| Abstract: | Two empirical strategies for open-loop on-line optimization are developed as alternatives to the use of mechanistic process models. These strategies are based on on-line identification of dynamic multi-input single-output (MISO) and multi-input multi-output (MIMO) models. The steady state gain of these models provides information for steady state optimization. Desirability functions, originally developed for multi-objective optimization, are utilized as objective function modifiers for constrained on-line optimization. The integration of dynamic model identification and desirability functions results in an on-line optimizer which combines fast optimizing speed with the ability to predict future encroachments on constraint boundaries. Corrections to the search direction are based on these predictions, reducing the probability of actual constraint violation. The optimization strategies are tested by simulation on nonlinear multivariable interacting systems at two levels of complexity: a CSTR supporting a multiple reaction and a fluid catalytic cracker. Both methods were effective in avoiding violation of constraints but the MIMO strategy required fewer steps to reach an optimum and was less prone to generate a nonfeasible optimization step. |
