Improved Leakage Detection and Model Calibration Using Night Fire Flow Testing

Water network models include assumptions made in relation to pipe characteristics and demand. The methodology discussed in this paper enables many of the assumptions to be confirmed thereby providing a high level of confidence in models' accuracy. A pressure-dependent leakage detection method has been developed. The results indicate that the method is effective despite model calibration problems and field data limitations. Optimisation predictions can be improved when based on hydraulic conditions that occur at night that have been impacted by hydrant and/or other planned interventions. This is even so for networks with excess design capacity and where loggers may be working close to their limits of accuracy. The paper considers the impact of more interventionist field testing on water mains networks and its beneficial influence on model calibration and optimisation techniques. The approach includes planned opening and shutting of hydrants during and around minimum night-flow. Line valves are also operated in a planned way. The water industry has been averse to such interventions because of concerns about discoloured water complaints. Mitigation of such risks is addressed. Due to the need to collect most of the data at night including hydrant discharges the new approach has been referred to as Night Fire Flow Testing (NFFT). Using fabricated NFFT, two simple desk top models, one with two pressure zones, have been reviewed as have two all-mains models supplied by United Utilities.