Towards Reliable and Efficient Real-Time Optimization of Processing Plants

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With increasing global competition, the process industry faces intense pressure to improve production efficiency, product quality and process safety. As a result, real-time optimization (RTO) is used extensively for the operational optimization of the plant (either a single unit or part of a larger plant). RTO is a model-based upper-level control system that repeatedly provides set points to the lower-level control system with the objective to maintain process operation as close as possible to the economic optimum. The RTO system provides a link between high-level planning and scheduling and regulatory control. RTO is usually performed on the basis of a rigorous, nonlinear process model. However, the model will never represent the true behavior of the process exactly, and so the optimization, which typically converges to the model optimum, will not be optimal for the real plant; in addition, the computed operating point may violate the constraints. Several approaches have been developed in the past decade to cope with this problem, which include parameter adaptation, gradient correction (called modifier adaptation) or direct search using only the observed plant behavior. These approaches all have certain drawbacks and limitations, in particular it cannot be guaranteed that the constraints are met at each iteration or convergence is slow. This project investigates ways of modifying and combining different approaches to come up with an improved data- and model-based RTO scheme. The goal is the development of an RTO scheme that implements fast convergence to the true plant optimum through the use of a model of realistic accuracy (i.e. without excessive effort for model building) and measured data.