Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks
Bayesian Optimization of Booster Disinfection Scheduling in Water Distribution Networks
复制标题
配水管网增压消毒调度的贝叶斯优化
DOI：
10.1016/j.watres.2023.120117
复制
发表时间：
2023
期刊：
影响因子：
12.8
通讯作者：
Abokifa, Ahmed A.
中科院分区：
文献类型：
--
作者：
Moeini, Mohammadreza;Sela, Lina;Taha, Ahmad F.;Abokifa, Ahmed A.
氯仍然是全世界饮用水处理和分配系统中使用最广泛的消毒剂。为了在整个配电网中保持最低的残留量，需要通过优化氯增强器的位置及其调度(即氯注入速率)来调节氯的用量。这种优化可能计算昂贵，因为它需要对水质(WQ)模拟模型进行大量评估。近年来，贝叶斯优化(BO)因其在优化黑盒函数方面的有效性而受到广泛关注。本研究首次尝试将生物优化方法应用于供水管网用水定额的优化。开发的基于蟒蛇的框架将BO与EPANET-MSX结合在一起，以优化氯源的调度，同时确保提供满足水质标准的水。利用高斯过程回归建立BO代理模型，对不同BO方法的性能进行综合分析。为此，对不同的捕获函数进行了系统的测试，包括改善概率、预期改善、置信度上界和熵搜索，并结合不同的协方差核，包括Matérn核、平方指数核、Gamma指数核和有理二次核函数。此外，还进行了全面的敏感性分析，以了解不同的BO参数的影响，包括初始点数、协方差核长度尺度以及勘探与开发的水平。结果表明，不同的BO方法的性能有很大的差异，捕获函数的选择对BO性能的影响比协方差核更深刻。
Chlorine remains the most widely used disinfectant in drinking water treatment and distribution systems worldwide. To maintain a minimum residual throughout the distribution network, chlorine dosage needs to be regulated by optimizing the locations of chlorine boosters and their scheduling (i.e., chlorine injection rates). Such optimization can be computationally expensive since it requires numerous evaluations of water quality (WQ) simulation models. In recent years, Bayesian optimization (BO) has garnered considerable attention due to its efficiency in optimizing black-box functions in a wide range of applications. This study presents the first attempt to implement BO for the optimization of WQ in water distribution networks. The developed python-based framework couples BO with EPANET-MSX to optimize the scheduling of chlorine sources, while ensuring the delivery of water that satisfies water quality standards. Using Gaussian process regression to build the BO surrogate model, a comprehensive analysis was conducted to evaluate the performance of different BO methods. To that end, systematic testing of different acquisition functions, including the probability of improvement, expected improvement, upper confidence bound, and entropy search, in conjunction with different covariance kernels, including Matérn, squared-exponential, gamma-exponential, and rational quadratic, was conducted. Additionally, a thorough sensitivity analysis was performed to understand the influence of different BO parameters, including the number of initial points, covariance kernel length scale, and the level of exploration vs exploitation. The results revealed substantial variability in the performance of different BO methods and showed that the choice of the acquisition function has a more profound influence on the performance of BO than the covariance kernel.