Application of Model-Based Predictive Control to Fossil Power Plants

研究的目的是为火电厂开发实用的多变量模型预测控制（MPC）。这里使用的方法包括开发一种通用的降阶模型（ROM），该模型捕获了发电厂的主要静态和动态特性。这个模型对于预测植物的行为是必要的。由于模型不能完全匹配真实的植物结构，因此必须使用预测误差方法或非线性最小二乘来估计模型的参数。然后将该模型用于卡尔曼滤波器中以实时估计过程状态。这些估计状态用于预测，使最优控制序列的计算成为可能。这项研究建立在一项初步研究的结果之上，该研究试图证明该方法的可行性。结果令人鼓舞，但要使这种方法切实可行，还需要做更多的工作。特别是，目前ROM的结构不能充分模拟植物的行为，因此需要更多的建模工作。另一个问题是计算负担，这目前妨碍了在微型计算机上进行实时操作。几种近似技术可以潜在地将负担减少十倍。最后，必须评估MPC算法的参数（权重和控制步长）以获得最佳性能。该方法的成功应用将提高效率和可用性，减少维护和污染。

The objective of the research is development of practical, multivariate model predictive control (MPC) for fossil power plants. The approach used here involves the development of a generic, reduced-order model (ROM) which captures the dominate static and dynamic characteristics of power plants. This model is necessary in order to predict the plant behavior. Since the model will not exactly match the true plant structure, the parameters of the model must be estimated using prediction error methods or nonlinear least squares. This model will then be used in a Kalman filter to estimate process states in real-time. These estimated states used for prediction, enabling the computation of the optimal control sequence. The research builds on the results of a preliminary study which attempted to demonstrate feasibility of the approach. The results were encouraging but much more work is needed to make the approach practical. In particular, the current structure of the ROM does not adequately model the behavior of a plant and thus more modeling work is required. Another problem is the computational burden, which currently precludes real-time operation on a microcomputer. Several approximation techniques can potentially reduce the burden by a factor of ten. Finally, parameters of the MPC algorithm (weighting and control step size) must be evaluated to achieve the best performance. The successful application of the method will enable increased efficiency and availability, and reduced maintenance and pollution.