Real-Time Optimization via Modifier Adaptation of Closed-Loop Processes using Transient Measurements

Jack Speakman, Gregory Francois

Research output: Contribution to journalArticlepeer-review

Abstract

Real-time optimization (RTO) has the ability to boost the performance of a process whilst satisfying the constraints by using process measurements, driving the operating conditions towards optimality. Modifier adaptation (MA) is a methodology of RTO which can find the optimal operating point of a process even in the presence of plant model mismatch. This work presents an extension to MA through the combination of two established frameworks, allowing for the optimization of a controlled process using transient measurements whilst using a steady-state open-loop model. In addition, an approach for model-based gradient estimation, despite the mismatch between the degrees of freedom of the closed-loop plant and the available open loop model is suggested that does not necessitate amending the model. The proposed scheme is illustrated on a case study of a CSTR and a distillation column, detailing how the gradient can be estimated.
Original languageEnglish
Article number106969
JournalComputers and Chemical Engineering
Volume140
Early online date13 Jun 2020
DOIs
Publication statusPublished - 2 Sep 2020

Keywords

  • Real-time optimization
  • Modifier adaptation
  • Transient measurements
  • Plant-model mismatch
  • Model-based gradient estimation

Fingerprint Dive into the research topics of 'Real-Time Optimization via Modifier Adaptation of Closed-Loop Processes using Transient Measurements'. Together they form a unique fingerprint.

Cite this