本文确定了俄勒冈州两个城市从计算机化配水中获得的效益 用于准备第2阶段消毒剂/消毒副产品（D/DBP）的模型 制定规则并确定运营改进。这些好处包括从两个方面吸取的经验教训 成功的动态模拟（或所谓的“延长期”）建模项目，适用于 供水系统。CH2M希尔和两个俄勒冈州公用事业公司尤金水电 董事会（EWEB）和奥尔巴尼市充分利用了GIS应用、实地工作、， SCADA和水力建模，以规划系统改进。 服务区人口约16万的EWEB，以及奥尔巴尼市 服务区人口4.3万，均认识到延长使用期限的重要性 进行初始配电系统评估（IDSE）所需的仿真（EPS）模型 根据第二阶段D/DBP规则。两家公用事业公司都将第一步视为建设和使用EPS 在其分配系统中确定高水龄区域。 本文提供了一些关键点，以帮助水务公司管理人员了解两个水务公司 选择将现有的稳态模型升级为动态模型，着眼于 符合新的IDSE法规。实地工作的挑战、好处和成本以及 描述了校准模型的程序。对于现场工作程序，泵流量 SCADA测量的水箱水面变化提供了系统每小时的变化 24至72小时内的需求。本文提供的例子包括 模型代表了低需求期的实际配水系统波动， 当水箱和管道中的水变得停滞时。这些模型有助于确定降低成本的方法 水的老化和水质的改善。包括表格、数字。

This paper identifies the benefits that two Oregon cities obtained from computerized water distribution models, used to prepare for the Stage 2 Disinfectants/Disinfection Byproducts (D/DBP) Rule and to identify operation improvements. These benefits include lessons learned from two successful dynamic simulation (or so-called "extended-period") modeling projects, as applied to water distribution systems. CH2M HILL and two Oregon utilities, Eugene Water and Electric Board (EWEB) and the City of Albany, took advantage of GIS applications, field work, SCADA, and hydraulic modeling to plan system improvements. EWEB, with a service area population of approximately 160,000, and the City of Albany, with a service area population of 43,000, both recognized the importance of using an extended period simulation (EPS) model to conduct the Initial Distribution System Evaluation (IDSE), required by the Stage 2 D/DBP Rule. Both utilities saw the first step as building and using an EPS to locate areas of high water age in their distribution systems. This paper provides key points to help water utility managers understand how two utilities chose to upgrade their existing steady-state models to dynamic models, with an eye towards meeting the new IDSE regulations. Challenges, benefits, and costs of the field work and the procedures for calibrating the models are described. For the field work procedures, pump flows and tank water surface changes measured by SCADA provided hourly changes in system demand over a period of 24 to 72 hours. Examples provided in this paper include how the models represented the actual water distribution system fluctuations during low demand periods, when water becomes stagnant in tanks and pipelines. The models help identify ways to reduce the water age and improve water quality. Includes tables, figures.