Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, Er-Wei; Deng, Kaiwen; Bondinuba, Francis Kwesi

doi:10.1016/j.biortech.2016.12.045

Cited by 116 publications

(35 citation statements)

References 39 publications

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“…The model of artificial neuron networks presents the possibility to predict the production of methane, taking into account, different input parameters. The model is characterized by a high correlation coefficient and a very low mean squared error, which is consistent with the results of several studies in the literature (Kurtgoz et al, 2018;Antwi et al, 2017;Nair et al, 2016). The third category of models determines the daily methane production of a biogas plant.…”

Section: Analysis and Discussionsupporting

confidence: 82%

“…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%

“…Artificial neuron network (Kurtgoz et al, 2018;Antwi et al, 2017;Vijay et al, 2016) The Artificial Neuron Network consists of neurons connected to each other, with connections having digital weights that can be adjusted during training. ANN structures include an input layers, a hidden layers, and an output layers, each with its own neurons.…”

Section: Methane Production Ratementioning

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%

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: Analysis and Discussionsupporting

confidence: 82%

Section: Kinetics Of Productionmentioning

confidence: 99%

Section: Methane Production Ratementioning

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.

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“…Backpropagation (BP) algorithm was used to develop the ANN model [11]. The typical topology of BPANN (Backpropagation Artificial Neural Network) involves three layers: input layer,where the data are introduced to the network; hidden layer, where the data are processed; and output layer,where the results of the given input are produced [12].…”

Section: Backpropagationmentioning

confidence: 99%

Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Siregar

Wanto

2017

IJISTECH

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Application of Artificial Neural Networks in the Tertiary Treatment of Liquid Effluent with the Microalgae Chlorella vulgaris

et al. 2021

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Modeling of biotechnological processes is difficult due to their nonlinear characteristics. Therefore, an artificial neural network was developed as a viable option for the prediction of the main variables of the tertiary treatment with Chlorella vulgaris. The network was designed with a hyperbolic tangent activation function and trained using a set of experimental and simulated data. The Levenberg‐Marquardt algorithm was used to train the network; four input neurons (luminosity, ammonium ion, phosphate, and initial biomass concentrations) and two output neurons (ammonium ion and phosphate concentrations) were fixed. The network architecture with one hidden layer [4,7,2] was chosen because it presented the lowest mean square error of the test combined with high R2, indicating that the network provides a good model for use in real applications.

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Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network

Cited by 116 publications

References 39 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

Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

Application of Artificial Neural Networks in the Tertiary Treatment of Liquid Effluent with the Microalgae Chlorella vulgaris

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