Application of Nonlinear Model-Based Predictive Control to Fossil Power Plants

该项目的目标是为化石电厂开发实用的、多元的、非线性的模型预测控制（NMPC）。该方法涉及开发一个通用的、第一性原理的、降阶的模型，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，该模型将用于实时估计过程状态，以获得最优控制序列。

The objective of this project is the development of practical, multivariate, nonlinear, model predictive control (NMPC) for fossil power plants. The approach involves the development of a generic, first-principles, reduced-order model which captures the dominant static and dynamic characteristics of a power plant. 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 to estimate process states in real time for an optimal control sequence.