RTO: An overview and assessment of current practice

The practice of implementing real-time optimization (RTO) using a rigorous steady-state model, in conjunction with model predictive control (MPC), dates back to the late 1980s. Since then, numerous projects have been implemented in refinery and chemical plants, and RTO has received significant attention in the industrial and academic literature. This history affords us the opportunity to assess the impact and success of RTO technology in the process industries. We begin with a discussion of the role RTO serves in the hierarchy of control and optimization decision making in the plant, and outline the key steps of the RTO layer and the coordination with MPC. Where appropriate, we point out the different approaches that have been used in practice and discuss the success factors that directly relate to the success of RTO within an organization. We also discuss alternative approaches that have been used to alleviate some of the challenges associated with implementing RTO and which may be appropriate for those unwilling to commit to the traditional RTO approach. Lastly, we provide suggestions for improvement to motivate further research.

► The current practice of real time optimization (RTO) based on a rigorous steady-state model is described.
► Challenges to successful RTO implementations include both technical and organizational issues.
► Alternate, simpler, formulations of RTO that have been implemented in industry typically target specific operational objectives.
► RTO may not be a feasible option for all companies but optimization has a role to play, even if limited to an offline model.
► Suggested research directions are provided.