Application of nonlinear model-based predictive control to fossil power plants

The authors report preliminary results on the development of practical, multivariate, nonlinear, model predictive control for fossil fuel power plants. The approach used involves the development of a first-principles, nonlinear reduced-order model which captures the dominant static and dynamic characteristics of a power plant. This model is used to predict the plant response to control inputs. 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 is then used in a Kalman filter to estimate process states in real time. These estimated states are used for prediction, enabling the computation of the optimal control sequence. The results of full-scale boiler control simulation were encouraging, and conclusively demonstrate the feasibility of the approach. The results appear to be significantly better than those of most existing control systems. Although some additional problems remain to be solved, no serious problems with the technique have been identified