellen谩ll a korr贸zi贸nak. Mechanikai tulaj-dons谩gainak k枚sz枚nhet艖en k枚nnyebb szerkezeti elemek megval贸s铆t谩s谩ra is alkalmazhat贸. Egy hom-lokzati lemez meg egy l谩mpaoszlop sz茅lnyom谩s alatti viselked茅se lett modellezve 茅s tanulm谩nyozva. A szakirodalombeli adatok alapj谩n kalibr谩lt numerikus szimul谩ci贸k olyan inform谩ci贸kat ny煤jtottak, melyeket a tov谩bbi gyakorlati k铆s茅rletek tervez茅sekor lehet, hogy 茅rdemes figyelembe venni
Textile-Reinforced Concrete Structural Elements
The textile reinforced concrete is a material with increased mechanical properties that can allow the production of lighter structural elements. The alkali-resistant textile reinforcement is not affected by corrosion. A structural facade panel and a light pole were modeled in order to study their behavior in the case of wind pressure. The developed numerical simulations were calibrated according to available data from the literature. These simulations revealed information potentially useful in the planning of further experimental tests