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Enhanced pump schedule optimization for large water distribution networks to maximize environmental and economic benefits.

机译：大型水分配网络的增强的泵计划优化，以最大程度地提高环境和经济效益。

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摘要

For more than four decades researchers tried to develop optimization method and tools to reduce electricity consumption of pump stations of water distribution systems. Based on this ongoing research trend, about a decade ago, some commercial pump operation optimization software introduced to the market. Using metaheuristic and evolutionary techniques (e.g. Genetic Algorithm) make some commercial and research tools able to optimize the electricity cost of small water distribution systems (WDS). Still reducing the environmental footprint of these systems and dealing with large and complicated water distribution system is a challenge. In this study, we aimed to develop a multiobjective optimization tool (PEPSO) for reducing electricity cost and pollution emission (associated with energy consumption) of pump stations of WDSs. PEPSO designed to have a user-friendly graphical interface besides the state of art internal functions and procedures that lets users define and run customized optimization scenarios for even medium and large size WDSs. A customized version of non-dominated sorting genetic algorithm II is used as the core optimizer algorithm. EPANET toolkit is used as the hydraulic solver of PEPSO. In addition to the EPANET toolkit, a module is developed for training and using an artificial neural network instead of the high fidelity hydraulic model to speed up the optimization process. A unique measure that is called "Undesirability" is also introduced and used to help PEPSO in finding the promising path of optimization and making sure that the final results are desirable and practical. PEPSO is tested for optimizing the detailed hydraulic model of WDS of Monroe city, MI, USA and skeletonized hydraulic model of WDS of Richmond, UK. The various features of PEPSO are tested under 8 different scenarios, and its results are compared with results of Darwin Scheduler (a well-known commercial software in this field). The test results showed that in a reasonable amount of time, PEPSO is able to optimize and provide logical results for a medium size WDS model with 13 pumps and thousands of system components under different scenarios. It also is concluded that this tool in many aspects can provide better results in comparison with the famous commercial optimization tool in the market.
机译：四十多年来，研究人员一直在尝试开发优化方法和工具，以减少配水系统泵站的电力消耗。基于这种持续的研究趋势，大约在十年前，一些商业泵运行优化软件被推向市场。使用元启发式和进化技术（例如遗传算法）使一些商业和研究工具能够优化小型水分配系统（WDS）的电费。仍然要减少这些系统的环境足迹并处理大型复杂的水分配系统仍然是一个挑战。在这项研究中，我们旨在开发一种多目标优化工具（PEPSO），以降低WDS泵站的电力成本和污染排放（与能耗相关）。除了先进的内部功能和程序外，PEPSO还具有易于使用的图形界面，使用户甚至可以为中型和大型WDS定义和运行定制的优化方案。非支配排序遗传算法II的定制版本用作核心优化器算法。 EPANET工具包用作PEPSO的液压求解器。除了EPANET工具包外，还开发了一个模块来训练和使用人工神经网络代替高保真液压模型，以加快优化过程。还介绍了一种称为“不良”的独特方法，该方法可用于帮助PEPSO找到最有希望的优化途径，并确保最终结果是理想和实用的。对PEPSO进行了测试，以优化美国密歇根州门罗市WDS的详细水力模型和英国里士满WDS的骨架化水力模型。在8种不同的情况下测试了PEPSO的各种功能，并将其结果与Darwin Scheduler（该领域著名的商业软件）的结果进行了比较。测试结果表明，在不同情况下，PEPSO能够针对具有13个泵和数千个系统组件的中型WDS模型优化并提供逻辑结果。还可以得出结论，与市场上著名的商业优化工具相比，该工具在许多方面都可以提供更好的结果。

著录项

  • 作者

    Sadatiyan A., S. Mohsen.;

  • 作者单位

    Wayne State University.;

  • 授予单位 Wayne State University.;
  • 学科 Civil engineering.;Water resources management.;Environmental engineering.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 235 p.
  • 总页数 235
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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