Application of Model-Based Predictive Control to Fossil Power Plants

本研究的目的是为火电厂开发实用的多变量模型预测控制。这里使用的方法包括开发一个通用的降阶模型(ROM)，该模型捕获了发电厂的主要静态和动态特性。为了预测植物的行为，这个模型是必要的。由于模型不能准确地匹配真实的植物结构，模型的参数必须使用预测误差方法或非线性最小二乘法来估计。然后，该模型将用于卡尔曼滤波，以实时估计过程状态。这些估计的状态用于预测，使得能够计算最优控制序列。这项研究建立在初步研究结果的基础上，该初步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。具体地说，ROM的当前结构不能充分地对植物的行为进行建模，因此需要更多的建模工作。另一个问题是计算负担，目前无法在微型计算机上进行实时操作。有几种近似技术可以潜在地将负担降低到原来的十分之一。最后，必须评估MPC算法的参数(加权和控制步长)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.