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

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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.