Leakage localisation method in a water distribution system based on sensitivity matrix: methodology and real test

Leaks are present in all water distribution systems. In this paper a method for leakage detection and localisation is presented. It uses pressure measurements and simulation models. Leakage localisation methodology is based on pressure sensitivity matrix. Sensitivity is normalised and binarised using a common threshold for all nodes, so a signatures matrix is obtained. A pressure sensor optimal distribution methodology is developed too, but it is not used in the real test. To validate this methodology it has been tested with a real situation in two District Management Areas (DMA) in Barcelona. This real test only allows validating the localisation part of the methodology. Some installed sensors in these DMA have been used. For one of these DMA historical data of a leakage period is used. In the other one a leakage has been forced.Peer ReviewedPostprint (author’s final draft

Data Validation and reconstruction for performance enhancement and maintenance of water networks

In a real water network, a telecontrol system must periodically acquire, store and validate data gathered by sensor measurements in order to achieve accurate monitoring of the whole network in real time. For each sensor measurement, data are usually represented by one-dimensional time series. These values, known as raw data, need to be validated before further use to assure the reliability of the results obtained when using them. In real operation, problems affecting the communication system, lack of reliability of sensors, or other inherent errors often arise, generating missing or false data during certain periods of time. These wrong data must be detected and replaced by estimated data. Thus, it is important to provide the data system with procedures that can detect such problems and assist the user in monitoring and processing the incoming data. Data validation is an essential step to improve data reliability. The validated data represent measurements of the variables in the required form where unnecessary information from raw data has been removed. In this paper, a methodology for data validation and reconstruction of sensor data in a water network is used to analyze the performance of the sectors of a water network. Finally, from this analysis several indicators of the components (sensors, actuators and pipes) and of the sectors themselves can be derived in order to organize useful plans for performance enhancement and maintenance. Nice practices have been developed during a large period in the water network of the company ATLL Concessionària de la Generalitat de Catalunya, S.A.Postprint (author's final draft

Ensemble model-based method for time series sensors’ data validation and imputation applied to a real waste water treatment plant

Intelligent Decision Support Systems (IDSSs) integrate different Artificial Intelligence (AI) techniques with the aim of taking or supporting human-like decisions. To this end, these techniques are based on the available data from the target process. This implies that invalid or missing data could trigger incorrect decisions and therefore, undesirable situations in the supervised process. This is even more important in environmental systems, which incorrect malfunction could jeopardise related ecosystems. In data-driven applications such as IDSS, data quality is a basal problem that should be addressed for the sake of the overall systems’ performance. In this paper, a data validation and imputation methodology for time-series is presented. This methodology is integrated in an IDSS software tool which generates suitable control set-points to control the process. The data validation and imputation approach presented here is focused on the imputation step, and it is based on an ensemble of different prediction models obtained for the sensors involved in the process. A Case-Based Reasoning (CBR) approach is used for data imputation, i.e., similar past situations to the current one can propose new values for the missing ones. The CBR model is complemented with other prediction models such as Auto Regressive (AR) models or Artificial Neural Network (ANN) models. Then, the different obtained predictions are ensembled to obtain a better prediction performance than the obtained by each individual prediction model separately. Furthermore, the use of a meta-prediction model, trained using the predictions of all individual models as inputs, is proposed and compared with other ensemble methods to validate its performance. Finally, this approach is illustrated in a real Waste Water Treatment Plant (WWTP) case study using one of the most relevant measures for the correct operation of the WWTPs IDSS, i.e., the ammonia sensor, and considering real faults, showing promising results with improved performance when using the ensemble approach presented here compared against the prediction obtained by each individual model separately.The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (published version

Application of CBR for intelligent process control of a WWTP

This paper proposes the use of a Case-Based Reasoning (CBR) system for the control and the supervision of a real wastewater treatment plant (WWTP). A WWTP is a critical system which aims to ensure the quality of the water discharged to the receiving bodies, stablished by applicable regulations. At the current stage the proposed methodology has been tested off-line on a real system for the control of the aeration process in the biological treatment of a WWTP within the ambit ofConsorci Besòs Tordera (CBT), a local water administration in the area of Barcelona. For this purpose, data mining methods are considered to extract the available knowledge from historical data to find a useful case base to be able to generate set-points for the local controllers in the WWTP. The results presented in this work are evaluated taking into account the performance of the CBR method e.g. case base size, CBR cycle time or number of cases resolved satisfactorily (forthcoming steps will include on-line tests). For this purpose, some Key Performance Indicators (KPI) are designed together with the plant manager and process experts, in order to monitor key parameters of the WWTP which are representative of the performance of the control and supervision system. Hence, these KPI are related with water quality regulations —e.g. ammonia concentration in the WWTP effluent— and the economic cost efficiency —e.g. electrical consumption of the installation. In order to evaluate the results, different flat-based memory organizations (i.e. cases are stored sequentially in a list) for the case base are considered. First, a unique case base is used. At the current stage and for the results shown in this work, this case base is divided in multiple libraries depending on a case classification. Finally, the combination of this approach with Rule-Based Reasoning (RBR) methods is proposed for the next stages of the work.The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

An interoperable workflow-based framework for the automation of building intelligent process control systems

One of the major problems to design and implement a control/supervision system for a process lies in the need to establish an ad-hoc system for each process installation. On the other side, an open challenge related to the deployment of Intelligent Decision Support Systems (IDSSs) is the interoperability of the different methods used, in order to allow interaction and reuse of different data mining methods and the use of methods based on a model or an expert. Thus, this paper proposes the use of visual workflows, to enable the automation of the design task and the implementation of Intelligent Process Control Systems (IPCSs). The framework will allow the user to specify the design and control of a concrete process as well as the required data-driven and expert models using a graphical workflow environment. The framework is based on a three-layer architecture: first, a comprehensive data science flow description layer (dataflow layer) to produce/discover data-driven models from process data; second, a flowchart of the different components of the process (process-design flow layer) to obtain a simulation model from the design. Finally, the on-line IPCS (process control workflow layer), where the different data-driven models, expert-based models and intelligent reasoning methods interoperate to control and supervise the process. Thus, the resulting system can automatically generate both simulation models of the process and programming code to control and supervise the process, using workflows designed for each particular installation. The case study is focused on the supervision of a Wastewater Treatment Plant (WWTP) located in the Barcelona region.The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

Knowledge extraction from raw data in water networks: application to the Barcelona supramunicipal water transport network

Critical Infrastructure Systems (CIS) such as the case of potable water transport network are complex large-scale systems, geographically distributed and decentralized with a hierarchical structure, requiring highly sophisticated supervisory and real-time control (RTC) schemes to ensure high performance achievement and maintenance when conditions are non-favorable due to e.g. sensor malfunctions (drifts, offsets, problems of batteries, communications problems,...). Once the data are reliable, a process to transform these validated data into useful information and knowledge is key for the operating plan in real time (RTC). And moreover, but no less important, it allows extracting useful knowledge about the assets and instrumentation (sectors of pipes and reservoirs, flowmeters, level sensors, ...) of the network for short, medium and large term management plans. In this work, an overall analysis of the results of the application of a methodology for sensor data validation/reconstruction to the ATLL water network in the city of Barcelona and the surrounding metropolitan area since 2008 until 2013 is described. This methodology is very important for assessing the economic and hydraulic efficiency of the network.Peer ReviewedPostprint (published version

Leakage isolation using pressure sensitivity analysis in water distribution networks: Application to the Barcelona case study

Leaks are present to some extent in all water-distribution systems. This paper proposes a leakage localisation method based on the pressure measurements and pressure sensitivity analysis of nodes in a network. The sensitivity analysis using analytical tools is not a trivial job in a real network because the huge non-explicit non-línear systems of equation that describe its dynamics. Simulations of the network in presence and absence of leakage may provide an approximation of this sensitivity. This matrix is binarised using a threshold independent of the node. The binary matrix is assumed as a signature matrix for leakages. However, there is a trade-off between the resolution of the leakage isolation procedure and the number of available pressure sensors. In order to maximise the isolability with a reasonable number of sensors, an optimal sensor placement methodology, based on genetic algorithms, is also proposed. This methodology has been developed for Barcelona Network using Piccolo simulator. The sensor placement and the leakage detection and localization methodologies are applied to district management areas (DMA).Peer ReviewedPostprint (published version

Herramienta basada en minería de datos para la automatización del diseño de sistemas inteligentes en EDAR

Uno de los principales problemas para diseñar e implementar un sistema de supervisión y control para un proceso radica en la necesidad de establecer una solución ad-hoc para cada instalación. La interoperabilidad de los diferentes métodos utilizados para este fin es uno de los desafíos actuales relacionados con el desarrollo de Sistemas Inteligentes de Soporte a la Toma de Decisiones (IDSS), con el objetivo de garantizar la interacción y reutilización de los diferentes métodos basados en modelos, en conocimiento experto o en minería de datos. En este trabajo se propone el uso de entornos y flujos de trabajo visuales para permitir la automatización del diseño e implementación de Sistemas Inteligentes de Control de Procesos (IPCS). Estos entornos permitirán al usuario especificar las características de un proceso concreto, así como los modelos requeridos —basados en datos y en conocimiento experto—, utilizando un entorno de desarrollo visual, con la finalidad de implementar la estrategia de control más adecuada a cada instalación particular. La herramienta propuesta se basa en una arquitectura de tres capas: la primera se corresponde a un proceso offline de generación de modelos e.g. data-driven a partir de datos históricos del sistema, con la finalidad de supervisarlo y controlarlo. La segunda se corresponde a un diagrama de flujo del sistema, incluyendo los distintos subprocesos que lo configuran y las señales correspondientes. Finalmente, la tercera capa es el núcleo de la aplicación, en la que se utilizan los modelos obtenidos por parte de los diferentes métodos de razonamiento inteligente, usados para supervisar el sistema, así como para generar las consignas de los actuadores. Así, a partir de la arquitectura propuesta se podrá generar automáticamente el diseño final para el control y supervisión del proceso. La naturaleza visual de la solución propuesta permite utilizar el propio flujo de control como interfaz gráfica de usuario, pudiéndose añadir distintos parámetros configurables por el usuario, así como indicadores clave de rendimiento (en inglés, KPI), útiles para dar soporte a las decisiones relacionadas con el sistema. El método presentado es genérico, pudiéndose implementar en aplicaciones de distinta tipología a la presentada en este trabajo, siendo la evolución natural el escalado a sistemas reales más complejos, aprovechando las ventajas que proporciona la generalidad de la solución propuesta para adaptar el método a otras instalaciones/aplicaciones. Finalmente se muestran los resultados obtenidos con un prototipo probado en una EDAR en el ámbito del Consorci Besos Tordera (CBT), para el control de una de las variables del proceso biológico.Los autores agradecen el soporte en este trabajo del Programa de Doctorado Industrial (2017-DI-006) y de los Grupos/Centros de Investigación Consolidados (2017 SGR 574) por la Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) de la Generalitat de Catalunya.Postprint (published version

Economic linear parameter varying model predictive control of the aeration system of a wastewater treatment plant

This work proposes an economic model predictive control (EMPC) strategy in the linear parameter varying (LPV) framework for the control of dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant (WWTP). A reduced model of the complex nonlinear plant is represented in a quasi-linear parameter varying (qLPV) form to reduce computational burden, enabling the real-time operation. To facilitate the formulation of the time-varying parameters which are functions of system states, as well as for feedback control purposes, a moving horizon estimator (MHE) that uses the qLPV WWTP model is proposed. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. The obtained results applying the EMPC strategy for the control of the aeration system in the WWTP of Girona (Spain) show its effectiveness.This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020- 114244RB-I00), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020 (ref. 001-P-001643 Looming Factory), and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482).Peer ReviewedPostprint (author's final draft

Leakage localisation method in a water distribution system based on sensitivity matrix: methodology and real test

Leaks are present in all water distribution systems. In this paper a method for leakage detection and localisation is presented. It uses pressure measurements and simulation models. Leakage localisation methodology is based on pressure sensitivity matrix. Sensitivity is normalised and binarised using a common threshold for all nodes, so a signatures matrix is obtained. A pressure sensor optimal distribution methodology is developed too, but it is not used in the real test. To validate this methodology it has been tested with a real situation in two District Management Areas (DMA) in Barcelona. This real test only allows validating the localisation part of the methodology. Some installed sensors in these DMA have been used. For one of these DMA historical data of a leakage period is used. In the other one a leakage has been forced.Peer Reviewe