Krüger A/S, Veolia Water Technologies, Søborg, Denmark E-mail:
Department of Environmental Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
Water Sci Technol. 2020 Apr;81(8):1766-1777. doi: 10.2166/wst.2020.266.
An integrated model predictive control (MPC) strategy to control the power consumption and the effluent quality of a water resource recovery facility (WRRF) by utilizing the storage capacity from the sewer system was implemented and put into operation for a 7-day trial period. This price-based MPC reacted to electricity prices and forecasted pollutant loads 24 hours ahead. The large storage capacity available in the sewer system directly upstream from the plant was used to control the incoming loads and, indirectly, the power consumption of the WRRF during dry weather operations. The MPC balances electricity costs and treatment quality based on linear dynamical models and predictions of storage capacity and effluent concentrations. This article first shows the modelling results involved in the design of this MPC. Secondly, results from full-scale MPC operation of the WRRF are shown. The monetary savings of the MPC strategy for the specific plant were quantified around approximately 200 DKK per day when fully exploiting the allowed storage capacity. The developed MPC strategy provides a new option for linking WRRFs to smart grid electricity systems.
采用一种集成的模型预测控制（MPC）策略，通过利用污水系统的储存能力来控制水资源回收设施（WRRF）的能耗和出水质量，并进行了为期 7 天的试验。这种基于价格的 MPC 对电价和 24 小时前预测的污染物负荷作出反应。工厂上游的污水系统具有很大的储存能力，可用于控制进入负荷，并间接地控制干旱天气运行期间 WRRF 的能耗。MPC 基于线性动力学模型和储存能力及出水浓度的预测来平衡电费和处理质量。本文首先展示了设计这种 MPC 所涉及的建模结果。其次，展示了 WRRF 的全规模 MPC 运行结果。当充分利用允许的储存能力时，该特定工厂的 MPC 策略可节省约 200 丹麦克朗/天的费用。所开发的 MPC 策略为 WRRF 与智能电网电力系统的连接提供了一种新的选择。
