Peter Chalk Centre

University of Exeter

Stocker Road

Exeter

EX4 4QD

Tel: +44 (0)1392 263637

E-mail: CCWI2019@exeter.ac.uk 

17th International Computing & Control for the Water Industry Conference

1st - 4th September 2019
University of Exeter, UK
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3B Demand, leakage, energy

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Green

Steven Buchberger

Chair:

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A multi-objective framework for managing self-cleaning capacity and leakage: application to a real network model

Edo Abraham

Presenter:

Authors:

Edo Abraham, Filippo Pecci and I Stoianov

Discolouration events are by far the main water quality related customer complaints globally for water utilities and their control has become a top priority. In this manuscript, we consider the proactive control of hydraulic conditions (flow velocities) to maximise the self-cleaning capacity (SCC) of a water distribution network under normal operations. For Dutch applications, we have mathematically formalised SCC and how it can be optimised through a change of network topology (i.e. by closing an appropriate selection of isolation valves) to form a more branched network with higher velocities. Recently, we have also shown how leakage can be managed through the optimal operation of pressure reducing valves (PRVs) and a dynamic network topology. Using existing network controllers like PRVs, flow velocities can also be controlled to maximise SCC under diurnal high demand periods. These two approaches can be used as elements of a practicable and cost-effective discolouration risk and leakage management strategy. In this work, a scalable multi-objective optimisation method was used to study the trade-off between maximising SCC and minimising average zone pressure (AZP) using optimal control of PRVs. By applying this to a real network model, we have characterised the Pareto optimal sets for maximising SCC and minimising leakage. We have shown that significant increases in the percentage of pipe lengths that achieve a self-cleaning velocity threshold can be achieved at the expense of only marginal increases in AZP. This is done by keeping SCC control only to peak demand times, for example, in the morning.

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BEEPANET, a tool for computing water-energy balance and performance assessment in drinking water systems

Laura Monteiro

Presenter:

Authors:

Aisha Mamade, Sílvia Fernandes, Laura Monteiro, Dália Loureiro and Dídia Covas

Water-energy balances have been developed in recent years to assess energy consumption and efficiency in water supply systems. These balances stand out from standard energy efficiency practices for considering not only the efficiency of pumping stations and for including other inefficiencies, namely water losses, friction in pipes and valves and layout associated losses. However, the calculation of detailed water-energy balance components in network models remains time-consuming and prone to errors. The main objective of this paper is to present BEEPANET, a novel, accurate, web-based and intuitive tool that automatically calculates a detailed water-energy balance, as well as performance indicators for water distribution networks. This tool is demonstrated with a full-scale distribution network. Future work includes the simulation of alternative solutions for the assessment of the most effective energy efficiency measures. This tool can be used by a broad audience, from students and researchers to water utility technicians and consultants since it will be open to the community and will be periodically updated.

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Inverse Problem Formulation for Leak Localisation in Water Distribution Networks

Caroline Blocher

Presenter:

Authors:

Caroline Blocher, Filippo Pecci and Ivan Stoianov

Leakage detection and localisation has become a critical activity for water companies in order to reduce costs, to avoid penalties imposed by regulators and to increase sustainability. In this study, we focus on methods for locating leaks in water distribution networks. The leak localisation problem has previously been formulated as an inverse problem. However, the inverse problem is in general ill-posed and retrieving the exact leak location is not always possible. Strategies to formulate and solve the ill-posed inverse problem for leak localisation are hence required. In this study, we investigate formulating the inverse problem by adding a weighted regularisation term to mitigate the effects of ill-posedness. The numerical results suggest that the proposed approach is useful to identify a set of leak candidates that substantially reduces the search area for the leak.

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Investigating the drivers of household water demand: A Random Forest modelling approach

Maria Xenochristou

Presenter:

Authors:

Maria Xenochristou, Chris Hutton, Jan Hofman and Zoran Kapelan

Water availability is a topic of increasing concern, especially under future socio-economic and climate changes. Thus, accurate forecasts of water consumption that account for household, customer, and weather characteristics are essential in order to ensure the maintenance of the water supply-demand balance. The current research quantifies the effect of several variables on water demand forecasting accuracy, whilst accounting for the complicated relationships between them. It uses a combination of statistical analysis and machine learning in order to identify the drivers of water demand when past consumption is included or excluded as an explanatory variable. Results indicate that when past consumption is included in the model, it is by far the most important factor whereas when past consumption data is not available, social, household, and temporal characteristics can be used to forecast demand.

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Long and Short Term Demand Forecast, a Real Application

Ramon Perez

Presenter:

Authors:

Sergi Grau, Anna Luis, Jordi Saludes and Ramon Perez

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.

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