Uncertainty effect on leak localisation in a DMA

The leak localisation methodologies based on data and models are affected by both uncertainties in the model and in the measurements. This uncertainty should be quantified so that its effect on the localisation methods performance can be estimated. In this paper, a model-based leak localisation methodology is applied to a real District Metered Area using synthetic data. In the generation process of the data, uncertainty in demands is taken into account. This uncertainty was estimated so that it can justify the uncertainty observed in the real measurements. The leak localisation methodology consists, first, in generating the set of possible measurements, obtained by Monte Carlo Simulation under a certain leak assumption and considering uncertainty, and second, in falsifying sets of nodes using the correlation with a leak residual model in order to signal a set of possible leaky nodes. The assessment is done by means of generating the confusion matrix with a Monte Carlo approach.Peer ReviewedPostprint (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

Implementation of a decision support system for sewage sludge management

In this work, a decision support system (DSS) coupled with wastewater treatment plant (WWTP) simulator tool that uses a hierarchical set of key performance indicators (KPIs) to provide an assessment of the performance of WWTP systems is presented. An assessment of different Scenarios in a real WWTP case study, each consisting of a different set of sludge line technologies and derived combinations, was successfully conducted with the developed DSS–WWTP simulator, based on Scenario simulation and hierarchical KPI analysis. The test carried out on the selected WWTP showed that although thermal valorisation and thermal hydrolysis showed similar (the best) economic viability, the latter showed additional benefits, including synergies related to improving the thermal balance of the overall WWTP even when considering other technologies. On the other hand, biogas-upgrading technologies allowed reduction of emissions, but with higher costs and thermal demands. The usage of this tool may allow the development of proposals for technological priorities as a pathway to the transition to circular economy based on the management criteria of the correspondent sanitation system.This work is supported by DAM (Depuración de Aguas del Mediterráneo) and by the Industrial Doctorate Programme (ref. 2017-DI-048) of the Catalan Agency of University and Research Grants Management (AGAUR).Peer ReviewedPostprint (published version

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.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

Parameter uncertainty modelling in water distribution network models

The use of water distribution network (WDN) models is an extended practice [13]. Confidence on decisions taken upon such models depends highly on their accuracy [11]. The parameters uncertainty has to be defined in order to include it in the model. Some of the parameters in a network (e.g. pipes lengths and diameters) can be easily measured and their uncertainty can be calculated on a statistical basis [4]. Demands cannot be measured directly and they have to be estimated using other measurements [10][8]. The uncertainty in the measurements used for that estimation is propagated to the parameters [1]. Besides, demands have their own stochastic nature that induces uncertainty. This paper describes how the pressure measurements are used to infer the uncertainty model in demands for a real network. The real data are treated in order to avoid the effect of boundary conditions. An uncertainty model for demands is calculated to justify the observed behaviour of the measurements. Montecarlo simulations are used for the validation.Peer ReviewedPostprint (published version

Aplicación de herramientas avanzadas de soporte a la toma de decisiones en la digestión anaerobia de lodos

El Consorci Besòs – Tordera (CBT), en su labor constante de investigación, mejora y optimización del funcionamiento de sus Estaciones Depuradoras de Aguas Residuales (EDARs), ha desarrollado un sistema de diagnosis avanzada del proceso de digestión anaerobia en tiempo real. Se trata de un sistema basado en la monitorización on-line de los parámetros clave de la digestión anaerobia, procesado y análisis avanzado de los datos y diagnosis del estado del proceso anaerobio. Dos parámetros clave monitorizados y con mayor peso en el balance y diagnóstico del proceso son el valor de ácidos grasos y la alcalinidad. El sistema considerado en este trabajo detecta desviaciones en el proceso, identifica las posibles causas de las desviaciones y propone medidas de actuación preventivas/correctivas. El sistema consigue incrementar la robustez y fiabilidad del proceso de digestión anaerobia. En este trabajo se presenta su aplicación en un sistema de saneamiento real en el municipio de Granollers (Barcelona).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

The geographical context of wastewater treatment and reuse : A benchmarking analysis for Spanish Mediterranean Wastewater Treatment Plants

Research supportive of the paper was funded by the Spanish CICYT under grant CSO2015-65182-C2-1-P, by the Industrial Doctorate Programme (ref. 2017-DI-048) of the Catalan Agency of University and Research Grants Management (AGAUR) and by DAM (Depuración de Aguas del Mediterráneo).This paper examines wastewater treatment and reuse in Catalonia and Valencia through a benchmarking analysis of energy intensities of wastewater treatment plants (WWTPs) located in the coastal municipalities of both regions also involving comparison with average European data on energy use by these plants. The comparison of European and Spanish Mediterranean WWTPs indicates that small Mediterranean plants are less energy intensive than their European counterparts, while for larger plants (above 10, 000 m/day) the reverse is true. As to the comparison between Catalan and Valencian plants, the latter are generally smaller than the former, and also slightly more energy intensive. Regarding reuse, the geographical context would explain these differences in terms of the final destination of effluents treated in these plants. The important presence of irrigated agriculture in Valencia is responsible for the reuse of 45% of potentially reclaimed water while Catalonia, with a different socioterritorial reality, reuses less than 3% of the total effluent treated

Sensor placement for combined sewer system monitoring in the Besòs river basin

In this paper, a sensor placement methodology for sewer systems monitoring in order to measure direct discharge to the river during intense rainfall events is presented. During these events, Combined Sewer Overflows (CSOs) may occur, causing serious problems of contamination of the corresponding receiving waters. The current national regulation compels sewer systems’ managers to monitor and quantify direct discharge to these receiving waters, in order to track these events. Hence, the selection of the appropriate sensor set in order to monitor the critical outlets of the network is of paramount importance to adequately monitor CSOs and minimize their effect by using the information gathered from these measurements. Here, a methodology considering relevant characteristics of each potential monitoring point —e.g. number of discharges, volume discharged or percentage of polluted mass— is defined to select the final sensor set. The presented methodology is applied to three different combined sewer systems in the Besòs river basin nearby Barcelona city area in Catalonia (Spain), i.e. Granollers, La Llagosta and Montornès systems.Peer ReviewedPostprint (published version