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首页> 外文期刊>Journal of Environmental Engineering >Real-Time Multistep Prediction of Sewer Flow for Online Chemical Dosing Control
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Real-Time Multistep Prediction of Sewer Flow for Online Chemical Dosing Control

机译：在线下药量控制的下水道流量实时多步预测

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Chemical dosing is the most common strategy for sulfide control in sewers. Recent research has shown that online control of chemical dosing can significantly reduce dosing costs, while achieving better control performance. One of the bottlenecks of online control is the prediction of sewage retention time in sewers, governed by future sewage flows. This study developed a methodology for real-time future flow prediction in sewers based on autoregressive moving average (ARMA) models and multistep iterative prediction. This methodology was validated with flow data collected from two pumping stations with different flow characteristics and different wet-well storage capacities. The results showed that the proposed methodology was capable of predicting future flow rates with good accuracy under different weather conditions. Online control of chemical dosing with real-time sewer flow prediction was tested through a simulation study. Results showed that future flow prediction improved sulfide control and significantly reduced chemical dosage. (C) 2014 American Society of Civil Engineers.
机译：化学计量是下水道中硫化物控制的最常见策略。最近的研究表明，化学计量的在线控制可以显着降低计量成本，同时实现更好的控制性能。在线控制的瓶颈之一是对下水道中污水保留时间的预测，这取决于未来的污水流量。这项研究开发了一种基于自回归移动平均（ARMA）模型和多步迭代预测的下水道实时未来流量预测的方法。从两个具有不同流量特性和不同湿井存储能力的泵站收集的流量数据验证了该方法。结果表明，所提出的方法能够在不同天气条件下以较高的精度预测未来的流量。通过模拟研究测试了具有实时下水道流量预测的化学加药在线控制。结果表明，未来的流量预测可以改善硫化物的控制并显着减少化学药剂的用量。 （C）2014美国土木工程师学会。

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