Model predictive control of purple bacteria in raceway reactors: Handling microbial competition, disturbances, and performance

Ali Moradvandi*, Bart De Schutter, Edo Abraham, Ralph E.F. Lindeboom

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Purple Phototrophic Bacteria (PPB) are increasingly being applied in resource recovery from wastewater. Open raceway-pond reactors offer a more cost-effective option, but subject to biological and environmental perturbations. This study proposes a hierarchical control system based on Adaptive Generalized Model Predictive Control (AGMPC) for PPB raceway reactors. The AGMPC uses simple linear models updated adaptively to project the complex process dynamics and capture changes. The hierarchical approach uses the AGMPC controller to optimize PPB growth as the core of the system. The developed supervisory layer adjusts set-points for the core controller based on two operational scenarios: maximizing PPB concentration for quality, or increasing yield for quantity through effluent recycling. Lastly, due to competing PPB and non-PPB bacteria during start-up phase, an override strategy for this transition is investigated through simulation studies. The Purple Bacteria Model (PBM) simulates this process, and simulation results demonstrate the control system's effectiveness and robustness.

Original languageEnglish
Article number108981
Number of pages17
JournalComputers and Chemical Engineering
Volume194
DOIs
Publication statusPublished - 2024

Keywords

  • Hierarchical decision-making control
  • Model predictive control
  • Override control
  • Purple bacteria
  • Supervisory control

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