La gestion incorrecte des eaux usées peut entraîner des dommages importants sur les stations de traitement et sur le récepteur final (écosystème aquatique). Dans le passé, la gestion des eaux usées n'a pas retenu beaucoup d'attention de la part des différentes parties prenantes. Toutefois, récemment, le changement de modèle de gestion des eaux usées et des eaux pluviales, a évolué du simple contrôle sanitaire et des inondations, à une protection globale de l'environnement. Un aspect très important, dans la politique de gestion des systèmes d'assainissement, est de détecter et d'éliminer une intrusion illicite, qui peut être intentionnelle. Ce travail thèse de doctorat est constitué de deux parties principales. Dans la première partie les problèmes relatifs à l'identification d'une intrusion illicite dans un système d'assainissement ont été abordés, proposant une méthodologie d'identification de la source (IS). Dans la deuxième partie, différentes méthodologies innovantes ont été proposées pour trouver l'emplacement optimal d'un nombre limité de capteurs dans le système d'assainissement. Dans cette thèse, le ISest résolu grâce à un modèle de simulation-optimisation, combinant l'outil de simulation Storm Water Management Model (SWMM) avec un code d'optimisation basé sur un algorithme génétique (Galib). Ceci nécessite des mesures en ligne de certains capteurs placés sur le réseau. Le SWMM ne possède pas l'outil de programmation. Afin d'intégrer le simulateur SWMM à la méthodologie de IS automatisé proposée, un outil ad-hoc a été développé. Une procédure de présélection, basée sur le concept de la matrice de la pollution et compte tenu de la topologie des égouts, a été mis en œuvre pour réduire l'effort de calcul. La méthodologie IS a été testée sur deux réseaux différents. L'un est un réseau connu dans la littérature, extrait du manuel de SWMM, tandis que l'autre réseau est un sous-bassin versant du réseau d'assainissement de Massa Lubrense, village situé près de Naples, en Italie. Les résultats montrent que les procédures de présélection réduisent considérablement l'effort de calcul, avec un rôle crucial pour les grands systèmes. En enquêtant sur la performance de la méthodologie IS, sa sensibilité par rapport aux paramètres de l'algorithme génétique a été vérifiée. En outre, l'influence de l'incertitude des flux entrés et des erreurs de mesure sur les résultats ont été approfondi. Un autre problème fondamental, associé à la surveillance de la qualité de l'eau des égouts, est le placement optimal d'un nombre limité de capteurs pour la détection précoce d'une source illicite. Dans la thèse l'emplacement du capteur est exprimé avec un problème d'optimisation mono ou multi-objectif. Le SWMM est utilisé pour extraire les données de qualité de l'eau. Différentes formulations ont été proposées et testées. Tout d'abord, la Théorie de l'Information (TI) basée sur la méthodologie d'optimisation multi-objectif est présentée. La TI considère deux objectifs : l'entropie conjointe, le contenu de l'information dans un ensemble de capteurs, qui est maintenu aussi haut que possible ; la corrélation totale, une mesure de la redondance, qui est maintenue aussi faible que possible. Dans la seconde approche multi-objectifs le temps de détection doit être minimisé et la fiabilité qui doit être maximisée. Les deux cas, les problèmes multi-objectifs sont résolues en utilisant l'algorithme Non-Dominating Sorting Genetic Algorithm-II (NSGA-II). Comme troisième alternative, un outil d'optimisation mono-objectif (Greedy) a été testé. Les objectifs précédemment considérées sont utilisés avec différentes combinaisons. Le réseau d'assainissement de Massa Lubrense a été utilisé pour tester les performances des différentes procédures proposées. Une comparaison normalisée entre toutes les approches montre que l'approche basée sur Greedy pourrait être une alternative pratique pour l'optimisation des emplacements de capteurs dans les systèmes d'assainissement / Improper wastewater management could result in significant damage to the treatment plants and the final recipient aquatic ecosystem. In the past, wastewater management did not get much attention from different stakeholders. However, recently a paradigm shift of wastewater and storm water management is evolving from a simple sanitary and flood control, respectively, to a whole environmental protection function. A very important aspect of the sewer systems management policy is to detect and eliminate an illicit intrusion. This PhD research is consisting of two main pillars. In the first pillar, the issues regarding the identification of an illicit intrusion in a sewer system have been addressed, proposing a source identification (SI) methodology. In the second pillar, different innovative methodologies have been proposed to find the optimal placement of a limited number of sensors in the sewer system. In the thesis, the SI is solved through a simulation-optimization model, combining the hydraulic and quality simulation tool Storm Water Management Model (SWMM) with a genetic algorithm code (GALib) as an optimizer. It requires online measurements from some sensors placed on the network. The SWMM does not have the programmer's toolkit. To integrate the SWMM simulator with the proposed automated SI methodology, an ad-hoc toolkit has been developed. A pre-screening procedure, based on the pollution matrix concept and considering the topology of sewers, has been implemented to reduce the computational effort. The SI methodology has been tested on two different networks. One is a literature network taken from the SWMM example manual while the other is one sub-catchment of the real sewer network of Massa Lubrense, a town located near Naples, Italy. The results show that the pre-screening procedure reduces the computational effort significantly, and it has a crucial role in large systems. In investigating the performances of the SI methodology, its sensitivity respect to the genetic algorithm parameters has been verified. Moreover, the influence of the uncertainty of the inflows values and the measurement errors on the results have been investigated. Another core problem associated with the water quality monitoring of sewers is represented by the optimal placement of a limited number of sensors for the early detection of an illicit source. In the thesis, the sensor location is expressed as a single or multi-objective optimization problem and the SWMM is used to extract the water quality data. Different formulations have been proposed and tested. First, an Information Theory (IT) based multi-objective optimization methodology is presented. The IT approach considers two objectives: the Joint entropy, the information content of a set of sensors, which is kept as high as possible; the Total correlation, a measure of redundancy, which is kept as low as possible. In the second multi-objective approach Detection time, to be minimized, and Reliability, to be maximized, are considered. In both cases, the multi-objective problems are solved using the Non-Dominating Sorting Genetic Algorithm-II (NSGA-II). As a third alternative, a single objective Greedy based optimization tool has been tested. The previously considered objectives are also used with different combinations. The Massa Lubrense sewer network is used to test the performances of various proposed procedures. A normalized comparison among all approaches shows that the Greedy based approach could be a handy alternative for optimizing the sensor locations in sewer systems

This dissertation presents the results of the qualitative and quantitative modeling of the Arroio Cancela urban basin, having an area of 4.35 km², with the use of the Storm Water Management Model (SWMM5). The generation and routing of the runoff, base flows and the processes of accumulation and washoff of the total suspended sediments (TSS) and organic matter represented by biochemical oxygen demand (BOD5) were modeled. The package PEST (Parameter Estimator) was used in the calibrations of the most sensitive parameters of the SWMM5. Eight months of monitored rainfall and runoff data containing 34 rainfall events were calibrated. Calibration of the qualitative processes used 10 events containing monitored data with concentrations of TSS and BOD5. The validation of the average calibrated parameters was carried out over a period of three months, having 16 rainfall events, 4 of which containing monitored data of TSS and BOD5. The results indicate that the SWMM5 is more sensitive to parameters related with the impermeable areas of the basin. The parameters of the permeable areas were more sensitive in events of greater magnitude. The use of PEST proved to be valuable in optimizing the model, considering the speed at which the algorithm converges to a satisfactory solution. The runoff calibration of the events reached very good Nash-Sutcliffe efficiencies (ENS) (average of 0.92). For the continuous simulations, calculated ENS reached a value of 0.72. The average errors in the flow volume for both cases were less than 14%. The calibration of TSS reached an average ENS value of 0.56 and, for BOD5, an average ENS of -0.75, with a high dispersion of the washoff parameters for both pollutants. The runoff validation for events produced an average ENS equal to 0.47, calculated median equal to 0.87 and hydrograms with good reproduction of the shape of the observed data. The validation of the continuous series presented ENS equal to 0.74 and an underestimation of the flow volume equal to 7.7%. The validation of the quality processes resulted very poor ENS indexes, with deficiently representation of the variation of TSS and BOD5 concentrations. The results indicate that the use of the SWMM5 model coupled with the PEST calibrator can produce good results in the prediction of runoff events and continuous flow series. However, the representation of the qualitative processes require better initial parameter estimations for buildup and washoff, in addition to improvements in the calculation algorithm of buildup and washoff of pollutants. / Este trabalho apresenta os resultados da modelagem qualiquantitativa da bacia urbana do Arroio Cancela, possuindo 4,35 km² de área, com o uso do modelo Storm Water Management Model (SWMM5). Foram modelados os processos de geração e propagação do escoamento superficial e de base, além dos processos de acumulação e lavagem do total de sedimentos em suspensão (TSS) e da matéria orgânica representada pela demanda bioquímica de oxigênio (DBO5). O pacote de rotinas PEST (Parameter Estimator) foi empregado nas calibrações dos parâmetros mais sensíveis do SWMM5. Ao total, foram calibrados 8 meses de dados monitorados de chuva e vazão, contendo 34 eventos chuvosos. Na calibração dos processos qualitativos empregaram-se 10 eventos contendo estimativas da concentração do TSS e da DBO5. A validação da média dos parâmetros calibrados foi realizada num período de 3 meses, possuindo 16 eventos chuvosos, dos quais 4 contém dados monitorados do TSS e da DBO5. Os resultados indicam que o SWMM5 é mais sensível aos parâmetros relativos às áreas impermeáveis da bacia. Os parâmetros das áreas permeáveis foram mais sensíveis nos eventos de maior magnitude. O uso do PEST revelou-se de grande valia na otimização do modelo, tendo em vista a velocidade com que o algoritmo converge para uma solução satisfatória. A calibração por eventos do escoamento superficial atingiu índices de eficiência de Nash-Sutcliffe (ENS) muito bons (média de 0,92). Para a série contínua, o ENS calculado atingiu o valor de 0,72. Os erros médios no volume de vazão para ambos os casos foi inferior a 14%. A calibração do TSS atingiu ENS médio de 0,56 e, da DBO5, ENS médio de -0,75, com parâmetros de lavagem muito dispersos para ambos os poluentes. Na validação dos eventos, a média dos parâmetros calibrados produziu valores médios de ENS igual a 0,47, mediana calculada igual a 0,87 e hidrogramas com boa reprodução da forma da série observada. A validação da série contínua alcançou ENS igual a 0,74 e subestimativa no volume de vazão igual a 7,7%. A validação dos processos qualitativos foi muito deficiente, não sendo reproduzida satisfatoriamente a variação da concentração do TSS e da DBO5. Os resultados apontam que o uso do modelo SWMM5 acoplado ao calibrador PEST produz bons resultados na predição do escoamento superficial em eventos isolados e em séries contínuas. Todavia, a representação dos processos qualitativos requer melhores estimativas dos parâmetros iniciais de acumulação e lavagem, além do aperfeiçoamento do algoritmo de cálculo dos mesmos.