The valuee of model-based process optimization systems for competitive advantage in many industries, has been widely recognized. Such model-based optimization systems include Real-Time Optimization, On-Line Optimizing Control, off-line process scheduling, and any other economic process optimization scheme which uses a process model to predict optimal plant operation. The thesis investigates the design of these model-based optimization systems, particularly with respect to model structure and adjustable parameter selection.

The main contribution of this work include design phase methods, based on fundamental principles of optimization and statistics theory, for determining whether a model-based optimization system can attain the plant optimum, as well as methods for discriminating between design alternatives. Three necessary conditions for zero-offset from the optimal plant operation are presented. These include Pont-Wise Model Adequacy, Augmented Model Adequacy and Point-Wise Stability. Recognizing that achieving zero-offset from the plant optimum may not always be possible, or may not be the only design objective, a Design Cost method is presented for selecting among design alternatives. This Design Cost method provides a natural "trade off" between offset elimination and variance of the predicted optimal manipulated variable values.

Finally, the thesis is completed with a larger-scale case study involving the Williams-Otto Plant [1960]. In the case study selection of a process model and the adjustable parameter set for implementation in closed-loop Real-Time Optimization system is investigated.