Affiliation:
1. Grupo de Investigación en Aplicación de Nuevas Tecnologías (GIANT), Departamento de Ingeniería Química Universidad Nacional de Colombia Campus La Nubia Manizales Colombia
2. PROSYS Research Centre, Department of Chemical and Biochemical Engineering Technical University of Denmark (DTU) Kgs. Lyngby Denmark
3. Grupo de Procesos Químicos, Catalíticos y Biotecnológicos, Departamento de Ingeniería Química Universidad Nacional de Colombia Campus La Nubia Manizales Colombia
Abstract
AbstractBACKGROUNDGluconic acid production by a glucose oxidase (GOx) and catalase (CAT) system has been proposed. However, the bioprocess optimal design and operation are limited by the lack of kinetic models of GOx involving oxygen as substrate. Herein, the GOx‐CAT system is modeled considering a continuous oxygen supply at a bioreactor lab scale. Initially, experiments and modeling for parameter estimations of and the individual enzymes were conducted. Then, experiments and modeling for the GOx‐CAT system were performed for final model tuning. Additionally, the model quality was evaluated, allowing for a deeper understanding of the system phenomenology.RESULTSFrom the oxygen transport model tuning, a highly accurate estimation was obtained (>0.98 and confidence interval <2%). The highest oxygen transport rate was obtained for the combined system Buffer‐antifoam‐GOx‐CAT that was 2.5 times larger than that obtained for DI water. Kinetic models for the individual enzymes were very accurate and the parameters were fully identifiable. For the integrated system, the gluconic acid evolution was properly predicted and there was a loss of predictive power for the oxygen model. Parameter identifiability analysis showed that and interpretability was compromised. However, the sensitivity analysis indicated that the model must not be simplified.CONCLUSIONHerein its has been demonstrated that is a complex parameter to be estimated for multi‐enzymatic systems due to its dependency on medium composition, particularly with GOx. Nonetheless, based on the acceptable model predictive power, the calculated parameters could be used for studies on the process design phase. © 2023 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
Funder
Danmarks Tekniske Universitet
COLCIENCIAS
Universidad Nacional de Colombia, Sede Manizales
Subject
Inorganic Chemistry,Organic Chemistry,Pollution,Waste Management and Disposal,Fuel Technology,Renewable Energy, Sustainability and the Environment,General Chemical Engineering,Biotechnology