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Enhancing industrial microalgae production through Economic Model Predictive Control

Pablo Otálora, Sigurd Skogestad, José Luis Guzmán, Manuel Berenguel

TL;DR

This work addresses the economic optimization of open-pond microalgae production in a raceway photobioreactor under climate variability. It develops a first-principles, multi-physics model (biological growth, pH/DO dynamics, and heat transfer) and embeds it into an Economic Model Predictive Control framework to maximize biomass revenue while managing energy and material costs. Through simulation with real disturbance data and various horizon strategies, the study demonstrates that EMPC significantly outperforms traditional operations, with consistent biomass terminal constraints and adaptable performance across seasons; a key simplification—delegating pH control to a PI controller—yields large computational savings with minimal loss in economic performance. The results offer practical insights into operational priorities and support the adoption of optimal control for scalable, sustainable industrial microalgae production.

Abstract

The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably challenging. This paper presents an economic optimization framework aiming to enhance the operation of such systems. An Economic Model Predictive Controller is proposed, centralizing the decision making and achieving the theoretical optimal operation. Different scenarios with changing climate conditions are presented, and a comparison with the typical, non-optimized industrial process operation is established. The obtained results achieve economic optimization and dynamic stability of the process, while providing some insight into the priorities during process operation at industrial level, and justifying the use of optimal controllers over traditional operation.

Enhancing industrial microalgae production through Economic Model Predictive Control

TL;DR

This work addresses the economic optimization of open-pond microalgae production in a raceway photobioreactor under climate variability. It develops a first-principles, multi-physics model (biological growth, pH/DO dynamics, and heat transfer) and embeds it into an Economic Model Predictive Control framework to maximize biomass revenue while managing energy and material costs. Through simulation with real disturbance data and various horizon strategies, the study demonstrates that EMPC significantly outperforms traditional operations, with consistent biomass terminal constraints and adaptable performance across seasons; a key simplification—delegating pH control to a PI controller—yields large computational savings with minimal loss in economic performance. The results offer practical insights into operational priorities and support the adoption of optimal control for scalable, sustainable industrial microalgae production.

Abstract

The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably challenging. This paper presents an economic optimization framework aiming to enhance the operation of such systems. An Economic Model Predictive Controller is proposed, centralizing the decision making and achieving the theoretical optimal operation. Different scenarios with changing climate conditions are presented, and a comparison with the typical, non-optimized industrial process operation is established. The obtained results achieve economic optimization and dynamic stability of the process, while providing some insight into the priorities during process operation at industrial level, and justifying the use of optimal controllers over traditional operation.

Paper Structure

This paper contains 16 sections, 22 equations, 10 figures, 10 tables.

Table of Contents

  1. Introduction
  2. System description
  3. Microalgae raceway reactor
  4. First-principles model
  5. Biological model
  6. Dynamic model for pH and dissolved oxygen
  7. Thermal model
  8. Economic Model Predictive Control
  9. Results
  10. Simulation framework
  11. Standard operation (benchmark case)
  12. Cost function and constraints
  13. EMPC considerations
  14. EMPC results
  15. Disturbance prediction approaches
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Semi-industrial scale raceway photobioreactor located in the CIESOL research center at the IFAPA facilities.
  • Figure 2: Inputs (MVs), disturbances and outputs (CVs) of the system.
  • Figure 3: Average irradiance received by microalgae depending on the biomass concentration from Equation \ref{['eq:Iav']}.
  • Figure 4: Growth rate depending on the radiation and biomass concentration from Equations \ref{['eq:muiav']} and \ref{['eq:Iav']}.
  • Figure 5: Economic Model Predictive Control diagram.
  • ...and 5 more figures