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PhD defence Juan Camilo Acosta Pavas

Juan Camilo Acosta Pavas

Thèse intitulée : Modeling and dynamic optimization of biomethanation: dynamic and multiphysics study of biological couplings and gas-liquid transfers to optimize the operation of industrial reactors

Travaux dirigés par Jérôme Morchain et César Arturo Aceves Lara

PhD defence lundi 11 septembre 2023 at 2:00 pm.
Lieu :   135 Avenue de Rangueil, 31400 Toulouse
Salle : 401

Jury composition: 

Jérôme MORCHAIN

Directeur de thèse

César Arturo ACEVES LARA

Co-directeur de thèse

Jean-Philippe STEYER

Rapporteur

Céline CASENAVE

Rapporteure

Arnaud COCKX

Examinateur

Carlos Eduardo ROBLES RODRIGUEZ

Examinateur

Oscar Andrés PRADO RUBIO

Examinateur

David GRIOL BARRES

Examinateur

 

Résumé :  

This thesis aims at studying biological methanation to find the optimal conditions to produce high-purity biomethane as a value-added product. The objective is addressed from a modeling point of view, based on the use of model-based control strategies and data-driven soft sensors. A bibliography synthesis was carried out to set the theoretical framework that includes dynamic models, control strategies, and monitoring tools applied to biological methanation. An extension of the Anaerobic Digestion Model No.1 (ADM1_ME) was proposed to describe the dynamics of the biological methanation process with the use of syngas (H2, CO2, and CO) as substrate. The variation of the volumetric mass transfer coefficient is considered as a function of two types of reactors, a bubble column reactor (BCR) and a Continuous Stirred Tank Reactor (CSTR). The ADM1_ME was accurately calibrated and validated in different operating conditions using experimental data from the literature. A Multi-Objective Dynamic Optimization (MODO) strategy was proposed to optimize the biological methanation performance. The MODO strategy was designed to consider three different objective functions to maximize: (i) yield  and productivity  of methane, (ii)  and  simultaneously complemented by a switch to maximize acetate yields  and productivities , and (iii) economic optimality in terms of  and . The results demonstrated the feasibility of the MODO strategy and its robustness to switch between products of interest and the key role of the manipulated variables (i.e., inlet liquid and gas flow rates) in the biological methanation process. Furthermore, data-driven soft sensors were applied to detect deviations from the optimal operation points when disturbances occurred in the manipulated variables. Specifically, Support Vector Machine (SVM) showed promising results and a potential application by using 2D visualizations constructed by pair of features.

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