Identification-based real-time optimization and its application to power plants.

Most real-time optimization (RTO) methods in process industries are based on rigorous (mechanistic) models or on some special performance tests. Both methods are difficult to apply due to low model accuracy and high costs in modeling, in computation or in performance tests. In this work, a real-time optimization method based on system identification is developed. No rigorous models or special performance tests are needed in this approach, making it cost-effective and applicable in various process industries and especially suitable for energy and utility systems. First, the selection of objective function and of decision variables is discussed. Then the identification-based optimization method is proposed where a closed-loop multivariable identification approach is incorporated. The method is verified using a simulated power plant and is also partially applied to a real power plant. • A real-time optimization method based on system identification is developed. • The steady-state gradient is obtained from dynamic data using system identification. • The gradient uncertainty issue is solved using system identification theory. • The proposed method is verified using a simulated 1000 MW power plant and partially applied to a 600 MW power plant. [ABSTRACT FROM AUTHOR]