RT info:eu-repo/semantics/doctoralThesis
T1 Development of modeling and control strategies in microalgae-bacteria photobioreactors for wastewater treatment
A1 Bausa Ortiz, Irina
A2 Universidad de Valladolid. Escuela de Doctorado
K1 Aguas residuales
K1 Modeling
K1 Modelado
K1 State estimation
K1 Estimación de estados
K1 Wastewater treatment
K1 Tratamiento de aguas residuale
K1 Predictive control
K1 Control predictivo
K1 3308 Ingeniería y Tecnología del Medio Ambiente
AB Population growth and industrialization have resulted into a substantial increase in wastewater production, thereby establishing water purification as a primary concern on a global scale. In this context, microalgae-bacteria based wastewater treatment has emerged as a solution for wastewater treatment and nutrient recovery at a low-energy demand. The increasing number of microalgae-based applications demands the development of model-based information and decision support systems that can deal with their complex behavior. Objectives:The present thesis focuses on the proposal of models and estimation methods in novel facilities for wastewater treatment using microalgae and bacteria, as well as the proposal of state estimation and model-based control strategies for these facilities. To this end, the modeling of anoxic-aerobic photobioreactor configurations is proposed, as well as a library of components that allows the reuse of models across diverse applications. The objective of the present study is to estimate states, parameters, and uncertainties in a microalgae-bacteria wastewater treatment plant, thereby facilitating the design and implementation of an online economic model predictive controller.Results and Conclusions:Existing models in the literature were adapted to represent novel configurations of anoxic–aerobic algal–bacterial photobioreactors for wastewater treatment. Simulation results revealed the model's versatility in photobioreactors with one or two stages, including sedimentation and biomass recirculation. At the same time, the simulation results for two different plants confirmed the model's capability to reproduce the experimental data, even in the treatment of high-strength wastewaters. Parameter estimation allowed the determination of the values of the most influential parameters of the microalgae–bacteria process. In the same line, parameter estimation in the settler allows the estimation of the main parameters related to settleability properties, which are not well-established in microalgae–bacteria processes. The simulation results closely match the experimental data, further validating the accuracy of the model and its potential for further application in the system operation, control, and monitoring.The methodology for parameter estimation, when multiple outputs and parameters are involved in the optimization problem, was tested in a photobioreactor for wastewater treatment. This approach prevents convergence issues and facilitates a more optimal alignment between the experimental and simulated data.The library of model components for a microalgae-bacteria wastewater treatment plant was developed. The components developed can be reutilized for multiple simulations and allow the easy interconnection between plant components.The MHE technique was applied to a microalgae-based wastewater treatment process. The focus was on estimating multiple states and parameters concurrently in order to evaluate effluent water quality. This study employed an estimation model incorporating multiple states and parameters with a significant structural mismatch between the estimation model and the actual plant. Multi-rate measurements obtained from online measurements and analytical procedures enhanced the estimator's performance. Simulation results confirmed MHE's efficacy in the online estimation of pertinent microalgae-based wastewater treatment process variables. The MHE provided an estimation of the system’s states, parameters, and uncertainties, which were then used in the model of an economic predictive controller for an industrial wastewater treatment plant. The controller is designed to maximize biomass production despite process uncertainties.
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/82713
UL https://uvadoc.uva.es/handle/10324/82713
LA eng
NO Escuela de Doctorado
DS UVaDOC
RD 15-feb-2026