4 research outputs found

    Modeling the reserve osmosis processes performance using artificial neural networks

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    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

    Multi-tier framework for the inferential measurement and data-driven modeling

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    A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat aix铆 com la integraci贸 de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'an脿lisi explorat貌ria de dades ha esta avaluat a partir de la caracteritzaci贸 de l'espai qu铆mic corresponent a la biodegradaci贸 de certs compostos org脿nics. Fruit d'aquest an脿lisi s'han establert relacions entre diverses variables f铆sico-qu铆miques que han estat emprades posteriorment per al desenvolupament de models de biodegradaci贸. A nivell del preproc茅s de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecci贸 de variables basada en l'煤s del Mapes Autoorganitzats (SOM). Tot i que el m猫tode proposat selecciona, en general, un major nombre de variables que altres m猫todes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat tamb茅 tot un conjunt de t猫cniques d'imputaci贸 de dades basades en el SOM amb un conjunt de dades est脿ndard corresponent als par脿metres d'operaci贸 d'una planta de tractament d'aig眉es residuals. Es proposa i avalua en un problema de predicci贸 de qualitat en aigua un nou model din脿mic per a ajustar el centre i la dispersi贸 en xarxes de funcions de base radial. El m猫tode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat tamb茅 al desenvolupament de models predictius i de classificaci贸 de les velocitats de biodegradaci贸 de compostos org脿nics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximaci贸 per a desenvolupar models basats en dades en aquells casos en els que la complexitat de din脿mica del proc茅s impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'煤s de algorismes de generaci贸 de regles i de grafs de depend猫ncia bayesiana per a introduir una nova capa que faciliti la interpretaci贸 dels models. Els resultats preliminars obtinguts a partir de la classificaci贸 dels Modes d'acci贸 T貌xica (MOA) apunten a que l'煤s dels MOA com a indicadors intermediaris dels efectes dels compostos qu铆mics en la salut 茅s una aproximaci贸 factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capa莽 d'inferir 铆ndexs de qualitat a partir de variables prim脿ries de proc茅s. El sensor resultant ha estat implementat en una planta qu铆mica real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcin貌gens d'un grup de compostos arom脿tics a partir de la seva estructura molecular. Els resultats obtinguts despr猫s d'aplicar el m猫tode de selecci贸 de variables basat en el SOM milloren els resultats pr猫viament publicats. Aquest marc de treball s'ha usat tamb茅 per a proporcionar una nova aproximaci贸 al modelat ambiental i l'an脿lisi de risc amb sistemes d'informaci贸 geogr脿fica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposici贸 i per a desenvolupar un nou m猫tode d'interpolaci贸 geogr脿fica. La combinaci贸 del SOM amb els models de mescla de gaussianes dona una nova formulaci贸 al problema de l'an脿lisi de risc des d'un punt de vista probabil铆stic

    Classification Of Antarctic Algae By Applying Kohonen Neural Network With 14 Elements Determined By Inductively Coupled Plasma Optical Emission Spectrometry

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    Optical emission spectrometers can generate results, which sometimes are not easy to interpret, mainly when the analyses involve classifications. To make simultaneous data interpretation possible, the Kohonen neural network is used to classify different Antarctic algae according to their taxonomic groups from the determination of 14 analytes. The Kohonen neural network architecture used was 5脳5 neurons, thus reducing 14-dimension input data to two-dimensional space. The input data were 14 analytes (As, Co, Cu, Fe, Mn, Sr, Zn, Cd, Cr, Mo, Ni, Pb, Se, V) with their concentrations, determined by inductively coupled plasma optical emission spectrometry in 11 different species of algae. Three taxonomic groups (Rhodophyta, Phaeophyta and Cholorophyta) can be differentiated and classified through only their Cu content. 漏 2005 Elsevier B.V. All rights reserved.605725730Martens, H., Naes, T., (1989) Multivariate Calibration, , Wiley New YorkMoberg, L., Robertsson, G., Karlberg, B., Spectrofluorimetric determination of chlorophylls and pheopigments using parallel factor analysis (2001) Talanta, 54, pp. 161-170Da Silva, J.C.G.E., Leit茫o, J.M.M., Costa, F.S., Ribeiro, J.L.A., Detection of verapamil drug by fluorescence and trilinear decomposition techniques (2002) Analytica Chimica Acta, 453, pp. 105-115Pereira-Filho, E.R., P茅rez, C.A., Poppi, R.J., Arruda, M.A.Z., Metals distribution and investigation of L'vov platform surface using principal component analysis, multi-way principal component analysis, micro synchrotron radiation X-ray fluorescence spectrometry and scanning electron microscopy after the determination of Al in a milk slurry sample (2002) Spectrochimica Acta, 57, pp. 1259-1276Pereira-Filho, E.R., Sena, M.M., Arruda, M.A.Z., Poppi, R.J., Exploratory analysis of L'vov platform surfaces for electrothermal atomic absorption spectrometry by using three-way chemometric tools (2003) Analytica Chimica Acta, 495, pp. 177-193Tokutaka, H., Yoshihara, K., Fujimura, K., Obu-Cann, K., Iwamoto, K., Surface and interface analysis of GaSb/GaAs semiconductor materials (1999) Applications of Surface Science, 144-145, pp. 59-66Vander Heyden, Y., Vankeerberghen, P., Novic, M., Zupan, J., Massart, D.L., The application of Kohonen neural networks to diagnose calibration problems in atomic absorption spectrometry (2000) Talanta, 51, pp. 455-466Maia, D.R.J., Balbinot, L., Poppi, R.J., De Paoli, M.-A., Effect of conducting carbon black on the photostabilization of injection molded poly(propylene-co-ethylene) containing TiO2 (2003) Polymer Degradation and Stability, 82, pp. 89-98Fid锚ncio, P.H., Ruis谩nchez, I., Poppi, R.J., Application of artificial neural networks to the classification of soils from S茫o Paulo state using near-infrared spectroscopy (2001) Analyst, 126, pp. 2194-2200Mazzatorta, P., Vracko, M., Jezierska, A., Benfenati, E., Modeling toxicity by using supervised Kohonen neural networks (2003) Journal of Chemical Information and Computer Sciences, 43, pp. 485-492Zupan, J., Novic, M., Ruis谩nchez, I., Kohonen and counterpropagation artificial neural networks in analytical chemistry (1997) Chemometrics and Intelligent Laboratory, 38, pp. 1-23Far铆as, S., P茅rez Arisnabarreta, S., Vodopivez, C., Smichowski, P., Levels of essential and potentially toxic trace metals in Antarctic macro algae (2002) Spectrochimica Acta, 57, pp. 2133-2140Haykin, S., (2001) Redes Neurais-Princ铆pios e pr谩tica, , 2nd ed. Bookman Porto AlegreMelssen, W.J., Smits, J.R.M., Buydens, L.M.C., Kateman, G., Using artificial neural networks for solving chemical problems: Part II. Kohonen self-organising feature maps and Hopfield networks (1994) Chemometrics and Intelligent Laboratory, 23, pp. 267-291Zupan, J., Gasteiger, J., (1999) Neural Networks in Chemistry and Drug Design, , 2nd ed. Wiley-VCH WeinheimRantanen, J.T., Laine, S.J., Antikainen, O.K., Mannermaa, J.-P., Simula, O.E., Yliruusi, J.K., Visualization of fluid-bed granulation with self-organizing maps (2001) Journal of Pharmaceutical and Biomedical, 24, pp. 343-352Mauseth, J.D., (1998) Botany: An Introduction to Plant Biology, , Jones and Bartlett Publishers SudburySmichowski, P., Far铆as, S., Valiente, L., Iribarren, M.L., Vocopivez, C., Total As content of nine species of Antarctic macro algae as determined by electrothermal atomic absorption spectrometry (2004) Analytical Bioanalysis Chemistry, 378, pp. 465-469Szolnoki, A.C., B谩thori, M., Blunden, G., Determination of elements in algae by different atomic spectroscopic methods (2000) Micochemistry Journal, 67, pp. 39-42Vodopivez, C., Smichowski, P., Marcovecchio, J., Trace metals monitoring as a tool for characterization of Antarctic ecosystems and environmental management. the Argentine Programme at Jubany Station (2001) Environmental Contamination in Antarctica. a Challenge to Analytical Chemistry, pp. 155-180. , S. Caroli P. Cescon D.W.H. Walton Elsevier Amsterda
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