Date of Award


Degree Type


Degree Name

Master of Engineering Science


Chemical and Biochemical Engineering


Dr. Arthur Jutan


The chemical industry is increasingly compelled to operate profitably in a very dynamic and global market. Real-time optimization plays an important role in plant operation because it is at the level of the control hierarchy at which business decisions are integrated into the operation. Model-based optimization schemes require accurate models which are sometimes hard to obtain in practice. The advantage of direct search optimization becomes obvious when an explicit model is unavailable. In this work, a new recursive least squares impulse weight, gradient based, feedback optimization method is developed and tested in both simulation and experimental applications. For a linearized dynamic system, the gradient of the system output with respect to the system input can be interpreted as the first non-zéro impulse weight of the system. This relationship was confirmed by open-loop and closed-loop tests on linear and non-linear static and dynamic systems. This gradient information can thus be used for extremum seeking. Closed-loop control real time case studies demonstrated the ability of the new gradient based optimization method to follow different time-varying optimal policies using a wide range of input signals. Simulation studies also demonstrate that a strong advantage of this new method is its ability to handle processes with changing dead time and model structure.



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