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Optimal integrated water resources planning in watersheds with limited water resources.

机译:水资源有限的流域的水资源综合优化规划。

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摘要

The literature in the area of water resources management persistently stresses the need to use an integrated water resources management approach especially in areas that experience water stress. The conventional engineering practice approach to water resources management consists primarily of collecting engineering data, evaluating a few alternatives and selecting one. In this research, optimization models for use in integrated water resources management have been developed. The first model developed, termed yearly static model, is a regional (watershed scale) water supply allocation model, which was formulated with a nonlinear objective function to maximize net benefits. The revenue from water supplies from different sources, the cost of supplying the water from these sources and the damage due to poor quality water delivered to the users were considered in the objective function. This nonlinear programming (NLP) model was applied to a regional water supply system located along the Rio Grande from Caballo dam in New Mexico to El Paso County, Texas.; In the second model, termed seasonal model', the problem was formulated as a dynamic model and applied to the regional water supply along the Rio Grande. This large-scale NLP model was too large to be solved using the previous release of GAMS/MINOS software, version 2.04, but was solvable using its latest release, version 2.25. With the same parameters considered in the objective function as the yearly static model, the solution obtained gave the "best" reservoir release policy for four seasons and a maximum net benefit that is slightly higher than that obtained using the static NLP model.; The third model developed was for the capacity expansion of water supply conveyance and treatment infrastructure over a long-term planning period. This model was formulated as a mixed integer nonlinear programming (MINLP) model that incorporates two sets of decision variables: the optimum timing of the capacity expansion of water supply conveyance systems and water treatment plants and the water allocation policy that maximizes the net benefit. A new methodology that interfaces Simulated Annealing (SA) heuristic search algorithm with the Generalized Reduced Gradient (GRG2) computer code was developed to solve this problem. The SA algorithm sets the binary decision variables used in modeling the capacity expansion.; The MINLP computer program developed is applied to El Paso County's water supply system. With three water supply regions, potentially two water sources (surface water and groundwater) for each region and a planning period of 10 years, the problem involved solving a problem size of over half a million NLP's which was formulated as a single MINLP. (Abstract shortened by UMI.)
机译:水资源管理领域的文献一直强调需要使用综合水资源管理方法,特别是在水资源短缺的地区。水资源管理的常规工程实践方法主要包括收集工程数据,评估一些备选方案并选择其中一种。在这项研究中,开发了用于水资源综合管理的优化模型。开发的第一个模型称为年度静态模型,是区域(流域尺度)供水分配模型,该模型是使用非线性目标函数制定的,以最大化净收益。在目标函数中考虑了来自不同来源的供水收入,来自这些来源的供水成本以及由于向用户输送的劣质水造成的损害。该非线性规划(NLP)模型被应用于沿里奥格兰德河(Rio Grande)从新墨西哥州的卡瓦洛大坝到德克萨斯州埃尔帕索县的区域供水系统。在称为季节性模型的第二个模型中,该问题被表述为动态模型,并应用于里奥格兰德河沿岸的区域供水。这个大型NLP模型太大,无法使用GAMS / MINOS软件的先前版本2.04来解决,但使用其最新版本2.25可以解决。在目标函数中考虑的参数与年度静态模型相同的情况下,所获得的解决方案给出了四个季节的“最佳”储层释放策略,并且最大净收益略高于使用静态NLP模型获得的净收益。开发的第三个模型是在长期计划期间扩大供水输送和处理基础设施的能力。该模型被公式化为混合整数非线性规划(MINLP)模型,该模型包含两组决策变量:供水输送系统和水处理厂的容量扩展的最佳时机以及使净收益最大化的水分配策略。为了解决此问题,开发了一种将模拟退火(SA)启发式搜索算法与广义缩减梯度(GRG2)计算机代码相接口的新方法。 SA算法设置用于容量扩展建模的二进制决策变量。开发的MINLP计算机程序应用于El Paso县的供水系统。有三个供水区域,每个区域可能有两个水源(地表水和地下水),规划期为10年,该问题涉及解决超过50万个NLP的问题大小,该问题大小制定为一个MINLP。 (摘要由UMI缩短。)

著录项

  • 作者

    Ejeta, Messele Zewdie.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Engineering Civil.; Operations Research.; Engineering Sanitary and Municipal.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 272 p.
  • 总页数 272
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;运筹学;建筑科学;
  • 关键词

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