Nonlinear Model Predictive Control of a Wastewater Treatment Process Fitted with a Submerged Membrane Bioreactor

International audienceSubmerged membrane bioreactors are increasingly applied for wastewater treamentbut requires a tight control of the membrane fouling so as to ensure safe and efficient operation.The objective of this paper is to design a nonlinear model predictive control to minimize theirreversible resistance while keeping the trans-membrane pressure, which is a good indicatorof membrane fouling, at an acceptable level. To this end, the manipulated variables are thepermeate flow and the air scouring flow, which allows the material layer formed on the membrane(in short the “cake”) to be detached. The NMPC structure is tested in simulation consideringa detailed simulator as the reference process, and a reduced-order model as the predictor. Theresults show that the process can be regulated until the irreversible resistance takes the mainrole in the fouling resistance. When this state is reached, a chemical cleaning is required, or alarger trans-membrane pressure has to be accommodated

Design, Analysis and Validation of a Simple Dynamic Model of a Submerged Membrane Bioreactor

International audienceIn this study, a simple dynamic model of a submerged Membrane BioReactor (sMBR) is proposed, which would be suitable for process control. The system dynamics is first analyzed showing the existence of three different time scales. The existence of slow-fast dynamics is central to the development of a dedicated parameter estimation procedure. The proposed model structure is validated using realistic simulation data from a detailed simulator built in a well-established environment, namely GPS-X. Finally, a nonlinear model predictive control is designed to illustrate the potential of the developed model within a model-based control structure. The problem of water treatment in a recirculating aquaculture system is considered as an application example

A novel case of human visceral leishmaniasis from the urban area of the city of Rio de Janeiro: autochthonous or imported from Spain ?

Submitted by Janaína Nascimento ([email protected]) on 2019-02-07T11:55:47Z No. of bitstreams: 1 ve_Silva_Guilherme_etal_INI_2017.pdf: 476774 bytes, checksum: 117ce9df08684188394f5ff125a0909f (MD5)Approved for entry into archive by Janaína Nascimento ([email protected]) on 2019-02-08T10:52:32Z (GMT) No. of bitstreams: 1 ve_Silva_Guilherme_etal_INI_2017.pdf: 476774 bytes, checksum: 117ce9df08684188394f5ff125a0909f (MD5)Made available in DSpace on 2019-02-08T10:52:32Z (GMT). No. of bitstreams: 1 ve_Silva_Guilherme_etal_INI_2017.pdf: 476774 bytes, checksum: 117ce9df08684188394f5ff125a0909f (MD5) Previous issue date: 2017Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle. Serviço de Anatomia Patológica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil

A novel case of human visceral leishmaniasis from the urban area of the city of Rio de Janeiro: autochthonous or imported from Spain ?

Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle, 10ª Enfermaria. Rio de Janeiro, RJ, Brasil.Universidade Federal do Estado do Rio de Janeiro. Centro de Ciências Biológicas e da Saúde. Hospital Universitário Gaffrée e Guinle. Serviço de Anatomia Patológica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Laboratório de Pesquisa Clínica e Vigilância em Leishmanioses. Rio de Janeiro, RJ, Brasil

Use of multivariate statistical methods for classification of olive oil

Multivariate statistical methods can contribute significantly to classification studies of extra virgin and common olive oil groups. Therefore, nuclear magnetic resonance (NMR) was used to discriminate olive oil samples, multivariate statistical techniques Principal Component Analysis - PCA, Fuzzy Cluster, Silhouette Validation Method to describe and classify. The groups' distinction into organic and common was observed by applying the non-hierarchical Fuzzy grouping with a distinction between the two groups with a 65% confidence interval. The validation was performed by the silhouette index that presented S (i) of 0.73, which showed that the adopted grouping presented adequate strength and distinction criterion. However, PCA only analyzed the behaviors of data from extra virgin olive oil. Thus, the Fuzzy clustering method was the most suitable for classifying extra virgin olive oil

description of the methodological approach

publishersversionpublishe

Modélisation non-linéaire, identification et contrôle des bioréacteurs à membranes

This thesis proposes a simple submerge membrane bioreactor (sMBR) dynamic model that comprises physical and biological process behaviors. The filtration, physical aspect, is a resistance-in-series model that is composed with reversible resistance, linked to sludge cake formation process that can be detached by air scouring, and the irreversible fouling resistance. The biological feature is implemented extending the simple chemostat model to the filtration mechanism. The model asymptotic analysis, observability, controllability and fast and slow dynamic study are carried out. The latter, based on the Tikhonov's theorem, reveals the possibility to simplify model dynamics by decoupling the process in three time scales, i.e. long-term fouling evolution (slow dynamic), biological degradation (fast dynamic) and fouling cake formation (ultrafast dynamic). As sMBR processes are relativity new, real process data are scarce. Thus, a recirculating aquaculture system pilot plant with an sMBR is design, build and automated. Process online measurements such as: temperature, total suspended solids (TSS), ammonia and nitrate effluent concentrations, air cross- and effluent flow rates and trans-membrane pressure are gathered in other to validate the proposed model. To evidence the model general framework the same model is confronted with real data sets obtained from an sMBR wastewater treatment plant. Therefore, a parameter identification is organized in three steps corresponding to the three time scales obtained from the analytical analysis. The parameter identification is implemented using a weighted least-squares cost function and the inverse of the Fisher Information Matrix (FIM), which is used to obtain the parameters confidence intervals, is computed by a lower bound on the covariance matrix of the parameter estimates. The model capacity to predict trans-membrane pressure and biological degradation is proved by model validation and cross-validation results, in which an accurate correlation coefficients (R^2) of approximately 0.83 are obtained. Concerning the process control, two different approach are used: a partial-linearizing feedback Lyapunov controller is designed in order to stabilize the fouling production by actuating in the air cross- and effluent flows; and a nonlinear model predictive control (NMPC) is implemented in other to optimize the effluent production rate and maximize the period between two chemical cleaning procedures. The results included in this thesis show the importance of analytical model studies in order to process cognition and model simplification. Another important point is the simple dynamic model structure, with a small quantity of the parameters, which is adequate to implement advanced control strategies on sMBR processes and, similarly, to predict biological degradation and fouling build-up dynamics.Cette thèse propose un modèle dynamique d'un bioréacteur à membrane submergée (sMBR) comprenant les comportements physiques et biologiques du processus. La filtration (aspect physique) est un modèle de résistances en série composé de la résistance réversible (liée au processus de formation d'un gâteau qui peut être enlevé par lavage de l'air) et de la résistance à colmatage irréversible. La fonction biologique est mise en œuvre par l'extension du modèle de chemostat simple avec un mécanisme de filtration.L'analyse du modèle comprend : l'analyse asymptotique, l'observabilité, la contrôlabilité et l'étude dynamique lente et rapide. Cette dernière, basée sur le théorème de Tikhonov, révèle la possibilité de simplifier la dynamique du modèle en découplant le processus en trois échelles de temps : l'évolution du colmatage à long terme (dynamique lente), la dégradation biologique ( dynamique rapide) et la formation du gâteau (dynamique ultrarapide). Comme les processus avec sMBRs sont relativement nouveaux, les données réelles de processus sont difficiles à obtenir. Ainsi, une installation pilote d'un système de recirculation de l'aquaculture avec une sMBR est conçue, construite et automatisée. Des mesures en ligne du processus, tels que la température, les matières en suspension (MES), l'ammoniac et les concentrations des effluents nitrates, la croisée de l'air et des débits d'effluents et la pression transmembranaire, sont réunis afin de valider le modèle proposé.Pour mettre en évidence le cadre général du modèle proposé, le même modèle est composé d'ensembles de données réelles obtenues à partir d'une installation de traitement des eaux usées à sMBR. Par conséquent, une identification de paramètre est organisée en trois étapes correspondant aux trois échelles de temps obtenues à partir de l'analyse analytique. L'identification de paramètre est implémentée en utilisant une fonction de coût aux moindres carrés pondérés et l'inverse de la Fisher Matrix Information (FIM), qui est utilisé pour obtenir les intervalles de confiance des paramètres calculées par une borne inférieure sur la matrice de covariance des estimations des paramètres. La capacité du modèle à prédire la pression transmembranaire et la dégradation biologique est prouvée par la validation du modèle et la validation croisée des résultats.Concernant le contrôle du processus, deux approches différentes sont utilisées : un contrôleur partielle linéaire basé sur la théorie de Lyapunov est conçu afin de stabiliser la production encrassement en actionnant dans la croisée de l'air et les flux d'effluents; une commande prédictive de modèle non linéaire (NMPC) est mise en œuvre afin d'optimiser le taux de production d'effluent et de maximiser la période entre deux opérations de lessivage chimique.Les résultats présentés dans cette thèse montrent l'importance des études analytiques sur des modèles afin de traiter la cognition et la simplification de modèle. Un autre point important est la structure du modèle dynamique simple avec une petite quantité de paramètres. Ce travail montre que cette structure est suffisante pour mettre en œuvre des stratégies de contrôle avancé sur les processus sMBR et même de prédire la dégradation biologique et la dynamique de croissance du colmatage

Stabilizing the cake evolution for a class of submerged membrane bioreactors using a Lyapunov controller

International audienc

Time Scaling Study Using Tikhonov's Theorem in a Submerged Membrane Bioreactor

IAP DYSCO Study Day : Dynamical systems, control and optimization, Nov 2013, Bruxelles, Belgiu

Experimental validation of a simple dynamic model of a laboratory scale recirculating aquaculture system fitted with submerged membrane bioreactor

in pressInternational audienceSubmerged membrane bioreactors (sMBR's) are a promising technology for nitrogen removal in recirculating aquaculture systems (RAS's). However, there are still relatively few reports on the experimental application of this strategy. In this study, a laboratory-scale system, mimicking a RAS fitted with a sMBR, \rouge{was} designed and automated, and a simple dynamic sMBR model including biological and physical phenomena \rouge{was} validated. The system \blue{was} analyzed based on measurements collected by a data logging structure involving a programmable logic controller (PLC), an \rouge{industrial network protocol} and a LabView application software. This study confirms the \rouge{suitability} of sMBR systems \blue{within} aquaculture applications. The dynamic model has good predictive capabilities and could be used for the design of advanced control structures, such as model predictive control