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智能缓冲控制系统在污水处理厂的性能评估。

Performance evaluation of a smart buffer control at a wastewater treatment plant.

机构信息

Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands; Witteveen+Bos, P.O. Box 233, 7400 AE Deventer, The Netherlands.

Waterways d.o.o., Gornji Vukojevac 10A, 44272 Lekenik, Croatia.

出版信息

Water Res. 2017 Nov 15;125:180-190. doi: 10.1016/j.watres.2017.08.042. Epub 2017 Aug 23.

DOI:10.1016/j.watres.2017.08.042
PMID:28854385
Abstract

Real time control (RTC) is increasingly seen as a viable method to optimise the functioning of wastewater systems. Model exercises and case studies reported in literature claim a positive impact of RTC based on results without uncertainty analysis and flawed evaluation periods. This paper describes two integrated RTC strategies at the wastewater treatment plant (WWTP) Eindhoven, the Netherlands, that aim to improve the use of the available tanks at the WWTP and storage in the contributing catchments to reduce the impact on the receiving water. For the first time it is demonstrated that a significant improvement can be achieved through the application of RTC in practice. The Storm Tank Control is evaluated based on measurements and reduces the number of storm water settling tank discharges by 44% and the discharged volume by an estimated 33%, decreasing dissolved oxygen depletion in the river. The Primary Clarifier Control is evaluated based on model simulations. The maximum event NH4 concentration in the effluent reduced on average 19% for large events, while the load reduced 20%. For all 31 events the reductions are 11 and 4% respectively. Reductions are significant taking uncertainties into account, while using representative evaluation periods.

摘要

实时控制(RTC)越来越被视为优化废水系统功能的可行方法。文献中报告的模型练习和案例研究声称,基于没有不确定性分析和有缺陷的评估期的结果,基于 RTC 的方法具有积极影响。本文描述了荷兰埃因霍温废水处理厂(WWTP)的两种集成 RTC 策略,旨在提高 WWTP 现有罐的使用效率,并在贡献流域中存储,以减少对接收水的影响。本文首次证明,通过在实践中应用 RTC 可以实现显著的改进。根据测量结果对 Storm Tank Control 进行了评估,该控制将暴雨沉降池的排放次数减少了 44%,排放量减少了约 33%,减少了河流中溶解氧的消耗。根据模型模拟对 Primary Clarifier Control 进行了评估。对于大事件,出水的最大事件 NH4 浓度平均降低了 19%,而负荷降低了 20%。对于所有 31 个事件,减少率分别为 11%和 4%。考虑到不确定性并使用代表性的评估期,这些减少是显著的。

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