From Real-Time Optimization Techniques to an Autopilot for Steady-State Processes

TL;DR

This paper reviews the evolution of real-time optimization (RTO) techniques and introduces an autopilot system aimed at fully automating steady-state process control in industrial plants, addressing decision-making under uncertainty.

Contribution

It presents novel methods that advance RTO towards complete automation of plant operations, integrating decision-making and control in steady-state processes.

Findings

Demonstrates improved automation capabilities for steady-state processes.

Shows robustness of the autopilot system under disturbances.

Provides a framework for integrating RTO into plant automation.

Abstract

Any industrial system goes along with objectives to be met (e.g. economic performance), disturbances to handle (e.g. market fluctuations, catalyst decay, unexpected variations in uncontrolled flow rates and compositions,...), and uncertainties about its behavior. In response to these, decisions must be taken and instructions be sent to the operators to drive and maintain the plant at satisfactory, yet potentially changing operating conditions. Over the past thirty years many methods have been created and developed to answer these questions. In particular, the field of Real-Time Optimization (RTO) has emerged that, among others, encompasses methods that allow the systematic improvement of the performances of the industrial system, using plant measurements and a potentially inaccurate tool to predict its behaviour, generally in the form of a model. Even though the definition of RTO can differ between authors, inside and outside the process systems engineering community, there is currently no RTO method, which is deemed capable of fully automating the aforementioned decision-making process. This thesis consists of a series of contributions in this direction, which brings RTO closer to being capable of a full plant automation. Keywords: Real-time optimization, Decision-making, Optimization, Reduced-order-model optimization, Autopilot for steady-state processes, Operational research.