Model-Predictive Control of a Large-Scale System: A Case Study

美国的许多城市都面临着 下水道溢出（民间社会组织）。 联合下水道输送混合物， 城市污水和暴雨径流。 在暴风雨期间， 该等污水渠的容量（及/或相关的处理 植物）可能会超过，合并污水必须 未经处理就释放到环境中。 实时控制 组合式污水输送系统是一种潜在的低成本系统 解决问题的办法。 调节闸门和泵可 操作，以最大限度地存储高峰负荷内 系统，以尽量减少每次风暴事件期间的CSO。 上面描述的问题是控制的设计 大规模系统的算法。 PI已调整模型 预测控制（MPC）策略，并开发了一种控制 大都会西部西雅图污水收集计划 系统 该项目涉及：（1）优化 有许多 操纵变量和许多输出变量，（2） 预测不可测的工厂干扰，（3）会计 对于大多数输出变量的不等式约束，以及（4） 结合包括积分器状态的设备动态， 非线性特性，以及滞后和时间延迟。 本研究是一项为期一年的案例研究， MPC对西雅图大都市污水处理的操作 系统 特定于应用程序的目标是最小化 在暴雨期间从管网中排放未经处理的污水。 模拟研究预测，民间社会组织可以减少28% 在MPC下（相对于现有的基于规则的计算机- 控制策略）。 MPC软件已经安装， 并将在网络中的23个位置调节流量， 10分钟一个周期。 数据将自动收集 在1992-93年雨季的每次风暴事件中， 离线分析，以：（1）衡量民间社会组织的减少， (2)诊断MPC策略中的任何缺点， 多元统计 使用MPC的数据 大规模问题应该鼓励MPC应用， 类似的问题也存在于整个化学工业中。 研究结果还将为一般性问题提供新的见解 在MPC中，包括：（1）模型的影响（和补偿） 误差、未测量的干扰和传感器/致动器故障， （2）约束条件和目标函数--易度的表达 与之竞争的控制目标可以实现在 MPC框架。

Many cities in the U.S. are faced with the problem of combined sewer overflows (CSOs). Combined sewers carry a mixture of municipal sewage and storm runoff. During storm conditions, the capacity of such sewers (and/or the associated treatment plants) may be exceeded and the combined sewage must be released to the environment untreated. Real-time control of the combined sewage conveyance system is a potential low cost solution to the problem. Regulating gates and pumps can be manipulated to maximize storage of peak loads within the system to minimize the CSOs during each storm event. The problem described above is the design of a control algorithm for a large-scale system. The PI has adapted Model Predictive Control (MPC) strategies and developed a control scheme for the Metropolitan West Seattle sewage collection system. The project has involved: (1) optimization of the dynamic response of a plant in which there are many manipulated variables and many output variables, (2) forecasting of unmeasured plant disturbances, (3) accounting for inequality constraints on most output variables, and (4) incorporating plant dynamics that include integrator states, nonlinear characteristics, and lags and time delays. This research is a one-year case study of the performance of MPC on the operation of the Seattle metropolitan sewage system. The application-specific goal is to minimize discharge of raw sewage from the network during storm events. Simulation studies predict that CSOs can be reduced by 28% under MPC (relative to the existing rule-based computer- control strategy). MPC software has already been installed, and will regulate flowrates at 23 locations in the network on a 10-minute cycle. Data will be collected automatically during each storm event in the 1992-93 rainy season, then analyzed off-line to: (1) measure the reduction of CSOs, and (2) diagnose any shortcomings in the MPC strategy using multivariate statistics. Data from the use of MPC on this large-scale problem should encourage application of MPC to similar problems which exist throughout the chemical industry. The results will also provide new insights on generic issues in MPC, including: (1) impact of (and compensation for) model error, unmeasured disturbances, and sensor/actuator failure, and (2) formulation of constraints and objective function-ease with which competing control objectives can be achieved in the MPC framework.