Some considerations about mathematical models and their application to anaerobic digestion
DOI:
https://doi.org/10.59169/pentaciencias.v6i2.1030Keywords:
application; anaerobic digestion; mathematical modelsAbstract
The objective of this work is to explore some key considerations related to the mathematical models applied to anaerobic digestion, which is achieved from a bibliographic research on the mathematical models and the deductive and inductive methods, which allowed us to reach the following conclusions: Mathematical models play a crucial role in understanding and predicting the processes associated with anaerobic digestion. From the characterization of substrate degradation kinetics to the simulation of the dynamic behavior of anaerobic reactors, these models offer a powerful tool for researchers and professionals in the field. The diversity of approaches and techniques used in formulating mathematical models reflects the inherent complexity of anaerobic digestion. From simple kinetic models based on Monod equations to more complex models that take into account microbial population dynamics and mass and energy transfer, there is a range of options available to suit different contexts and research objectives. And the practical applications of mathematical models in anaerobic digestion are numerous and varied. From optimizing the design and operation of wastewater treatment plants to evaluating the potential for biogas production from different substrates, these models offer valuable tools for making informed decisions in the environmental and energy spheres.
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Ayawei, N., Ebelegi, A. N., & Wankasi, D. (2017). Modelling and interpretation of adsorption isotherms. Journal of Chemistry, 2017. https://doi.org/10.1155/2017/3039817
Brouers, F., & Al-Musawi, T. J. (2015). On the optimal use of isotherm models for the characterization of biosorption of lead onto algae. Journal of Molecular Liquids, 212, 46-51. doi: 10.1016/j.molliq.2015.08.054
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
Edeline, F. (1980). L'epuratuion biologique des eaux residuaires. Théorie et technologie. Belgium: Cebdic-Liege.
Edgar, T. F. & Himmelblau, D. M. (1989). Optimization of Chemical Processes.
Elmorsi, T. M. (2011). Equilibrium isotherms and kinetic studies of removal of methylene blue dye by adsorption onto miswak leaves as a natural adsorbent. Journal of Environmental Protection, 2(6), 817.
Gershenfeld, N. (1999). The nature of mathematical modeling. Cambridge university press: Cambridge.
Hanna, O. T., & Sandall, O. C. (1995). Computerization Methods in Chemical Engineering. Prentice-Hall International, New Jersey, NH, USA.
Jeyaseelan, S. (1997). A simple mathematical model for anaerobic digestion process. Water science and technology, 35(8), 185-191. doi: 10.1016/S0273-1223(97)00166-2
Kapoor, A., & Yang, R. T. (1989). Correlation of equilibrium adsorption data of condensible vapours on porous adsorbents. Gas Separation & Purification, 3(4), 187-192. doi: 10.1016/0950-4214(89)80004-0
Kumar, K. V., & Sivanesan, S. (2006). Pseudo second order kinetics and pseudo isotherms for malachite green onto activated carbon: comparison of linear and non-linear regression methods. Journal of Hazardous Materials, 136(3), 721-726. doi: 10.1016/j.jhazmat.2006.01.003
Kundu, S., & Gupta, A. K. (2006). Arsenic adsorption onto iron oxide-coated cement (IOCC): regression analysis of equilibrium data with several isotherm models and their optimization. Chemical Engineering Journal, 122(1-2), 93-106. doi: 10.1016/j.cej.2006.06.002
Kythreotou, N., Florides, G., & Tassou, S. A. (2014). A review of simple to scientific models for anaerobic digestion. Renewable Energy, 71, 701-714. doi: 10.1016/j.renene.2014.05.055
McCarty, P. L., & Mosey, F. E. (1991). Modelling of anaerobic digestion processes (a discussion of concepts). Water Science and Technology, 24(8), 17-33. doi: https://doi.org/10.2166/wst.1991.0216
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the society for Industrial and Applied Mathematics, 11(2), 431-441. doi: 10.1137/0111030
Ng, J. C. Y., Cheung, W. H., & McKay, G. (2002). Equilibrium studies of the sorption of Cu (II) ions onto chitosan. Journal of Colloid and Interface Science, 255(1), 64-74. doi: 10.1006/jcis.2002.8664
Ng, J. C. Y., Cheung, W. H., & McKay, G. (2003). Equilibrium studies for the sorption of lead from effluents using chitosan. Chemosphere, 52(6), 1021-1030. doi: 10.1016/S0045-6535(03)00223-6
Taylor, R. (1990). Interpretation of the correlation coefficient: a basic review. Journal of diagnostic medical sonography, 6(1), 35-39. doi: 10.1177/875647939000600106
Theivarasu, C., & Mylsamy, S. (2011). Removal of malachite green from aqueous solution by activated carbon developed from cocoa (Theobroma Cacao) shell-A kinetic and equilibrium studies. Journal of Chemistry, 8(S1), S363-S371.
Velázquez-Martí, B., Meneses-Quelal, O. W., Gaibor-Chavez, J., & Niño-Ruiz, Z. (2018). Review of mathematical models for the anaerobic digestion process. In Biogas. IntechOpen. doi: http://dx.doi.org/10.5772/intechopen.80815
Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
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