MPC is multivariable control of process that relies on multi-input multi-output, dynamic model of the process. MPC models are usually empirical and are identified by step testing of the process. Such models are linear and are good representation of the process dynamic behavior around the normal operating conditions. Usually, MPC executes on a sub-minute frequency and continuously implements manipulated variable moves.
RTO also relies on a multi-dimensional model of the process. Traditionally, RTO models are first principles and are based on steady state conservation of mass, energy and momentum. Such models are non-linear and are good representation of the process steady state behavior over the entire operating region. Usually, RTO executes on a much slower frequency as a supervisory layer giving setpoints to MPC for implementation of the optimum.
The development and maintenance of two separate models for MPC and RTO is inherently inefficient. An alternate formulation of RTO that leverages MPC models is possible and much more efficient. One such approach and its application to industrial processes is presented in this paper.