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Geoffrey de Ryck et al . The proposed model was further validated using a case study of a metropolitan water distribution system in Montreal, Canada. The model also predicted the minimum theoretical water flow for daily operation, the expected water quality and the maximum attenuation of contaminants before they degrade to an unacceptable level for drinking water. Such predictions serve as a basis for refinement of the network model and for the design of on-line, automated system.The model can be used to investigate design criteria to optimize the operation of a water distribution system. With the initial model of a real system, predictions of future performance can be made. For example, it can be used to analyze how the introduction of an automatic metering device affects the consumption of water, the quality of water and water pressure.The model is versatile and can be applied to any water distribution system, including water treatment plants and large distribution networks. It can also be used to simulate the dynamics of entire distribution systems. The paper shows how the framework approach can solve complex WDN problems efficiently by integrating a model for drinking water distributions. So far, the model has been validated using a real water distribution system in Montreal, Canada. It has been applied successfully to analyse design criteria for the operation of a distribution system in Montreal, Canada. At this stage, in order to save a lot of time, the screening and sizing of network components will be further considered and this will be reported in detail in a separate paper. However, several interesting results have been achieved as follows: (1) the pressure profile and the water flow rate at each node can be simulated continuously and easily, (2) the total water flow calculated by the model was 5%-7% lower than the result of an independent water flow model, and (3) the predicted power consumption by the network model was within 3% of the value calculated by independent WDN model.
The authors developed a new model using visual programming tools. The model consists of two individual models EPANET 2 and EPANET-PDX. A user interface design tool has been developed to allow any user to develop and run their own code. The link between the components is made through a module EPANET-PDX, which provides the communication between the two models. One of the most important features of this model is that there are no restrictions on the required input parameters.
In addition, a Python script was written to gather all of the input data needed by the two individual EPANET models. The limitations of the input data are specified in the calibration file. EPANET 2 and EPANET-PDX were first run separately with the same input data and the simulation time required to analyse the simulation results was calculated. The simulation times were compared to the previously obtained results and were found to be very similar to the previously performed calculations.
The model requires a huge amount of memory and the simulations were computationally very expensive. A potential solution is to run the two models in parallel. Therefore, EPANET-PDX was integrated with EPANET 2. The EPANET-PDX was executed in a simulation mode and the results were analysed in a post-processing mode.
This method in EPANET is somewhat arbitrary, but works by transforming the inverse demand-pressure curve to the direct demand-pressure curve. The problem with this approach is that, due to the complexities of the transformation, a significant deviation from the original demand-pressure curve is often seen (Figures 1-7).
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