A nonlinear approach for input signal design with persistent excitation applied to pH modelling in microalgae raceway reactors

The fast pace of urbanization, population growth, and fossil fuel dependency have brought environmental challenges such as global warming and water contamination, pressing for solutions in greenhouse gas reduction and wastewater treatment. Microalgae cultivation offers promising results by assimilating CO2 and purifying wastewater. This study focuses on the identification of pH models in microalgae raceway reactors, essential for accurate control and optimization of growth conditions. A novel multisine-based persistent excitation approach combined with a range controller is proposed to enhance data quality and coverage across varying operating points, without violating output constraints. This method demonstrates improved operational stability and enriched dataset acquisition for model identification. Experimental results, conducted at the University of Almeria and IFAPA research center, confirm the method's effectiveness in generating reliable excitation data, supporting accurate pH model identification for industrial-scale applications.