Barcelona Water Distribution Network Research Articles

Non targeted screening of nitrogen containing disinfection by-products in formation potential tests of river water and subsequent monitoring in tap water samples.

The generation of disinfection by-products during water chlorination is a major concern in water treatment, given the potential health risks that these substances may pose. In particular, nitrogen-containing DBPs are believed to have greater toxicological significance than carbon-based DBPs. Hence, high performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS) in positive mode was employed to identify new non-volatile nitrogen containing disinfection by-products (DBPs) and to assess their presence in potable water. Nine water samples were taken in the Llobregat river, in the context of a water reuse trial, near the catchment of a drinking water treatment plant (DWTP) in 2019. River samples were disinfected with chlorine under controlled formation potential tests conditions and analysed with a non-target approach. The peak lists of raw and chlorinated samples were compared exhaustively, resulting in an extensive list of 495 DBPs that include bromine and/or chlorine atoms. 172 of these species were found frequently, in three or more chlorinated samples. The empirical formulae of these DBPs were unambiguously annotated on the basis of accurate m/z measurements, isotopic patterns and common heuristic rules. Most of the annotated species (310) contained bromide, which is consistent with the relatively high bromide content of the Llobregat basin (>0.3mg/l). Drinking water samples were taken at the outlet of the DWTP during the same sampling period. According to their analysis, a large portion of the DBPs detected after the formation potential tests do not reach real-life drinking water, which suggests that the treatment train successfully removes a significant fraction of DBP precursors. However, 131 DBPs could still be detected in the final product water. A larger sampling was carried in the Barcelona water distribution network, during six consecutive weeks, and it revealed the presence of 78 halogenated DBPs in end-consumer water, most of which were nitrogen-containing. MS/MS fragmentation and retention times were employed to tentatively suggest molecular structure for these recalcitrant DBPs.

Multi-Model Prediction for Demand Forecast in Water Distribution Networks

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

Optimal Management of Barcelona Water Distribution Network using Non-linear Model Predictive Control * *This work has been partially funded by the European Union through the project EFFINET (ref. FP7-ICT-2011-8-318556), by the Spanish Government and FEDER through the projects CI-CYT ECOCIS (ref. DPI2013-48243-C2-1-R), CICYT DEOCS (ref. DPI2016-76493-C3-3-R) and CICYT HARCRICS (ref. DPI2014-58104-R).

Abstract This paper presents a non-linear optimal control strategy for the operational management of water distribution networks (WDNs) including both flow and hydraulic head/pressure constraints. The optimal operation of WDNs should guarantee water supply with suitable pressures at all the demand nodes in the network. The challenge for non-linear model predictive control in this context is to compute control strategies for the pumps and valves in a WDN to supply the required demand while optimizing performance goals related to cost and safety. A two-layer scheme is used in order to produce set-points that can be directly sent to the actuators: on-off schedules for pumps and pressure set-points for pressures reducing valves. Finally, the results of applying the proposed control strategy to a portion of the Barcelona real WDN are provided.

Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network

Water distribution networks are large complex systems affected by leaks, which often entail high costs and may severely jeopardise the overall water distribution performance. Successful leak location is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak location process, since the overall performance and success of this process highly depends on the choice of the sensors gathering data from the network. Common problems when isolating leaks in large scale highly gridded real water distribution networks include leak mislabelling and the obtention of large number of possible leak locations. This is due to similarity of leak effect in the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions (e.g. geographically cluster alike leak behaviors) and by taking into account real aspects of the practical application, such as the acceptable leak location distance. In this paper, a sensor placement methodology considering these aspects and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented and exemplified with a small illustrative case study. Finally, the proposed method is applied to two real District Metered Areas (DMAs) located within the Barcelona water distribution network.

Robust sensor placement for leak location: analysis and design

In this paper, a nominal sensor placement methodology for leak location in water distribution networks is presented. To reduce the size and the complexity of the optimization problem a clustering technique is combined with the nominal sensor placement methodology. Some of the pressure sensor placement methods for leak detection and location in water distribution networks are based on the pressure sensitivity matrix analysis. This matrix depends on the network demands, which are nondeterministic, and the leak magnitudes, that are unknown. The robustness of the nominal sensor placement methodology is investigated against the fault sensitivity matrix uncertainty. Providing upon the dependency of the leak location procedure on the network operating point, the nominal sensor placement problem is then reformulated as a multi-objective optimization for which Pareto optimal solutions are generated. The robustness study as well as the resulting robust sensor placement methodology are illustrated by means of a small academic network as well as a district metered area in the Barcelona water distribution network.

Optimal Pressure Sensor Placement for Leak Localisation Using a Relaxed Isolation Index: Application to the Barcelona Water Network

Water distribution networks are large complex systems that are affected by leaks, which often entail high costs and may severely jeopardize the overall water distribution performance. Successful leak localisation is paramount in order to minimize the impact of these leaks when occurring. Sensor placement is a key issue in the leak localisation process, since the overall performance and success of the leak isolation method highly depends on the choice of the sensors gathering data from the network. Common problems when leak isolating in large scale highly-gridded real water distribution networks include leak mislabelling and large isolation areas obtention due to similarity of the measurements, which may be caused by topological issues and led to incomplete coverage of the whole network. The sensor placement strategy may minimize these undesired effects by setting the sensor placement optimisation problem with the appropriate assumptions e.g. geographically cluster alike leak behaviors, taking into account real aspects of the practical application such as the acceptable isolation distance. Here, a sensor placement methodology considering these assets and a general sensor distribution assessment method for leak diagnosis in water distribution systems is presented for a real District Metered Area (DMA) located within the Barcelona water distribution network.

Optimal Pressure Sensor Placement in Water Distribution Networks Minimizing Leak Location Uncertainty

In this paper an optimal sensor placement strategy based on pressure sensitivity matrix analysis and an exhaustive search strategy that maximizes some diagnosis specifications for a water distribution network is presented. An average worst leak expansion distance as a new leak location performance measure has been proposed. This metric is later used to assess the leak location uncertainty provided by a sensor configuration. The method is combined with a clustering technique in order to reduce the size and the complexity of the sensor placement problem. The strategy is successfully applied to determine the location of a set of pressure sensors in a district metered area in the Barcelona water distribution network.

Fault Identification in Distributed Sensor Networks Based on Universal Probabilistic Modeling.

This paper proposes a holistic modeling scheme for fault identification in distributed sensor networks. The proposed scheme is based on modeling the relationship between two datastreams by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems, which estimate the specific relationship over consecutive time windows. Every system state, including the nominal one, is represented by an HMM and the novel data are categorized according to the model producing the highest likelihood. The system is able to understand whether the novel data belong to the fault dictionary, are fault-free, or represent a new fault type. We extensively evaluated the discrimination capabilities of the proposed approach and contrasted it with a multilayer perceptron using data coming from the Barcelona water distribution network. Nine system states are present in the dataset and the recognition rates are provided in the confusion matrix form.

Des plexus sacrés bien trop visible au PET-scanner

In this paper we consider periodic optimal operation of constrained periodic linear systems. We propose an economic model predictive controller based on a single layer that unites dynamic real time optimization and control. The proposed controller guarantees closed-loop convergence to the optimal periodic trajectory that minimizes the average operation cost for a given economic criterion. A priori calculation of the optimal trajectory is not required and if the economic cost function is changed, recursive feasibility and convergence to the new periodic optimal trajectory is guaranteed. The results are demonstrated with two simulation examples, a four tank system, and a simplified model of a section of Barcelona's water distribution network.

Single-layer economic model predictive control for periodic operation

In this paper we consider periodic optimal operation of constrained periodic linear systems. We propose an economic model predictive controller based on a single layer that unites dynamic real time optimization and control. The proposed controller guarantees closed-loop convergence to the optimal periodic trajectory that minimizes the average operation cost for a given economic criterion. A priori calculation of the optimal trajectory is not required and if the economic cost function is changed, recursive feasibility and convergence to the new periodic optimal trajectory is guaranteed. The results are demonstrated with two simulation examples, a four tank system, and a simplified model of a section of Barcelona's water distribution network.

Sensor placement for leak detection and location in water distribution networks

The performance of a leak detection and location algorithm depends on the set of measurements that are available in the network. This work presents an optimization strategy that maximizes the leak diagnosability performance of the network. The goal is to characterize and determine a sensor configuration that guarantees a maximum degree of diagnosability while the sensor configuration cost satisfies a budgetary constraint. To efficiently handle the complexity of the distribution network an efficient branch and bound search strategy based on a structural model is used. However, in order to reduce even more the size and the complexity of the problem the present work proposes to combine this methodology with clustering techniques. The strategy developed in this work is successfully applied to determine the optimal set of pressure sensors that should be installed in a District Metered Area in the Barcelona water distribution network.

Robustness Analysis of Sensor Placement for Leak Detection and Location under Uncertain Operating Conditions

Some pressure sensor placement methods for leak detection and location in water distribution networks are based on the pressure sensitivity matrix analysis. This matrix depends on the network demands, which are nondeterministic, and the leak magnitudes, which are unknown. In this paper, the robustness of a sensor placement methodology against the fault sensitivity matrix uncertainty is studied. The robustness study is illustrated by means of a small academic network as well as a district metered area in the Barcelona water distribution network. Results reveal that this uncertainty should be taken into account in the sensor placement methodology.

Learning in the model space for cognitive fault diagnosis.

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.

Optimal Sensor Placement for Leakage Detection and Isolation in Water Distribution Networks

In this paper, the problem of leakage detection and isolation in water distribution networks is addressed applying an optimal sensor placement methodology. The chosen technique is based on structural models and thus it is suitable to handle non-linear and large scale systems. A drawback of this technique arises when costs are assigned uniformly. A main contribution of this paper is the proposal of an iterative methodology that focuses on identifying essential sensors which ultimately leads to an improvement of the optimal search efficiency. The algorithm presented in this work is successfully applied to a District Metered Area (DMA) in the Barcelona water distribution network.

Methodology to Detect and Isolate Water Losses in Water Hydraulic Networks: Application to Barcelona Water Network

This paper presents a methodology to detect real water losses in urban water networks. The proposed method is based on the combined use of the DMA economical water balance confidence intervals for a period of time longer than a month (a trimester, semester or year) with appropriated night flow ratio intervals. This method allows distinguishing between real and apparent water losses in a given DMA. The use of uncertainty models allows taking into account measurement uncertainty of the flow data sensors. The use of different periods of time allows the stabilization of the results. This methodology is illustrated in a DMA of the Barcelona water distribution network using real data.

Application of Distributed MPC to Barcelona Water Distribution Network

AbstractThe paper presents application of Distributed Model Predictive Control (DMPC) schemes to complex system of Barcelona water distribution network. The dual decomposition of convex optimization problems is well known and has been already adopted to DMPC. However, the application of dual based DMPC to truly large scale systems requires efficient algorithms for consensus iterations. The paper treats DMPC with and without centralized coordinator. The non-centralized coordination is based on Nesterov accelerated gradient method and centralized coordination is based on limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method.

Validation and reconstruction of flow meter data in the Barcelona water distribution network

This paper presents a signal analysis methodology to validate (detect) and reconstruct the missing and false data of a large set of flow meters in the telecontrol system of a water distribution network. The proposed methodology is based on two time-scale forecasting models: a daily model based on a ARIMA time series, while the 10-min model is based on distributing the daily flow using a 10-min demand pattern. The demand patterns have been determined using two methods: correlation analysis and an unsupervised fuzzy logic classification, named LAMDA algorithm. Finally, the proposed methodology has been applied to the Barcelona water distribution network, providing very good results.

Pressure sensor distribution for leak detection in Barcelona water distribution network

This paper proposes a leakage detection method based on detecting significant discrepancies between pressure measurements and their estimations obtained from the simulation of a calibrated water distribution network model. Every sensor in the network will allow to detect a discrepancy in pressure due to leakage depending on its location. Then, a set of well distributed pressure sensors will generate a leakage signature that allows leakage localisation. This paper presents the methodology used in the Barcelona network for distributing properly the sensors for a good discrimination in the leakage localisation process. The methodology for sensor placement uses the pressure sensitivity matrix to the leakage presence. This matrix is normalised and binarised in order to be used as a leakage signature matrix using the standard model based fault diagnosis approach. Sensors may be installed in any node and leakages are simulated as a constant demand that can appear in any node too. The problem of deciding which are the best localisations for a small number of sensors in order to detect and localise leakages is an inverse problem that should be solved using optimisation. The resulting optimisation problem is of discrete nature and very huge for a real network. This type of problem is, in general, hard to solve and very time consuming. The use of GA (Genetic Algorithms) has been proved adequate according to the formulation of the signatures in the sensitivity matrix.

Stripping analysis of heavy metals in tap water using the bismuth film electrode

A commercially available screen-printed carbon electrode coated with an ex situ deposited bismuth film (BiSPCE) has been applied to the determination of Pb(II) and Zn(II) ions in tap water (Barcelona water distribution network) by means of stripping voltammetry (SV) and stripping chronopotentiometry (SCP). A good reproducibility of the measurements and a satisfactory agreement between SV and SCP data were observed for both heavy metal ions. Although, in principle, the procedure could be also suited to the determination of Cd(II), this species was not detected. The results were also consistent with the routine ICP-OES measurements of the water distribution company, thus confirming the potential usefulness of such BiSPCE disposable devices for the analysis of heavy metals in natural waters.

ARIMA Models for Data Consistency of Flowmeters in Water Distribution Networks

This paper presents a signal analysis methodology to check the coherence of the flowmeters data in a water distribution network telecontrol system. The proposed methodology is based on a new daily ARIMA forecasting model. Finally, the proposed methodology has been applied to the Barcelona water distribution network providing very good results by comparison with other ARIMA forecasting models proposed previously in Quevedo et al. (2006).