[1]   Stowers, C. C., Cox, B. M., & Rodriguez, B. A., 2014. Development of an industrializable fermentation process for propionic acid production. Journal of Industrial Microbiology and Biotechnology, 41(5), pp.837-852.

[2]   Wang, Z., & Yang, S. T., 2013. Propionic acid production in glycerol/glucose co-fermentation by Propionibacterium freudenreichii subsp. shermanii. Bioresource technology, 137, pp. 116-123.

[3]   Monge, E. C., Levi, M., Forbin, J. N., Legesse, M. D., Udo, B. A., deCarvalho, T. N., & Gardner, J. G., 2020. High-throughput screening of environmental polysaccharide-degrading bacteria using biomass containment and complex insoluble substrates. Applied microbiology and biotechnology, 104, pp. 3379-3389.

[4]   Himmi, E. H., Bories, A., Boussaid, A., & Hassani, L., 2000. Propionic acid fermentation of glycerol and glucose by Propionibacterium acidipropionici and Propionibacterium freudenreichii ssp. shermanii. Applied Microbiology and Biotechnology, 53, pp. 435-440.

[5]   Pradhan, N., Dipasquale, L., d'Ippolito, G., Fontana, A., Panico, A., Lens, P. N.,& Esposito, G., 2016. Kinetic modeling of fermentative hydrogen production by Thermotoga neapolitana. International Journal of Hydrogen Energy, 41(9), pp. 4931-4940.

[6]   Miller, K. V., & Block, D. E., 2020. A review of wine fermentation process modeling. Journal of Food Engineering, 273, p.109783.

[7]   Waszkielis K., Białobrzewski I., Bułkowska K., 2022. Application of anaerobic digestion model No. 1 for simulating fermentation of maize silage, pig manure, cattle manure and digestate in the full-scale biogas plant. Fuel, 317, p. 123491, ISSN 0016-2361.

[8]   De Crescenzo C., Marzocchella A., Karatza D., Chianese S., Musmarra D., 2024. Autogenerative high-pressure anaerobic digestion modelling of volatile fatty acids: Effect of pressure variation and substrate composition on volumetric mass transfer coefficients, kinetic parameters, and process performance. Fuel, 358, Part A, p. 130144, ISSN 0016-2361.

[9]   Wang, J., & Wan, W., 2009. Kinetic models for fermentative hydrogen production: a review. International journal of hydrogen energy, 34(8), pp. 3313-3323.

[10] Zhao, M., Zhao, S., & Liu, F., 2023. Semi− Supervised Hybrid Modeling of the Yeast Fermentation Process. Machines, 11(1),p. 63.

[11] Marinho, C., Santos, A., Barreto, L., Saraiva, S., Carvalho, F., & Coêlho, d., 2018. avaliação cinética e modelagem matemática na fermentação propiônica. XXII congresso brasileiro de engenharia química, são paulo - sp

[12] Monod, J., 1949. The growth of bacterial cultures. Annual review of microbiology, 3(1), pp. 371-394.

[13] Andrews, J. F., 1968. A mathematical model for the continuous culture of micro-organisms utilizing inhibitory substrates. Biotechnol Bioeng.

[14] Nunes, K. G. P., Dávila, I. V. J., Amador, I. C. B., Estumano, D. C., & Féris, L. A., 2021. Evaluation of zinc adsorption through batch and continuous scale applying Bayesian technique for estimate parameters and select model. Journal of Environmental Science and Health, Part A, 56(11), pp. 1228-1242.

[15] Moura, C. H., Viegas, B. M., Tavares, M., Macedo, E., & Estumano, D. C., 2022. Estimation Of Parameters And Selection Of Models Applied To Population Balance Dynamics Via Approximate Bayesian Computational. Journal of Heat and Mass Transfer Research, 9(1), pp. 53-64.

[16] Nunes, K. G. P., Davila, I. V. J., Arnold, D., Moura, C. H. R., Estumano, D. C., & Féris, L. A., 2022. Kinetics and thermodynamic study of laponite application in caffeine removal by adsorption. Environmental Processes, 9(3), p. 47.

[17] Tavares, R., Santana Dias, C., Rodrigues Moura, C. H., Rodrigues, E. C., Viegas, B., Macedo, E., & Estumano, D. C., 2022. Parameter Estimation in Mass Balance Model Applied in Fixed Bed Adsorption Using the Markov Chain Monte Carlo Method. Journal of Heat and Mass Transfer Research, 9(2), pp. 219-232.

[18] Ferreira, J. R., Sena, A. P., Coutinho, J. P. D. S., Estumano, D. C., & Macêdo, E. N., 2023b. Fluid dynamics characterization of stirred-tank reactors via approximate Bayesian computational (ABC) for parameter estimation and model selection. Numerical Heat Transfer, Part A: Applications, pp. 1-18.

[19] Jurado-Davila, I. V., Schneider, I. A. H., Estumano, D., & Amaral Féris, L., 2023b. Phosphate removal using dolomite modified with ultrasound: mathematical and experimental analysis. Journal of Environmental Science and Health, Part A, 58(5), pp. 469-482.

[20] Mitchell, D. A., Krieger, N., & Estumano, D. C., 2023. Estimation of selectivities in the lipase-catalyzed esterification of trimethylolpropane with fatty acids. Biochemical Engineering Journal, 198, p.109024.

[21] Ferreira, J. R., Senna, A. P., Macêdo, E. N., & Estumano, D. C., 2023a. Aerobic bioreactors: A Bayesian point of view applied to hydrodynamic characterization and experimental evaluation of tracers. Chemical Engineering Science, 277, p.118850.

[22] Jurado-Davila, V., De Oliveira, J. T., Estumano, D., & Féris, L. A., 2023a. Fixed-bed column for phosphate adsorption combining experimental observation, mathematical simulation, and statistics: Classical and Bayesian. Separation and Purification Technology, 317, p.123914.

[23] Toffoli de Oliveira, J., da Luz Arsufi, A. B., Cardoso Estumano, D., & Féris, L. A., 2023. Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design. Industrial & Engineering Chemistry Research, 62(18), pp. 7127-7137.

[24] Estumano, D. C., Hamilton, F. C., Colaco, M. J., Leiroz, A. J., Orlande, H. R. B., Carvalho, R. N., & Dulikravich, G. S., 2014. Bayesian estimate of mass fraction of burned fuel in internal combustion engines using pressure measurements. In Engineering Optimization IV-Proceedings of the 4th International Conference on Engineering Optimization (pp. 997-1004).

[25] Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P., 2009. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), pp. 187-202.

[26] Liepe, J., Kirk, P., Filippi, S., Toni, T., Barnes, C. P., & Stumpf, M. P., 2014. A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. Nature protocols, 9(2), pp. 439-456.

[27] Turner, B. M., & Van Zandt, T., 2012. A tutorial on approximate Bayesian computation. Journal of Mathematical Psychology, 56(2), pp. 69-85.

[28] Leuenberger, C., & Wegmann, D., 2010. Bayesian computation and model selection without likelihoods. Genetics, 184(1), pp. 243-252.

[29] Marjoram, P., Molitor, J., Plagnol, V., & Tavaré, S., 2003. Markov chain Monte Carlo without likelihoods. Proceedings of the National Academy of Sciences, 100(26), pp. 15324-15328.

[30] Silva, N. P., Loiola, B. R., Costa, J. M., &, Helcio, R.B., 2020. Approximate Bayesian Computation Applied to Model Selection and Parameter Calibration in Cell Proliferation. In 14th WCCM-ECCOMAS Congress 2020 (Vol. 1300).

[31] Pirt, S. J., 1965. The maintenance energy of bacteria in growing cultures. Proceedings of the Royal Society of London. Series B. Biological Sciences, 163(991), pp. 224-231.

[32] Bailey, J. E., & Ollis, D. F., 1986. Biochemical Engineering Fundamentals. McGraw-Hill.

[33] Mulchandani, A.; Luong, J. H. T., 1989. Microbial inhibition kinetics revisited. Enzyme and Microbial Technology, 11(2), p. 66-73.