Stentoft P A, Munk-Nielsen T, Møller J K, Madsen H, Valverde-Pérez B, Mikkelsen P S, Vezzaro L
Krüger A/S, Veolia Water Technologies, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
Krüger A/S, Veolia Water Technologies, Denmark.
Water Res. 2021 May 15;196:116960. doi: 10.1016/j.watres.2021.116960. Epub 2021 Feb 26.
This study presents a general model predictive control (MPC) algorithm for optimizing wastewater aeration in Water Resource Recovery Facilities (WRRF) under different management objectives. The flexibility of the MPC is demonstrated by controlling a WRRF under four management objectives, aiming at minimizing: (A) effluent concentrations, (B) electricity consumption, (C) total operations costs (sum electricity costs and discharge effluent tax) or (D) global warming potential (direct and indirect nitrous oxide emissions, and indirect from electricity production) . The MPC is tested with data from the alternating WRRF in Nørre Snede (Denmark) and from the Danish electricity grid. Results showed how the four control objectives resulted in important differences in aeration patterns and in the concentration dynamics over a day. Controls B and C showed similarities when looking at total costs, while similarities in global warming potential for controls A and D suggest that improving effluent quality also reduced greenhouse gasses emissions. The MPC flexibility in handling different objectives is shown by using a combined objective function, optimizing both cost and greenhouse emissions. This shows the trade-off between the two objectives, enabling the calculation of marginal costs and thus allowing WRRF operators to carefully evaluate prioritization of management objectives. The long-term MPC performance is evaluated over 51 days covering seasonal and inter-weekly variations. On a daily basis, control A was 9-30% cheaper on average compared to controls A, D and to the current rule-based control. Similarly, control D resulted on average in 35-43% lower greenhouse gasses daily emission compared to the other controls. Difference between control performance increased for days with greater inter-diurnal variations in electricity price or greenhouse emissions from electricity production, i.e. when MPC has greater possibilities for exploiting input variations. The flexibility of the proposed MPC can easily accommodate for additional control objectives, allowing WRRF operators to quickly adapt the plant operation to new management objectives and to face new performance requirements.
本研究提出了一种通用的模型预测控制（MPC）算法，用于在不同管理目标下优化水资源回收设施（WRRF）中的废水曝气。通过在四个管理目标下控制WRRF，展示了MPC的灵活性，目标是将以下各项降至最低：（A）出水浓度；（B）电力消耗；（C）总运营成本（电力成本与排放废水税之和）；或（D）全球变暖潜能值（直接和间接的一氧化二氮排放，以及电力生产产生的间接排放）。使用来自丹麦诺勒斯内德的交替式WRRF和丹麦电网的数据对MPC进行了测试。结果表明，这四个控制目标在曝气模式和一天内的浓度动态方面产生了显著差异。从总成本来看，控制措施B和C表现出相似性，而控制措施A和D在全球变暖潜能值方面的相似性表明，提高出水质量也能减少温室气体排放。通过使用组合目标函数，同时优化成本和温室气体排放，展示了MPC在处理不同目标方面的灵活性。这显示了两个目标之间的权衡，能够计算边际成本，从而使WRRF运营商能够仔细评估管理目标的优先级。在涵盖季节和周际变化的51天内评估了MPC的长期性能。与控制措施A、D以及当前基于规则的控制相比，控制措施A每天平均便宜9 - 30%。同样，与其他控制措施相比，控制措施D平均每天的温室气体排放量降低35 - 43%。在电价或电力生产产生的温室气体排放日际变化较大的日子里，即MPC有更大机会利用输入变化时，控制性能之间的差异会增大。所提出的MPC的灵活性能够轻松适应额外的控制目标，使WRRF运营商能够迅速使工厂运营适应新的管理目标，并应对新的性能要求。
