Existing Empirical Kinetic Models in Biochemical Methane Potential (BMP) Testing, Their Selection and Numerical Solution

Pererva, Yehor; Miller, Charles D.; Sims, Ronald C.

doi:10.3390/w12061831

Cited by 33 publications

(22 citation statements)

References 61 publications

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“…Others employed continuously or semicontinuously operated digesters (Section 3.4). Kinetic data from experimental studies are sometimes used in modelling to estimate potential values, as reviewed by Pererva et al [20] and Raposo et al [21]; these estimates may be presented with or without experimental values, but only experimental data are considered here.…”

Section: Overview On Types Of Testing Methods Appliedmentioning

confidence: 99%

“…The availability of proprietary systems from various suppliers has been making BMP tests more popular in the last five years, because such solutions offer ready-to-use equipment with pre-defined specifications. Pererva et al [20] lists several types of systems available (including YieldMaster, Nautilus BMP, Anaero Technology, AMPTS II). Most users have digested wastes, but some have studied crop-based materials.…”

Section: Methane Potentials Obtained In Bmp and Long Retention Batch Testsmentioning

confidence: 99%

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Estimating the Methane Potential of Energy Crops: An Overview on Types of Data Sources and Their Limitations

et al. 2021

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As the anaerobic digestion of energy crops and crop residues becomes more widely applied for bioenergy production, planners and operators of biogas plants, and farmers who consider growing such crops, have a need for information on potential biogas and methane yields. A rich body of literature reports methane yields for a variety of such materials. These data have been obtained with different testing methods. This work elaborates an overview on the types of data source available and the methods that are commonly applied to determine the methane yield of an agricultural biomass, with a focus on European crops. Limitations regarding the transferability and generalisation of data are explored, and crop methane values presented across the literature are compared. Large variations were found for reported values, which can only partially be explained by the methods applied. Most notably, the intra-crop variation of methane yield (reported values for a single crop type) was higher than the inter-crop variation (variation between different crops). The pronounced differences in reported methane yields indicate that relying on results from individual assays of candidate materials is a high-risk approach for planning biogas operations, and the ranges of values such as those presented here are essential to provide a robust basis for estimation.

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Section: Overview On Types Of Testing Methods Appliedmentioning

confidence: 99%

Section: Methane Potentials Obtained In Bmp and Long Retention Batch Testsmentioning

confidence: 99%

Estimating the Methane Potential of Energy Crops: An Overview on Types of Data Sources and Their Limitations

et al. 2021

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“…En el modelo de aumento exponencial máximo, de la tabla 2, "a" es el acumulado de rendimiento de metano, y el coeficiente de tasa de hidrólisis "B" es el factor limitante para el proceso de biodegradación anaeróbica (Elagroudy et al, 2020). En los modelos Gompertz y Richards modificados (tabla 2) la tasa máxima de producción de biogás "b" está dada por la pendiente de la línea tangente al punto de inflexión, ya que no se forma gas durante la fase lag (Pererva et al, 2020). El tiempo de la fase lag "c" es la intersección del eje x de esta pendiente.…”

Section: Materiales Y Métodosunclassified

“…Así también, algunos investigadores llevaron a cabo modelos matemáticos en sus estudios para obtener parámetros cinéticos de la DA (Zwietering et al, 1990;Altaş, 2009;Li et al, 2012;Ware y Power, 2017;Nguyen et al, 2019;Almomani y Bhosale, 2020;Pererva et al, 2020;Tian et al, 2020;Elagroudy et al, 2020;Li et al, 2020). Los modelos matemáticos generalmente se refieren a la forma algebraica canónica, con coeficientes adimensionales, o se basan en ecuaciones que describen la dependencia de la velocidad de las reacciones químicas en función de la concentración de sustrato (Pererva et al, 2020). Se ha encontrado que, para mejorar el rendimiento de los digestores y el conocimiento del proceso, se requiere un modelado dinámico adecuado de la DA (Elagroudy et al, 2020).…”

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Modelación de la generación de metano a partir de suero lácteo y excreta de ganado en codigestión anaerobia

López-Aguilar

Barrón

Franco

et al. 2021

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Introducción: la producción de biogás es una alternativa sostenible para el aprovechamiento energético de los residuos orgánicos. Esta investigación presenta el estudio y modelación de la producción de biogás, generada por la codigestión de excreta de vaca y suero de leche. Método: se realizaron experimentos de Digestión Anaerobia (DA), utilizando reactores herméticos, a temperatura ambiente monitoreados por 75 días consecutivos con base en el método de potencial bioquímico de metano (BMP). Se utilizaron los modelos logísticos: exponencial de crecimiento máximo Gompertz y Richards modificados para representar la cinética del fenómeno de producción de metano. Resultados y discusiones: se determinó la tasa máxima de producción de metano, la duración de la fase lag, y el potencial de producción de metano acumulado. Se comparó la suma de cuadrados residual y el coeficiente de correlación para identificar el modelo matemático que mejor describe el fenómeno. Se encontró que existe un potencial para la generación de biogás y el aprovechamiento de los residuos experimentados en codigestión. Conclusiones: la experimentación y modelación matemática permitió describir el fenómeno de la producción de biogás, con residuos de la industria láctea y de los sistemas pecuarios. Los resultados de la investigación presentan el potencial de incentivar la producción de energía, en el sector agropecuario regional, a partir de la tecnología DA aprovechando los residuos de la industria láctea bajo el concepto de economía circular.

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“…It requires identification by experimental data analysis of three parameters which have not necessarily biological meanings. The logistic expression has been artificially modified in order to give biological meanings to its three parameters (Crescenzo and Paraggio, 2019;Gerber and Span, 2008;Lemon et al, 2006;Pererva et al, 2020;Van et al, 2018). This paper intends to present essentially two main contributions.…”

Section: Introductionmentioning

confidence: 99%

Determination of Logistic Model Parameters From AM2 Model for Methane Production for Batch Bioreactors

Zaatri

2021

EUR J SUSTAIN DEV RES

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Modeling the estimation of cumulated methane production generated from batch anaerobic bioreactors is of paramount importance. In this context, there are two main modeling approaches. The first approach is based on developing mathematical expressions representing the processes involved in the bioreactors. The AM1 model is known as the most complete one. However, it is a complicated model as it requires about 80 parameters to be tuned. A model named AM2 which is a simplified version of AM1 has also been developed. It is based on only two microbial growth processes and requires only 13 parameters. Nevertheless, both AM1 and AM2 models do not provide explicit mathematical expressions that enable to estimate the temporal evolution of methane production with respect to the parameters involved in the considered models. These models are much more useful for simulations and graphical visualizations of the dynamical behavior of the state variables including methane production. On the other hand, the second approach suggests semi-empirical or data-driven models which are based on simple explicit mathematical expressions that provide an estimate of the cumulated methane production (Logistic model, Gompertz model, etc.). But, this type of models require the identification of few parameters which are extracted from experimental data. Usually, they are simplistic and use only one growth process and thus cannot exhibit the influence of the many parameters involved in such complex dynamic biotechnological systems.On the other hand, this expression resembles formally to the semi-empiric logistic model. However, the equivalent parameters of the proposed expression as self-defined by the parameters of the AM2 model and do not require experimental data to be identified as it is for the semi-empiric logistic model. Moreover, by comparing the proposed logistic model to the semi-empiric logistic model, an identification of the parameters of the semiempiric model is linked to the parameters of the AM2 model, providing more insight into the methane production. This can be considered as a second contribution of this paper.

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Existing Empirical Kinetic Models in Biochemical Methane Potential (BMP) Testing, Their Selection and Numerical Solution

Cited by 33 publications

References 61 publications

Estimating the Methane Potential of Energy Crops: An Overview on Types of Data Sources and Their Limitations

Estimating the Methane Potential of Energy Crops: An Overview on Types of Data Sources and Their Limitations

Modelación de la generación de metano a partir de suero lácteo y excreta de ganado en codigestión anaerobia

Determination of Logistic Model Parameters From AM2 Model for Methane Production for Batch Bioreactors

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