Leak detection in a DMA, a real application of flow modelling

This paper presents a versatile methodology to calculate parameters that characterise the demand of a DMA. This parameters are used for the leak modelling so that a predictive model is built and trained with historical data and used to detect on-line new leaks so that the repair time can be reduced applying proper leak localisation techniques. This methodology has been programmed using R where the modelling packages available provide assortment of predictive models easily to implement. It has been integrated in a Data analysis tool in order to utilise the great amount of information coming continuously from the WDN. Once the methodology and the tool are described the results applied to a real DMA are presented. This work has been carried out by the fundació CTM Centre Tecnològic (CTM) collaborating with Research Center for Supervision, Safety and Automatic Control (CS2AC) within a research project of Aigües de Manresa.Peer ReviewedPostprint (published version

Model calibration and leakage assessment applied to a real Water Distribution Network

The use of water distribution network models depends highly on the confidence that the operators have on them. Models generated automatically from the Geographical Information System using the hydraulic equations integrated in a simulation model [7] are available in most of the water utilities. Once the first simulation results are compared with the available measurements, calibration is required [6]. This paper presents the process of adjusting the original network model of a village called Sant Joan de Vilatorrada network to fit the simulation results with the measurements. The adjustments in the model range from the macroalibration level to the microcalibration level [3]. The macrocalibration process is based on the analysis done by the engineers, and the conclusions are formalised for future use in other networks. Microcalibration is centred in setting the emitter coefficients using Genetic Algorithm optimisation. The results of the work include an adjusted model for decision taking, an assessment of the background leakage and a methodology to be applied in other parts of the network.Peer ReviewedPostprint (published version

Chlorine concentration modelling and supervision in water distribution systems

The quality of the drinking water distributed through the networks has become the main concern of most operators. This work focuses on one of the most important variables of the drinking water distribution networks (WDN) that use disinfection, chlorine. This powerful disinfectant must be dosed carefully in order to reduce disinfection byproducts (DBPs). The literature demonstrates researchers’ interest in modelling chlorine decay and using several different approaches. Nevertheless, the full-scale application of these models is far from being a reality in the supervision of water distribution networks. This paper combines the use of validated chlorine prediction models with an intensive study of a large amount of data and its influence on the model’s parameters. These parameters are estimated and validated using data coming from the Supervisory Control and Data Acquisition (SCADA) software, a full-scale water distribution system, and using off-line analytics. The result is a powerful methodology for calibrating a chlorine decay model on-line which coherently evolves over time along with the significant variables that influence it.Peer ReviewedPostprint (author's final draft

Flow Data Based DMA Characterisation

Data in water companies have been collected since sensors (level, pressure, flow, Temperature, chlorine concentration, etc.) have been gradually introduced. Supervision and control of processes were the first drivers of this introduction. In the last decade the evolution of sensors, communications, data management and data analysis suggest the revolution of industry 4.0 that has its counterpart in utilities 4.0. The first challenge of such revolution for assuming the utility is to adapt the cutting edge methodologies to the existing technologies in its system. This work focusses in the analysis of the existing sensors, in particular flow sensors, in order to use them intensively for a more intelligent water network management. Flow sensors are abundant in a water network. Their purposes are not unique and using the data, produced by themselves, can be helpful for characterising them. Once the sensors are characterised the data received from them can be more easily validated and reconstructed

Flow Data Based DMA Characterisation

Data in water companies have been collected since sensors (level, pressure, flow, Temperature, chlorine concentration, etc.) have been gradually introduced. Supervision and control of processes were the first drivers of this introduction. In the last decade the evolution of sensors, communications, data management and data analysis suggest the revolution of industry 4.0 that has its counterpart in utilities 4.0. The first challenge of such revolution for assuming the utility is to adapt the cutting edge methodologies to the existing technologies in its system. This work focusses in the analysis of the existing sensors, in particular flow sensors, in order to use them intensively for a more intelligent water network management. Flow sensors are abundant in a water network. Their purposes are not unique and using the data, produced by themselves, can be helpful for characterising them. Once the sensors are characterised the data received from them can be more easily validated and reconstructed.Postprint (published version

Modelling daily water consumption through potential curves. Disaggregating apparent and real losses

This is an Accepted Manuscript of an article published by Taylor & Francis in Urban Water Journal on 18 May 2020, available online: http://www.tandfonline.com/10.1080/1573062X.2020.1764594This paper presents a model, based on potential curves, that describes the behaviour of the inverse of the daily cumulated frequency of the flows provided to a District Metered Area (DMA). The model has two terms, the first corresponds to the variable consumption due to the aggregation of demand patterns of consumers. The evolution of this term presents periodic behaviours with annual and weekly frequency. An extreme drought episode that affected Catalunya, reduced this parameter 19%. A second term presents exponential behaviour in its evolution and includes the real leakage. The leakage disaggregation together with the billing information allows the estimation of the apparent loses, 14.89% in the case study. The difficulty of estimating the parameters in a potential model, a complex problem of optimization, is simplified by applying mathematical moments. Hence, daily parameters become a linear relation of the daily moments that allows their algebraic operation.This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the projects SCAV (ref. MINECO DPI2017-88403-R) and DEOCS (ref. MINECO DPI2016-76493) and AGAUR ACCIO RIS3CAT UTILITIES 4.0 – P4 MODEM. Data have been gently provided by the company Aigües de Manresa.Peer ReviewedPostprint (author's final draft

Long and short term demand forecast, a real application

Two of the main purposes of a water supply company are the operation of the network and its planning. One of the critical elements for the planning and the operation is the demand forecast. There are multiple methods for the short term. These tools are based on the analysis of historical data (daily, hourly or higher frequency) as an indicator of future flows due to a repetitive and cyclic behaviour of the consumers. The Autoregressive Integrated Moving Average (ARIMA) is one of the most straightforward approaches producing good results. Nevertheless, the demand forecasting is continuously evolving and new models are suggested like the fully adaptive forecasting model or those based on the chaos theory. The ARIMA model for short term, predicts one day demand using 22 features grouped in three types. Water demand of the previous 48 hours. To capture fast changes and weather influence. Water demand of the previous 10 same week days. To capture type day influence and seasonality. Normalized water demand of the previous 10 same week days. To avoid the false seasonality influence. A second short-term water demand forecasting model is used. It is a heuristic model that automatically stores and updates water demand patterns and demand factors for all days of the week and for a configurable number of deviating days like national holidays, vacation periods, and individual deviating days. The model uses this information to adaptively learn the patterns and factors that are used when forecasting the water demand. The two demand forecast algorithms are used and compared for the short and long term prediction. Their results are compared with those of the literature. The results suggest that both methods perform similarly in short term but the ARIMA is more easily generalizable for long term predictions.Peer ReviewedPostprint (published version

Data setup for water distribution system supervision

The availability of on-line data coming from the water distribution systems (WDS) allows the monitoring of such critical infrastructures. Nevertheless the huge amount of data that come to the control centre implies an enormous processing challenge [4]. The use of data avoids any physical theory and relies on statistical correlations and inferences. Nevertheless the previous efforts in modelling the networks encourage the companies to fusion information coming from both sources. Models help validating data and data update these models. This paper presents the first stage of an ongoing project focused in the integration of data and models. Data are collected, harmonised and validated using the models so that they will be used in following stages of the project for the supervision of the WDS (water balance and quality). A tool is being developed in R where the different modules will be integrated.Peer Reviewe