Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

Nair, Vijay V.; Dhar, Hiya; Kumar, Sunil; Thalla, Arun Kumar; Mukherjee, Somnath; Wong, Jonathan W.C.

doi:10.1016/j.biortech.2016.03.046

Cited by 114 publications

(31 citation statements)

References 18 publications

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“…It consists of modified mathematical models to introduce biological parameters into the model. Numerous models have been used to evaluate cumulative methane production (Gioannis et al, 2009;Lo et al, 2010;Pavlostathis and Giraldo, 1991;Altas, 2009;Manjula and Mahanta, 2014;Li et al, 2012;Jagadish et al, 2012;Kafle and Chen, 2016;Kurtgoz et al, 2018;Antwi et al, 2017;Nair et al, 2016). Altas (2009) emphasized on the effect of heavy metal inhibitors (Cr, Cd, Ni and Zn) on anaerobic granular methane-producing sludge.…”

Section: Kinetics Of Productionmentioning

confidence: 99%

See 1 more Smart Citation

Theoretical models for prediction of methane production from anaerobic digestion: A critical review

Ali¹,

Dia²,

Bilal³

et al. 2018

Int. J. Phys. Sci.

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This work presents a critical analysis for three models group of methanogen potential prediction. The first group allows determination of the methane productivity of substrates, through three models (BMPthCOD, BMPthAtC and BMPthOFC). The BMPthCOD is suitable for a first approximation calculation. BMPthAtC and BMPthOFC are more accurate; however, require a complex characterization of substrates. The second models group predicts the cumulative methane production using seven models. The analysis shows that the Artificial Neuron Network (ANN) is more accurate; moreover, it allows carrying out an optimization of the cumulative methane production. The third group of models is particularly involved in the determination of daily flow of methane by a biodigester. The Hashimoto model, which uses the operating parameters, has been identified as the most suitable.

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Section: Kinetics Of Productionmentioning

confidence: 99%

“…A new model to predict the potential of the methane through anaerobic digestion exists in the literature (Kurtgoz et al, 2018;Antwi et al, 2017;Nair et al, 2016). Artificial Neuron Network (ANN) allows predicting the potential of methane production with the possibility of choosing the number of input parameters.…”

Section: Introductionmentioning

confidence: 99%

Theoretical models for prediction of methane production from anaerobic digestion: A critical review

Ali¹,

Dia²,

Bilal³

et al. 2018

Int. J. Phys. Sci.

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show abstract

“…It aids in performing comprehensive description of the arising complexities like predicting biogas production by sledge processing. [39] described the efficiency of bio reactor in determining pH, volatile solids and fatty acids and methane content during biogas emission from anaerobic degradation of organic waste loaded at <120 kg volatile solids/m 3 . The organic municipal garbage yielded 347 l methane in where the outcomes were statistically optimized using artificial neural model.…”

Section: Mathematical and Neuralmentioning

confidence: 99%

A Comprehensive analysis on Bioenergy Yield via Anaerobic Disintegration of Organic Matter

Diaz¹

2017

SIJST

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Solid and liquid waste disposal by anaerobic digestion is feasible enough to produce biofuel by reducing volume of sludge. The rate of waste conversion to biogas in activated sludge process has been increasing as it reduces negative environmental effect and stabilizes ecological balance. A brief overview of substrates that is suitable for biogas yield has been presented, where it is clear that the feedstock with higher total solid concentration results to increase biofuel generation potential. It intends to provide various anaerobic disintegration techniques that emphasized on bioenergy development. Further, the recommendations over future scope have also been dealt with.

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“…This model could provide appreciable results in predicting wall temperature for the range of operating conditions and helped for selecting the appropriate material for water‐wall tubes. Nair et al () proposed a NN‐based model to evaluate methane yield from biogas in a laboratory‐scale anaerobic bioreactor. The author investigated the performance of a laboratory‐scale anaerobic bioreactor to determine methane content in biogas yield from digestion of organic fraction of municipal solid waste.…”

Section: Introductionmentioning

confidence: 99%

“…This model could provide appreciable results in predicting wall temperature for the range of operating conditions and helped for selecting the appropriate material for water-wall tubes. Nair et al (2016) Nevertheless, each of the soft computing techniques has its merits and demerits. Performance of individual technique becomes limited with the increase of complexity of a problem and hence motivates for combining their merits in a common platform.…”

mentioning

confidence: 99%

Comparative analysis on the application of neuro‐fuzzy models for complex engineered systems: Case study from a landfill and a boiler

Meher

Behera²,

Rene

et al. 2017

Expert Systems

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This work aims at developing an explicit neuro‐fuzzy (NF) model to characterize complex engineered systems associated with high nonlinearity, uncertainties, and multivariable couplings. The NF model synergistically exploits the advantages of fuzzy belongingness of each input variable to all output variables and learning ability of neural networks. Owing to the inherent complexities associated with 2 complex engineered systems, a landfill and a boiler were selected to develop models that provide intelligent decisions for optimizing the operational parameters. Data compiled from field‐scale investigation/real plant operation involving various operating scenarios were used to develop the models. Predicting capability of the developed models was evaluated through the correlation coefficient and mean absolute percentage error values. Superiority of the proposed NF model to other similar models has been justified and demonstrated.

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Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor

Cited by 114 publications

References 18 publications

Theoretical models for prediction of methane production from anaerobic digestion: A critical review

Theoretical models for prediction of methane production from anaerobic digestion: A critical review

A Comprehensive analysis on Bioenergy Yield via Anaerobic Disintegration of Organic Matter

Comparative analysis on the application of neuro‐fuzzy models for complex engineered systems: Case study from a landfill and a boiler

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