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Model predictive control of urban drainage systems: A review and perspective towards smart real-time water management

机译:城市排水系统的模型预测控制:智能实时水管理的综述与视角

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Model predictive control (MPC) can be used to manage combined urban drainage systems more efficiently for protection of human health and the environment, but examples of operational implementations are rare. This paper reviews more than 30years of partly heterogeneous research on the topic. We propose a terminology for MPC of urban drainage systems and a hierarchical categorization where we emphasize four overall components: the receding horizon principle, the optimization model, the optimization solver, and the internal MPC model. Most of the reported optimization models share the trait of a multiobjective optimization based on a conceptual internal MPC model. However, there is a large variety of both convex and non-linear optimization models and optimization solvers as well as constructions of the internal MPC model. Furthermore, literature disagrees about the optimal length of the components in the receding horizon principle. The large number of MPC formulations and evaluation approaches makes it problematic to compare different MPC methods. This review highlights methods, challenges, and research gaps in order to make MPC of urban drainage systems accessible for researchers and practitioners from different disciplines. This will pave the way for shared understanding and further development within the field, and eventually lead to more operational implementations.
机译:模型预测控制(MPC)可用于更有效地管理组合的城市排水系统以保护人类健康和环境,但操作实施的例子很少见。本文回顾了30多年的部分异构研究。我们为城市排水系统的MPC提出了术语和分层分类,在那里我们强调四个整体组件:后退地平线原理,优化模型,优化求解器和内部MPC模型。大多数报告的优化模型都以概念内部MPC模型为基于概念内部MPC模型的多目标优化的特征。然而,各种各样的凸和非线性优化模型以及优化求解器以及内部MPC模型的结构。此外,文学不同意后退地平线原理中组件的最佳长度。大量MPC配方和评估方法使得比较不同的MPC方法是有问题的。此综述突出了方法,挑战和研究差距,以使来自不同学科的研究人员和从业者可访问的城市排水系统的MPC。这将为公园内的共同理解和进一步发展铺平道路,最终导致更多的运营实施。

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