Enhancing industrial microalgae production through Economic Model Predictive Control

TL;DR

This paper introduces an Economic Model Predictive Control framework to optimize industrial microalgae production, addressing biological nonlinearities and climate variability for improved economic and operational performance.

Contribution

It proposes a novel economic MPC approach tailored for microalgae systems, demonstrating its effectiveness over traditional control methods under varying conditions.

Findings

Achieves economic optimization of microalgae production

Ensures dynamic stability during process operation

Outperforms non-optimized industrial processes

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.