Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed loop control performance. This study addresses the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. A simulated monoclonal antibody production is sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples are interpolated into 15 minute intervals and a linear dynamic model is developed to predict the glucose concentration at 15 minute intervals. These predicted values are then used in a feedback control loop, using model predictive control or a conventional proportional and integral controller, to control glucose concentration at 15 minute sampling intervals. For setpoint tracking, model predictive control reduces the integral of absolute errors to 14600 from 15900 (with 1 hour sampling time) or 8.2% reduction. With adaptive model predictive control, the integral of absolute errors is reduced from 14500 (1 hour sampling time) to 14200 for stepoint tracking and from 13500 (1 hour sampling time) to 13300 for disturbance rejection. A final comparison demonstrates that the proposed method can also cope with random variations in the sampling time.
Author(s): Joynes L, Zhang J
Publication type: Article
Publication status: Published
Online publication date: 24/07/2022
Acceptance date: 22/07/2022
Date deposited: 22/07/2022
ISSN (electronic): 2227-9717
Publisher: MDPI AG
Altmetrics provided by Altmetric