1,884 research outputs found

    Model predictive control and moving horizon estimation for water level regulation in inland waterways

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    This work regards the design of optimization techniques for the purposes of state estimation and control in the framework of inland waterways, often characterized by negligible bottom slopes and large time delays. The derived control-oriented model allows these issues to be handled in a suitable manner. Then, the analogous moving horizon estimation and model predictive control techniques are applied in a centralized manner to estimate the unmeasurable states and fulfill the operational goals, respectively. Finally, the performance of the methodology is tested in simulation by means of a realistic case study based on part of the inland waterways in the north of France. The results show that the proposed methodology is able to guarantee the navigability condition, as well as the other operational goals.Peer ReviewedPostprint (author's final draft

    Curses, Tradeoffs, and Scalable Management:Advancing Evolutionary Multiobjective Direct Policy Search to Improve Water Reservoir Operations

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    Optimal management policies for water reservoir operation are generally designed via stochastic dynamic programming (SDP). Yet, the adoption of SDP in complex real-world problems is challenged by the three curses of dimensionality, modeling, and multiple objectives. These three curses considerably limit SDP’s practical application. Alternatively, this study focuses on the use of evolutionary multiobjective direct policy search (EMODPS), a simulation-based optimization approach that combines direct policy search, nonlinear approximating networks, and multiobjective evolutionary algorithms to design Pareto-approximate closed-loop operating policies for multipurpose water reservoirs. This analysis explores the technical and practical implications of using EMODPS through a careful diagnostic assessment of the effectiveness and reliability of the overall EMODPS solution design as well as of the resulting Pareto-approximate operating policies. The EMODPS approach is evaluated using the multipurpose Hoa Binh water reservoir in Vietnam, where water operators are seeking to balance the conflicting objectives of maximizing hydropower production and minimizing flood risks. A key choice in the EMODPS approach is the selection of alternative formulations for flexibly representing reservoir operating policies. This study distinguishes between the relative performance of two widely-used nonlinear approximating networks, namely artificial neural networks (ANNs) and radial basis functions (RBFs). The results show that RBF solutions are more effective than ANN ones in designing Pareto approximate policies for the Hoa Binh reservoir. Given the approximate nature of EMODPS, the diagnostic benchmarking uses SDP to evaluate the overall quality of the attained Pareto-approximate results. Although the Hoa Binh test case’s relative simplicity should maximize the potential value of SDP, the results demonstrate that EMODPS successfully dominates the solutions derived via SDP

    A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

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    This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.Peer ReviewedPostprint (author's final draft

    Output-feedback model predictive control of sewer networks through moving horizon estimation

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    Based on a simplified control-oriented hybrid linear delayed model, model predictive control (MPC) of a sewer network designed to reduce pollution during heavy rain events is presented. The lack of measurements at many parts of the system to update the initial conditions of the optimal control problems (OCPs) leads to the need for estimation techniques. A simple modification of the OCP used in the MPC iterations allows to formulate a state estimation problem (SEP) to reconstruct the full system state from a few measurements. Results comparing the system performance under the MPC controller using full-state measurements and a moving horizon estimation (MHE) strategy solving a finite horizon SEP at each time instant are presented. Closed-loop simulations are performed by using a detailed physically-based model of the network as virtual reality.Peer ReviewedPostprint (author’s final draft

    Numerical simulation of flooding from multiple sources using adaptive anisotropic unstructured meshes and machine learning methods

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    Over the past few decades, urban floods have been gaining more attention due to their increase in frequency. To provide reliable flooding predictions in urban areas, various numerical models have been developed to perform high-resolution flood simulations. However, the use of high-resolution meshes across the whole computational domain causes a high computational burden. In this thesis, a 2D control-volume and finite-element (DCV-FEM) flood model using adaptive unstructured mesh technology has been developed. This adaptive unstructured mesh technique enables meshes to be adapted optimally in time and space in response to the evolving flow features, thus providing sufficient mesh resolution where and when it is required. It has the advantage of capturing the details of local flows and wetting and drying front while reducing the computational cost. Complex topographic features are represented accurately during the flooding process. This adaptive unstructured mesh technique can dynamically modify (both, coarsening and refining the mesh) and adapt the mesh to achieve a desired precision, thus better capturing transient and complex flow dynamics as the flow evolves. A flooding event that happened in 2002 in Glasgow, Scotland, United Kingdom has been simulated to demonstrate the capability of the adaptive unstructured mesh flooding model. The simulations have been performed using both fixed and adaptive unstructured meshes, and then results have been compared with those published 2D and 3D results. The presented method shows that the 2D adaptive mesh model provides accurate results while having a low computational cost. The above adaptive mesh flooding model (named as Floodity) has been further developed by introducing (1) an anisotropic dynamic mesh optimization technique (anisotropic-DMO); (2) multiple flooding sources (extreme rainfall and sea-level events); and (3) a unique combination of anisotropic-DMO and high-resolution Digital Terrain Model (DTM) data. It has been applied to a densely urbanized area within Greve, Denmark. Results from MIKE 21 FM are utilized to validate our model. To assess uncertainties in model predictions, sensitivity of flooding results to extreme sea levels, rainfall and mesh resolution has been undertaken. The use of anisotropic-DMO enables us to capture high resolution topographic features (buildings, rivers and streets) only where and when is needed, thus providing improved accurate flooding prediction while reducing the computational cost. It also allows us to better capture the evolving flow features (wetting-drying fronts). To provide real-time spatio-temporal flood predictions, an integrated long short-term memory (LSTM) and reduced order model (ROM) framework has been developed. This integrated LSTM-ROM has the capability of representing the spatio-temporal distribution of floods since it takes advantage of both ROM and LSTM. To reduce the dimensional size of large spatial datasets in LSTM, the proper orthogonal decomposition (POD) and singular value decomposition (SVD) approaches are introduced. The performance of the LSTM-ROM developed here has been evaluated using Okushiri tsunami as test cases. The results obtained from the LSTM-ROM have been compared with those from the full model (Fluidity). Promising results indicate that the use of LSTM-ROM can provide the flood prediction in seconds, enabling us to provide real-time flood prediction and inform the public in a timely manner, reducing injuries and fatalities. Additionally, data-driven optimal sensing for reconstruction (DOSR) and data assimilation (DA) have been further introduced to LSTM-ROM. This linkage between modelling and experimental data/observations allows us to minimize model errors and determine uncertainties, thus improving the accuracy of modelling. It should be noting that after we introduced the DA approach, the prediction errors are significantly reduced at time levels when an assimilation procedure is conducted, which illustrates the ability of DOSR-LSTM-DA to significantly improve the model performance. By using DOSR-LSTM-DA, the predictive horizon can be extended by 3 times of the initial horizon. More importantly, the online CPU cost of using DOSR-LSTM-DA is only 1/3 of the cost required by running the full model.Open Acces

    Flood Forecasting Using Machine Learning Methods

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    This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

    A review of applied methods in Europe for flood-frequency analysis in a changing environment

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    The report presents a review of methods used in Europe for trend analysis, climate change projections and non-stationary analysis of extreme precipitation and flood frequency. In addition, main findings of the analyses are presented, including a comparison of trend analysis results and climate change projections. Existing guidelines in Europe on design flood and design rainfall estimation that incorporate climate change are reviewed. The report concludes with a discussion of research needs on non-stationary frequency analysis for considering the effects of climate change and inclusion in design guidelines. Trend analyses are reported for 21 countries in Europe with results for extreme precipitation, extreme streamflow or both. A large number of national and regional trend studies have been carried out. Most studies are based on statistical methods applied to individual time series of extreme precipitation or extreme streamflow using the non-parametric Mann-Kendall trend test or regression analysis. Some studies have been reported that use field significance or regional consistency tests to analyse trends over larger areas. Some of the studies also include analysis of trend attribution. The studies reviewed indicate that there is some evidence of a general increase in extreme precipitation, whereas there are no clear indications of significant increasing trends at regional or national level of extreme streamflow. For some smaller regions increases in extreme streamflow are reported. Several studies from regions dominated by snowmelt-induced peak flows report decreases in extreme streamflow and earlier spring snowmelt peak flows. Climate change projections have been reported for 14 countries in Europe with results for extreme precipitation, extreme streamflow or both. The review shows various approaches for producing climate projections of extreme precipitation and flood frequency based on alternative climate forcing scenarios, climate projections from available global and regional climate models, methods for statistical downscaling and bias correction, and alternative hydrological models. A large number of the reported studies are based on an ensemble modelling approach that use several climate forcing scenarios and climate model projections in order to address the uncertainty on the projections of extreme precipitation and flood frequency. Some studies also include alternative statistical downscaling and bias correction methods and hydrological modelling approaches. Most studies reviewed indicate an increase in extreme precipitation under a future climate, which is consistent with the observed trend of extreme precipitation. Hydrological projections of peak flows and flood frequency show both positive and negative changes. Large increases in peak flows are reported for some catchments with rainfall-dominated peak flows, whereas a general decrease in flood magnitude and earlier spring floods are reported for catchments with snowmelt-dominated peak flows. The latter is consistent with the observed trends. The review of existing guidelines in Europe on design floods and design rainfalls shows that only few countries explicitly address climate change. These design guidelines are based on climate change adjustment factors to be applied to current design estimates and may depend on design return period and projection horizon. The review indicates a gap between the need for considering climate change impacts in design and actual published guidelines that incorporate climate change in extreme precipitation and flood frequency. Most of the studies reported are based on frequency analysis assuming stationary conditions in a certain time window (typically 30 years) representing current and future climate. There is a need for developing more consistent non-stationary frequency analysis methods that can account for the transient nature of a changing climate

    Modeling and real-time control of urban drainage systems: A review

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    Urban drainage systems (UDS) may be considered large-scale systems given their large number of associated states and decision actions, making challenging their real-time control (RTC) design. Moreover, the complexity of the dynamics of the UDS makes necessary the development of strategies for the control design. This paper reviews and discusses several techniques and strategies commonly used for the control of UDS. Moreover, the models to describe, simulate, and control the transport of wastewater in UDS are also reviewed.This work has been partially supported by Mexichem, Colombia through the project “Drenaje Urbano y Cambio Climático: Hacia los Sistemas de Alcantarillado del Futuro.” Fase II, with reference No. 548-2012, the scholarships of Colciencias No. 567-2012 and 647-2013, and the project ECOCIS (Ref. DPI2013-48243-C2-1-R).Peer Reviewe

    Hybrid modelling and receding horizon control of combined sewer networks

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    Thesis submitted for the degree of Doctor of Philosophy in the Universitat Politècnica de Catalunya; Departament d’Enginyeria de Sistemes, Automàtica i Informàtica Industrial. Programa de doctorat: Automàtica, Robòtica i Visió.-- Aquesta tesi ha estat realitzada a: Institut de Robòtica i Informàtica Industrial, CSIC-UPC.[EN]: Combined sewer networks carry wastewater and storm water together. During normal operation all the water is delivered to wastewater treatment plants, where it is treated before being released to surrounding natural water bodies. However, during heavy rain events, the network capacity may become insufficient leading to untreated water discharges to the receiving environments. To mitigate these undesired effects, combined sewer networks are usually provided with detention tanks and flow redirection elements, managed to fully take advantage of the network capacity. In the last few decades automatic control techniques for the regulation of these storage and redirection elements have been developed, with real-time, global, model-based predictive ones being widely regarded as the most efficient ones due to their capacity to take advantage of instantaneous network measurements and rain intensity forecasts. In this thesis a complete methodology to develop a real-time, global, model-based predictive controller to minimize pollution effects in combined sewer networks is proposed. The physically-based model for open-channel flow is based on a set of partial differential equations, which must be solved numerically. Since in a real-time predictive control strategy the model equations must be solved many times to evaluate the effect of different control actions, the time needed to solve the equations limits the use of the physically-based model to small network instances with simple topologies. Therefore, it is a common practice to use simplified control-oriented models for real-time control. The first part of the thesis is focused on the development, calibration and validation of a simplified control-oriented model for water transport in combined sewer networks, taking into account three main features: accuracy, calibration ease and computational speed. The proposed model describes the flows through the most common elements and hydraulic structures present in combined sewer networks, some of which requiring the use of piecewise equations. Once the model equations are presented, calibration procedures to compute all the model parameters are developed. The modelling and calibration methodology is then applied to a real case study and validation results are provided. Finally, sensitivity analysis is conducted with respect to both the most relevant model parameters and the intensity of the considered rain scenarios. The second part of the thesis is devoted to model-based optimal control. First, the piecewise equations of the model are reformulated to obtain a general expression of the system by means of a set of linear equations and inequalities including continuous and binary variables. Using this general expression, matrix-based procedures for the formulation of Optimal Control Problems and State Estimation Problems are presented. Using an implementation of the case study network in a commercial sewer network simulator solving the complete physically-based model equations as virtual reality, the proposed model-based controller is evaluated. By iteratively solving State Estimation Problems and Optimal Control Problems and using the simulator to provide network measurements, a Receding Horizon Control strategy is simulated. The inclusion of State Estimation Problems in the control loop allows to perform output feedback control simulations taking into account that in a sewer network the number of available measurements is limited. Finally, a discussion of the results obtained with these simulations corresponding to different measurement availability scenarios is provided.[CA]: Les xarxes de clavegueram combinades transporten conjuntament aigües residuals i aigües pluvials. En absència de pluges, tota l'aigua és conduïda cap a plantes de tractament on és degudament tractada abans de ser retornada als cossos aquàtics adjacents. En canvi, durant episodis de pluja intensa, la capacitat de la xarxa pot esdevenir insuficient donant lloc a inundacions en zones urbanes i abocaments d'aigua no tractada als medis receptors. Per tal de mitigar aquests efectes, les xarxes de clavegueram combinades acostumen a disposar de dipòsits de retenció i elements de redistribució del cabal, regulats amb la finalitat d'aprofitar al màxim la capacitat de la xarxa. En les últimes dècades s'han desenvolupat tècniques de control automàtic per a la regulació d'aquests elements d'emmagatzematge i redistribució, essent el control a temps real, global i predictiu basat en models la tècnica considerada més eficient, donat que és capaç de tenir en compte mesures instantànies del sistema i prediccions d'intensitat de pluja. En aquesta tesi, es proposa una metodologia completa per al desenvolupament d'un controlador a temps real, global i predictiu basat en model per minimitzar els efectes contaminants en xarxes de clavegueram combinades. El model físic que descriu els fluxos en canals oberts es basa en un sistema d'equacions en derivades parcials que s'ha de resoldre numèricament. Com que en una estratègia de control predictiu a temps real les equacions del model s'han de resoldre moltes vegades per avaluar els efectes de diferents accions de control, el temps necessari per resoldre les equacions limita l'ús del model físic a xarxes petites i amb topologies simples. Per tant, és una pràctica habitual utilitzar models simplificats orientats a control per al control a temps real. La primera part de la tesi es centra en el desenvolupament, calibratge i validació d'un model simplificat orientat a control del moviment de l'aigua en xarxes de clavegueram combinades, tenint en compte tres característiques principals: la precisió, la facilitat de calibratge i la velocitat computacional. El model presentat descriu el cabal a través dels elements i estructures hidràuliques més comunes en xarxes de clavegueram combinades, algunes de les quals requereixen l'ús de funcions definides a trossos. Una vegada les equacions del model han estat presentades, es desenvolupen procediments per al calibratge de tots els paràmetres del model. La metodologia de modelat i calibratge ésaleshores aplicada a un cas d'estudi corresponent a una xarxa de clavegueram real i es presenten resultats de validació. Finalment, es duu a terme una anàlisi de sensitivitat respecte als paràmetres més rellevants del model i respecte a la intensitat dels escenaris de pluja considerats. La segona part de la tesi està dedicada al control òptim basat en el model. En primer lloc, les equacions definides a trossos del model són reformulades per obtenir una expressió del sistema en termes d'un conjunt d'equacions i desigualtats lineals incloent variables contínues i binàries. Usant aquesta expressió general es presenta un procediment basat en matrius per a la formulació de problemes de Control Òptim i Estimació d'Estat. Mitjançant una implementació de la xarxa del cas d'estudi en un simulador comercial de xarxes de clavegueram que resol les equacions del model físic complet com a realitat virtual, s'avalua el controlador basat en model descrit anteriorment. Resolent iterativament problemes d'Estimació d'Estat i de Control Òptim i utilitzant el simulador per obtenir mesures, se simula una estratègia de control amb horitzó lliscant. La inclusió de problemes d'Estimació d'Estat en llaç de control permet la simulació del controlador amb output feedback, tenint en compte que el nombre de mesures disponibles en una xarxa de clavegueram és limitat. Finalment, es discuteixen els resultats obtinguts en aquestes simulacions corresponents a diferents escenaris de disponibilitat de mesures.Peer Reviewe
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