Flow estimation and fault diagnosis for automatic control valves in water supply networks

Dynamic adaptability of water supply networks (WSNs) in terms of connectivity and hydraulic conditions is essential for their operation as there are increasing demands on serviceability (leakage, water quality, incident management and fire flow), resilience and cost efficiency. A common approach to achieve multiple control functions throughout networks is to employ automatic control valves (ACVs). Advances in low-powered electronics and micro-actuators enable a wide range of novel control methods in WSNs, including the flow-based pressure control (or flow modulation control (FM)). The implementation of FM schemes has been steadily increasing as it has a major advantage of a closed-loop (feedback) control by utilising measurements to define the flow-pressure control profile. The performance of the FM scheme relies on continuous and accurate flow measurements. Hence, to achieve robust control in WSNs, high-level reliability of the control solution is required. Herein, two methods for the reliable operation of ACVs are investigated, namely (i) Flow estimation and (ii) Fault detection and diagnosis. A novel flow estimation method for diaphragm-actuated globe valves has been developed and experimentally investigated. The method utilises three pressure measurements, namely the valve inlet pressure, the valve outlet pressure and the control chamber pressure (the 3P flow estimation method). The method relies upon the accurate computation of the valve stem position, the measured pressure differential across the valve and the flow coefficients of the valve (Cv, Kv). The developed valve stem position estimation model results in multiple solutions. Advances in signal processing are combined with a machine learning technique (support vector machine) to distinguish the correct solution. The proposed 3P method is compared with a method which uses sensor measurements of the valve stem position (the 2P&Pos method), and its performance validated against measurements from an electromagnetic flowmeter. The uncertainty bounds of the flow estimation methods are also derived. For fault diagnosis, methods for early fault detection and diagnosis (FDD) are investigated. Potential faults are categorised, and residuals and feature variables are defined to detect a fault and diagnose its likely cause. Experimental data have been generated and utilised from controlled laboratory conditions, from an operational network and also from a numerical simulation. The performance of the proposed schemes has been validated.Open Acces

Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River

Frequent saltwater intrusions in the Chao Phraya River have had an impact on water supply to the residents of Bangkok and nearby areas. Although relocation of the raw water station is a long-term solution, it requires a large amount of time and investment. At present, knowing in advance when an intrusion occurs will support the waterworks authority in their operations. Here, we propose a method to forecast the salinity at the raw water pumping station from 24 h up to 120 h in advance. Each of the predictor variables has a physical impact on salinity. We explore a number of model candidates based on two common fitting methods: multiple linear regression and the artificial neural network. During model development, we found that the model behaved differently when the water level was high than when the water level was low (water level is measured at a point 164 km upstream of the raw water pumping station); therefore, we propose a novel multilevel model approach that combines different sub-models, each of which is suitable for a particular water level. The models have been trained and selected through cross-validation, and tested on real data. According to the test results, the salinity can be forecasted with an RMSE of 0.054 g L${^{-1}}$ at a forecast period of 24 h and up to 0.107 g L${^{-1}}$ at a forecast period of 120 h

CCWI2017: F143 'Fault Detection and Diagnosis for Pressure Control Valves in Water Supply Networks'

The control of water supply networks is becoming more advanced and complex, and it is therefore increasingly important to continuously monitor and optimise the performance of automatic control valves. This paper investigates a method for early fault detection and diagnosis (FDD) of pressure control valves in water supply networks. Potential faults are categorised, and different process variables and residuals are defined from continuous measurements and model-based simulations of the operation of a diaphragm actuated globe valve in order to detect a fault and diagnose its likely cause. We generate and utilise experimental data from controlled laboratory conditions and an operational network together with numerically simulated data to validate the performance of the proposed FDD method.<br

Theoretical Estimation of Energy Balance Components in Water Networks for Top-Down Approach

The energy balance calculation for pressurized water networks is an important step in assessing the energy efficiency of water distribution systems. However, the calculation generally requires mathematical modelling of the water networks to estimate three important energy components: outgoing energy through water loss (El), friction energy loss (Ef) and energy associated with water loss (EWL). Based on a theoretical energy balance analysis of simplified pipe networks, a simple method is proposed to estimate El, Ef and EWL with minimum data requirements: input energy, water loss (WL) and head loss between the source and the minimum energy point (ΔH). By inclusion of the head loss in water networks into the estimation, the percentages of El and EWL are lower and higher, respectively, than using only the percentage of WL. The percentage of Ef can be a function of the percentage of ΔH. By demonstrating our analysis with the simulation results from the mathematical models of 20 real water networks, the proposed method can be used to effectively estimate El, Ef and EWL as a top-down energy balance approach

Theoretical Estimation of Disinfectant Mass Balance Components in Drinking Water Distribution Systems

The water quality audit concept is an important feature in assessing the loss of disinfectant mass in drinking water distribution systems. Based on this concept, the loss can be divided into the loss of disinfectant mass through water losses (Ml) and the decay of disinfectant mass due to chemical reactions (Mr). When an audit focuses on the effect of water losses on the loss of disinfectant mass, the decay of disinfectant mass by chemical reactions with the ideal condition of no water losses (Mro) has to be estimated; thus, the disinfectant mass associated with water losses (MWL=Ml+Mr−Mro) can be assessed. Generally, the computation of these components (Ml, Mr, and MWL) needs hydraulic and water quality modeling. In this study, we propose a novel method based on a simple theoretical analysis to evaluate these components using only two parameters: the ratio of water losses (p) and the ratio of disinfectant concentrations at the critical pressure point and the network inlet (Cp*). The coefficients of our theoretical Ml, Mr, and MWL were estimated using 20 real network models, with p between 2.8% and 54.9% and Cp* between 18.4% and 91.9%. The results showed that our equations were effective at assessing the loss of disinfectant mass in drinking water distribution networks for the top-down auditing approach