Harif Siroos, Azizyan Gholamreza, Dehghani Darmian Mohsen, Givehchi Mohammad
Civil Engineering Department, University of Sistan and Baluchestan, Zahedan, Iran.
Chair of Engineering Hydrology and Water Management, Technical University of Darmstadt, Darmstadt, Germany.
Environ Sci Pollut Res Int. 2023 Apr;30(18):53229-53252. doi: 10.1007/s11356-023-26075-5. Epub 2023 Feb 28.
One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation-optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58 min and the best detection time of 5.06 min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood.
减少污水消耗最有效的方法之一是在配水管网（WDN）中合理布置水质传感器。在本研究中，开发了NSGA-III算法，通过平衡四个相互冲突的目标来优化传感器的最佳位置：（1）检测可能性；（2）预期检测时间；（3）检测冗余度；（4）检测前受影响的节点。研究过程提出了氯在定义的上下限之间的动态变化，这是利用蒙特卡罗模拟模型确定的。为了选择具有所有可能事件相同特征和影响的污染矩阵，应用了一种启发式方法。本研究引入重要性系数来评估污染事件和管网节点。使用所提出的模拟优化方法，针对基准和实际配水管网计算了两组相互冲突目标中每组目标的帕累托前沿。结果表明，应将传感器安装在管网下游以最大化传感器检测可能性；然而，这会增加检测时间。对于基准管网，最大和最小检测可能性分别计算为92.8%和61.1%，对应的最差检测时间为11.58分钟，最佳检测时间为5.06分钟。因此，关于这两个目标函数的传感器位置相互冲突。此外，与传感器数量相关的敏感性分析表明，当传感器数量增加时，帕累托前沿成为更有效的工具。通过将实际水网中的传感器数量从5个增加到10个和从5个增加到15个，最佳污染检测可能性分别提高了18.93%和24.66%。此外，向基准管网添加超过10个传感器以及向实际系统添加超过15个传感器，增加的检测可能性很小。
