Abstract
Mathematical models are the basic tool that simulates the operation of the Water Distribution System (WDS). Building such a tool is a complex task that requires as much detail as possible. The information needed to build a model can be divided into two categories: network data and WDS operating data. The first group includes pipe and node attributes, such as pipe length, pipe diameter, pipe roughness, junction elevation, and junction demand. The second category includes data specifying network performance such as pump characteristics, water demand patterns, and controls. The quality of these data will reflect the quality (compatibility) of the model. In WDS modeling—especially dynamic modeling—water demand patterns will have a significant impact on model accuracy. The appearance of each pattern may be different; it depends on the type of consumption (domestic, industrial) or the period analyzed. Consumption patterns define the operational work of the WDSs. Changes in water demand patterns may affect the accuracy of the model calibration. Real WDS models were used in this paper. Three simulations were analyzed, with each corresponding to a different period: one year, six months, and one month. Junction demand and water demand patterns were generated from a GIS (Geographic Information System) and SCADA (Supervisory Control and Data Acquisition) database.
Highlights
Mathematical models are a basic tool used by water supply companies to support decisionmaking
The purpose of Water Distribution System (WDS) modeling is to reflect the operational workings of a network
Pipe roughness is dependent on the pipe diameter, material, age and water quality, which can be defined by mathematical function or systemized
Summary
Mathematical models are a basic tool used by water supply companies to support decisionmaking. The most important task is to achieve the highest possible accuracy of the WDS model, independently of the chosen period or the occurrence of failure to be analyzed. According to Walski [2], the highest uncertainty of data is related to pipe roughness and water demand. These data are verified in the final stage of calibration—micro-calibration [2,3]. Industry water consumption patterns are determined by the nature of the customers’ work—the maximum water demand may occur in the afternoon or at night—while for businesses, it is characterized by an equal water intake throughout the day (Figure 1). Selection of the simulation period should be preceded by precise analysis of the network operational work and water consumption, so that it reflects the normal operating work of the WDS
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