<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Rapid growth of the human population has led to various problems, such as massive overload of wastewater treatment plants. Therefore, optimal control of these plants is a relevant subject. This contribution analyses control of a cascade of ten biochemical reactors using simulation results with the aim to design optimal and predictive control strategies and to compare the achieved control performance. The plant represents a complicated process with many variables involved in the model structure, reduced to the single-input and single-output system. The first implemented approach is linear offset-free model predictive control which provides the optimal input trajectory minimising a quadratic cost function. The second control strategy is robust model predictive control with similar features as model predictive control but including the uncertainty of the process. The final approach is generalised predictive control, mostly used in the industry because of its simple structure and sufficiently good control performance. All considered predictive controllers provide satisfactory control performance and remove the steady-state control error despite the constrained control inputs.</p>
</abstract>

Rapid growth of the human population has led to various problems, such as massive overload of wastewater treatment plants. Therefore, optimal control of these plants is a relevant subject. This contribution analyses control of a cascade of ten biochemical reactors using simulation results with the aim to design optimal and predictive control strategies and to compare the achieved control performance. The plant represents a complicated process with many variables involved in the model structure, reduced to the single-input and single-output system. The first implemented approach is linear offset-free model predictive control which provides the optimal input trajectory minimising a quadratic cost function. The second control strategy is robust model predictive control with similar features as model predictive control but including the uncertainty of the process. The final approach is generalised predictive control, mostly used in the industry because of its simple structure and sufficiently good control performance. All considered predictive controllers provide satisfactory control performance and remove the steady-state control error despite the constrained control inputs.

Predictive control of a cascade of biochemical reactors

Slovak University of Technology in Bratislava

Acta Chimica Slovaca

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Predictive control of a cascade of biochemical reactors"}

Rapid growth of the human population has led to various problems, such as massive overload of wastewater treatment plants. Therefore, optimal control of these...

Predictive control of a cascade of biochemical reactors

Published Online: May 24, 2021

Page range: 51 - 59

DOI: https://doi.org/10.2478/acs-2021-0007

Keywords
biochemical reactor, generalised predictive control, model predictive control, robust model predictive control, wastewater treatment

© 2021 Martin Mojto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Predictive control of a cascade of biochemical reactors

Published Online: May 24, 2021

Page range: 51 - 59

DOI: https://doi.org/10.2478/acs-2021-0007

Keywordsbiochemical reactor, generalised predictive control, model predictive control, robust model predictive control, wastewater treatment

© 2021 Martin Mojto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
biochemical reactor, generalised predictive control, model predictive control, robust model predictive control, wastewater treatment