Semnan University Press Journal of Heat and Mass Transfer Research 2345-508X 12 1 2025 05 01 Application of the ABC Algorithm in Parameter Estimation and Kinetic Model Selection in Propionic Fermentation 73 80 8959 10.22075/jhmtr.2024.31812.1478 EN Waldecleia Queiroz Da Costa Simulation and Computational Biology Laboratory, High Performance Computing Center, UFPA, Belém-PA, Brazil Miguel Fernando Saraiva Maia Faculty of Chemical Engineering, Federal University of Pará, Belém, PA, 66075-110, Brazil Nilton Pereira Da Silva Deparatament of Mechanical Engineering, Federal University of Amazonas, Manaus, AM, 69067-005, Brazil Deibson Silva Da Costa Faculty of Materials Engineering, Federal University of Pará, Belém, PA, 66075-110, Brazil Emerson Cardoso Rodrigues Faculty of Chemical Engineering, Federal University of Pará, Belém, PA, 66075-110, Brazil 0000-0002-0303-4578 Diego Cardoso Estumano Simulation and Computational Biology Laboratory, High Performance Computing Center, UFPA, Belém-PA, Brazil Journal Article 2023 09 17 A propionic acid fermentation process not only provides a more sustainable approach but also opens the door to propionic acid production capacity in regions with limited petroleum supplies. With fermentation, low-cost substrates can be used, such as residual biomass; reducing their concentration in nature. This process becomes interesting because from it propionic acid is considered natural. Several models have already been developed to describe the dynamics of components such as: Microorganism (biomass), nutrients (substrate), metabolites (product). However, a challenge is how to define the model that best represents the kinetic term, and therefore, there are several models for this modeling. This article's novelty is the application of the Bayesian technique (Computational Bayesian Approximation) to estimate parameters and simultaneously select the best model. Model validation was carried out considering propionic fermentation regarding experimental data from the literature, which selected the Andrews model as the best to predict the dynamic of biomass, substrate and product by the following parameters estimated = 0.192, <br />ms = 0.005, mp = 0.017. https://jhmtr.semnan.ac.ir/article_8959_7d2692bad747d4e966d2c9828f0c69c4.pdf