Modelling methane production kinetics of complex poultry slaughterhouse wastes using sigmoidal growth functions

Ware, Aidan; Power, Niamh

doi:10.1016/j.renene.2016.11.045

Cited by 169 publications

(108 citation statements)

References 21 publications

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“…It uses the Logistic, Gompertz and Richards models (Table 4 and Equations 1, 2 and 3) to determine the cumulative methane production from the volume of methane produced by a mass of substrate introduced into the digester and the final time of digestion. These three models all agree with experience as mentioned in the references (Ware and Power, 2017;Pavlostathis and Giraldo, 1991;Altas, 2009;Manjula and Mahanta, 2014;Li et al, 2012). However, the results obtained by Gompertz and Richards models are similar and give a better correlation coefficient than that obtained from the Logistic model (Ware and Power, 2017;Altas, 2009).…”

Section: Kinetics Of Productionsupporting

confidence: 87%

“…The evaluation of methane productivity is increasingly recognized as a necessary parameter for determining the productivity of a substrate. The determination technique called biochemical methane potential (BMP) provides a range of information on the methanogen potential (Ware and Power, 2017). The BMP test is a respirometric test to determine the amount of methane produced under normal conditions of temperature and pressure, knowing the amount of waste (Lesteur et al, 2010).…”

Section: Methane Productivity Of Substratesmentioning

confidence: 99%

“…As compared to experimental result (BMPexp), a thorough knowledge of the organic composition of a substrate is necessary to determine the methane productivity as well the best configuration of co-digestion with saving of time and cost (Nielfa et al, 2015). Ware and Power (2017) studied the modeling of kinetic methane production of complex poultry slaughterhouse waste using four sinusoidal growth functions (Logistic, Gompertz, Richards and Stannard). Gompertz and Logistic models for three parameter present limitations for complex substrates.…”

Section: Introductionmentioning

confidence: 99%

“…Gompertz and Logistic models for three parameter present limitations for complex substrates. When it comes to complex substrates, the Richards model introduces a fourth parameter (Shape coefficient of the curve) that allows a better correlation with the experimental curve (Ware and Power, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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 Productionsupporting

confidence: 87%

Section: Methane Productivity Of Substratesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ali¹,

Dia²,

Bilal³

et al. 2018

Int. J. Phys. Sci.

View full text Add to dashboard Cite

show abstract

“…Ideal input and output values are obtained with respect to the structured models and values from wastewater processing that is expected to enhance biofuel quantity and quality-wise. [43] Modified sigmoidal bacterial growth curve relations using nonlinear least square regression assessment were employed to kinetically evaluate CH 4 production. Though this method comprising Richards, logistics, Gompertz model is suitable in case with high fatty waste digestion, it becomes complex in solving inflection points and in such case, this model is further extended.…”

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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Effect of the co‐digestion of agricultural lignocellulosic residues with manure from South American camelids

Meneses-Quelal

Gaibor‐Chávez

Niño

2021

Biofuels Bioprod Bioref

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This study aims to evaluate the effects of the co‐digestion of agricultural residues with manure from camelids from the Andean zone. Different combinations of llama manure (LM) and vicuñas (VM) were made with amaranth (AS), quinoa (QS), and wheat (WS) residues. They were fermented using sewage sludge as inoculum. The co‐digestion was evaluated under mesophilic conditions for 40 days. The ratios of volatile substances of substrate / co‐substrate evaluated were 0:100; 25:75; 50:50, 75:25, and 100:0. Two substrate / inoculum ratios (SIR 1:1 and SIR1:2) were also evaluated. The results indicate that the maximum methane accumulation rate is obtained in SIR 1:1 for a VM‐AS ratio (25:75) with 540 mL/g volatile solid (VS). In general, the results did not increase with the increase in inoculum; rather, the tendency to improve methane yield is associated with an increase in the amount of agricultural residues, mainly AS. Regarding the kinetic modeling, the transfer model is the one that best adjusted the predicted values to those observed with an r2 between 0.991 and 0.999, and an RMSE value between 2.06 and 13.62 mL/g (volatile solid) VS. Finally, all the trials presented synergistic effects in their co‐digestion except the digesters formed by LM‐AS, LM‐QS and LM‐WS of SIR 1:2. These presented antagonistic effects in which the addition of the co‐substrate generated competition with the substrates, reducing methane production. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

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Modelling methane production kinetics of complex poultry slaughterhouse wastes using sigmoidal growth functions

Cited by 169 publications

References 21 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

Effect of the co‐digestion of agricultural lignocellulosic residues with manure from South American camelids

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