@article{10.11648/j.ijepe.20200902.11,
  author = {Abiodun Suleiman Momodu and Tofunmi Dorcas Adepoju},
  title = {System Dynamics Model of the Kinetics of Biogas Production from Vegetal Matter},
  journal = {International Journal of Energy and Power Engineering},
  volume = {9},
  number = {2},
  pages = {22-28},
  doi = {10.11648/j.ijepe.20200902.11},
  url = {https://doi.org/10.11648/j.ijepe.20200902.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20200902.11},
  abstract = {The use of renewable energy sources including biomass for energy generation, to achieve diversification in energy production, has been found to be sustainable economically, financially and environmentally. Various energy production technologies exist by which biomass can be converted for energy generation. Such technologies include anaerobic digestion, gasification, thermal depolymerization, pyrolysis, fermentation, anaerobic digestion, amongst others. The focus of this study is on the use of anaerobic digestion technology. Anaerobic digestion is recognized as one of the best options for treating biomass as it helps to avoid CO2 emissions and run off of biomass. It is a natural process in which bacteria convert organic materials into biogas and fertilizer production in an environmentally friendly way. Anaerobic digestion is a series of sequential process including hydrolysis, acidogenesis, acetogenesis and methanogenesis. Different models have been applied to capture the characteristics of the anaerobic digestion process such as first-order model, Gompertz model and logistic model. However, Gompertz model is considered as the best model in describing the growth of animals and plants as well as the volume of bacteria. It is also used to describe the cumulative biogas production curve in batch digestion assuming that substrate levels limit growth in a logarithmic relationship. This study developed a System Dynamics model (SDM) for predicting biogas production (BP) in an anaerobic condition, based on Gompertz-Laird model. The objective is to describe the process of a System Dynamic (SD) model of two stage kinetics of BP. Primary data used were obtained from a laboratory experiment of BP using vegetal wastes, while secondary data were obtained from literature on studies using similar substrates. The Causal loop diagram generated, describes the anaerobic digestion (AD) process usually undergone by a substrate, while the Stock Flow diagram depicts the building blocks of the dynamic behavior of the same process. The developed SD model consists of two-level variables which depict the equations driving the AD process represented as hydrolysis-acidogenesis and acetogenesis-methanogenesis. The model results showed a significant lag phase between methanogenesis and fermentation stage, which was found to be linked to the inoculum-substrate ratio. The study conclusion includes: inoculum to substrate ratio affects BP; inconsistency of the experimental data caused by inhibition explains the variation observed between the empirical and simulated results.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - System Dynamics Model of the Kinetics of Biogas Production from Vegetal Matter
AU  - Abiodun Suleiman Momodu
AU  - Tofunmi Dorcas Adepoju
Y1  - 2020/04/29
PY  - 2020
N1  - https://doi.org/10.11648/j.ijepe.20200902.11
DO  - 10.11648/j.ijepe.20200902.11
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 22
EP  - 28
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20200902.11
AB  - The use of renewable energy sources including biomass for energy generation, to achieve diversification in energy production, has been found to be sustainable economically, financially and environmentally. Various energy production technologies exist by which biomass can be converted for energy generation. Such technologies include anaerobic digestion, gasification, thermal depolymerization, pyrolysis, fermentation, anaerobic digestion, amongst others. The focus of this study is on the use of anaerobic digestion technology. Anaerobic digestion is recognized as one of the best options for treating biomass as it helps to avoid CO2 emissions and run off of biomass. It is a natural process in which bacteria convert organic materials into biogas and fertilizer production in an environmentally friendly way. Anaerobic digestion is a series of sequential process including hydrolysis, acidogenesis, acetogenesis and methanogenesis. Different models have been applied to capture the characteristics of the anaerobic digestion process such as first-order model, Gompertz model and logistic model. However, Gompertz model is considered as the best model in describing the growth of animals and plants as well as the volume of bacteria. It is also used to describe the cumulative biogas production curve in batch digestion assuming that substrate levels limit growth in a logarithmic relationship. This study developed a System Dynamics model (SDM) for predicting biogas production (BP) in an anaerobic condition, based on Gompertz-Laird model. The objective is to describe the process of a System Dynamic (SD) model of two stage kinetics of BP. Primary data used were obtained from a laboratory experiment of BP using vegetal wastes, while secondary data were obtained from literature on studies using similar substrates. The Causal loop diagram generated, describes the anaerobic digestion (AD) process usually undergone by a substrate, while the Stock Flow diagram depicts the building blocks of the dynamic behavior of the same process. The developed SD model consists of two-level variables which depict the equations driving the AD process represented as hydrolysis-acidogenesis and acetogenesis-methanogenesis. The model results showed a significant lag phase between methanogenesis and fermentation stage, which was found to be linked to the inoculum-substrate ratio. The study conclusion includes: inoculum to substrate ratio affects BP; inconsistency of the experimental data caused by inhibition explains the variation observed between the empirical and simulated results.
VL  - 9
IS  - 2
ER  - 

Copy | Download