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Contaminant Source Identification in Water Distribution Networks Under Conditions of Demand Uncertainty

机译：需求不确定条件下的自来水管网污染物源识别

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摘要

Water distribution systems are susceptible to accidental and intentional chemical or biological contamination that could result in adverse health impact to the consumers. This study focuses on a water distribution forensics problem, contaminant source identification, subject to water demand uncertainty. Due to inherent variability in water consumption levels, demands at consumer nodes remain one of the major sources of uncertainty. In this research, the nodal demands are considered to be stochastic in nature and are varied using Gaussian and Autoregressive models. A hypothetical source identification problem is constructed by simulating observations at the sensor nodes from an arbitrary contaminant source. A simulation-optimization approach is used to solve the source identification problem with EPANET tool as the simulator and Genetic Algorithm (GA) as the optimizer. The goal is to find the source location and concentration by minimizing the difference between the simulated and observed concentrations at the sensor nodes. Two variations of GA, stochastic GA and noisy GA are applied to the same problem for comparison. Results show that noisy GA is more robust and is less computationally expensive than stochastic GA in solving the source identification problem. Moreover, the autoregressive demand uncertainty model better represents the uncertainty in the source identification process than the Gaussian model.
机译：供水系统容易受到意外和故意的化学或生物污染，可能对消费者造成不利的健康影响。这项研究的重点是受需水不确定性影响的水分配取证问题，污染物源识别。由于水消耗水平的内在变化，消费者节点的需求仍然是不确定性的主要来源之一。在这项研究中，节点需求本质上被认为是随机的，并且使用高斯模型和自回归模型而变化。通过模拟来自任意污染物源的传感器节点处的观察，构造了一个假设的源识别问题。采用仿真优化方法，以EPANET工具为仿真器，遗传算法（GA）为优化器，解决了源识别问题。目的是通过最小化传感器节点处模拟和观察到的浓度之间的差异来找到源位置和浓度。遗传算法的两种变体，即随机遗传算法和噪声遗传算法，都被应用于同一问题进行比较。结果表明，有噪声的遗传算法在解决源识别问题上比随机遗传算法更健壮，计算量也更少。此外，自回归需求不确定性模型比高斯模型更好地表示了源识别过程中的不确定性。

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