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Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors

This paper presents the integration of active disturbance rejection control (ADRC) with soft sensors for enhancing the composition control performance in a distillation column. Static and dynamic soft sensors are developed to estimate the top and bottom product compositions using multiple tray tempe...

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Detalles Bibliográficos
Autores principales: Al Kalbani, Fahad, Zhang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861002/
https://www.ncbi.nlm.nih.gov/pubmed/36679817
http://dx.doi.org/10.3390/s23021019
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author Al Kalbani, Fahad
Zhang, Jie
author_facet Al Kalbani, Fahad
Zhang, Jie
author_sort Al Kalbani, Fahad
collection PubMed
description This paper presents the integration of active disturbance rejection control (ADRC) with soft sensors for enhancing the composition control performance in a distillation column. Static and dynamic soft sensors are developed to estimate the top and bottom product compositions using multiple tray temperatures. In order to cope with the collinearity issues in tray temperature measurements, static and dynamic principal component regression is used in developing the soft sensors. The soft sensor outputs are introduced as the feedback signals to ADRC. This control scheme is termed as “inferential ADRC control”. Static control offsets are eliminated through mean updating in the soft-sensor models. The effectiveness of the proposed control scheme is demonstrated on a benchmark simulated methanol-water distillation column.
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spelling pubmed-98610022023-01-22 Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors Al Kalbani, Fahad Zhang, Jie Sensors (Basel) Article This paper presents the integration of active disturbance rejection control (ADRC) with soft sensors for enhancing the composition control performance in a distillation column. Static and dynamic soft sensors are developed to estimate the top and bottom product compositions using multiple tray temperatures. In order to cope with the collinearity issues in tray temperature measurements, static and dynamic principal component regression is used in developing the soft sensors. The soft sensor outputs are introduced as the feedback signals to ADRC. This control scheme is termed as “inferential ADRC control”. Static control offsets are eliminated through mean updating in the soft-sensor models. The effectiveness of the proposed control scheme is demonstrated on a benchmark simulated methanol-water distillation column. MDPI 2023-01-16 /pmc/articles/PMC9861002/ /pubmed/36679817 http://dx.doi.org/10.3390/s23021019 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Al Kalbani, Fahad
Zhang, Jie
Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors
title Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors
title_full Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors
title_fullStr Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors
title_full_unstemmed Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors
title_short Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors
title_sort inferential composition control of a distillation column using active disturbance rejection control with soft sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861002/
https://www.ncbi.nlm.nih.gov/pubmed/36679817
http://dx.doi.org/10.3390/s23021019
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