Optimizing the process parameters to maximize biogas yield from anaerobic co-digestion of alkali-treated corn stover and poultry manure using artificial neural network and response surface methodology

Aklilu, Ermias Girma; Waday, Yasin Ahmed

doi:10.1007/s13399-021-01966-0

Cited by 20 publications

(8 citation statements)

References 41 publications

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“…The relative impact of the factors can be deduced with this equation when the coefficients of the factors are compared. The accuracy of a model is determined by the R 2 value; 0.6109 and 0.5547 were recorded in this work which are lower compared to 0.997, 0.995 and 0.995166 that were reported in the literature (Aklilu and Waday, 2021; Sathish and Vivekanandan, 2016; Tetteh et al, 2018) but in the same range with what was reported (0.698 and 0.6371) in another similar research (Abubakar and Ibrahim, 2021)…”

Section: Resultscontrasting

confidence: 51%

“…The relative impact of the factors can be deduced with this equation when the coefficients of the factors are compared. The accuracy of a model is determined by the R 2 value; 0.6109 and 0.5547 were recorded in this work which are lower compared to 0.997, 0.995 and 0.995166 that were reported in the literature (Aklilu and Waday, 2021;Sathish and Vivekanandan, 2016;Tetteh et al, 2018) The interactive effect of the selected parameters on methane yield was investigated by plotting 3D surface plots as shown in Figures 3 and 4. The 3D statistical analysis shows the interactive effects of the selected process parameters for oDMMY (Figure 3 temperature was noticed to be directly proportional to the yield.…”

Section: Interactive Effect Of Process Parameters On Methane Yieldmentioning

confidence: 75%

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Modelling the effects of particle size pretreatment method on biogas yield of groundnut shells

et al. 2022

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Optimising biogas yields from anaerobic digestion of organic wastes is significant to maximum energy recovery in the biodigestion process and has become an important topic of interest. Substrate particle size is an important process parameter in biogas production, and it precedes other pretreatments methods for the majority of the lignocellulose materials. Optimisation of biogas yield using Response Surface Methodology (RSM) was done, and temperature, hydraulic retention time and particle size were considered variables to develop the predictive models. Pretreatment of groundnut shells was investigated using particle size reduction of mechanical pretreatment methods. After pretreatment, 30 samples were digested in a batch digester at mesophilic temperature. The experimental results showed that the temperature, hydraulic retention time and particle size had significant effects of interaction ( p < 0.05). The optimum experimental and predicted yields are: 44.70 and 42.92 (lNkgoDM) organic dry matter biogas yield, 20.80 and 19.09 (lN/kgFM) fresh mass biogas yield, 24.00 and 22.68 (lNCH4oDM) organic dry methane yield and 12.30 and 15.59 (lNCH4FM) fresh mass methane yield, respectively. The R2 recorded for the four yield components were 0.6268, 0.5875, 0.6109 and 0.5547. These values seem to be lower and a sign of the average fit of the model. Biogas production from groundnut shells was significantly improved with statistical optimisation and the pretreatment method.

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Section: Resultscontrasting

confidence: 51%

“…The relative impact of the factors can be deduced with this equation when the coefficients of the factors are compared. The accuracy of a model is determined by the R 2 value; 0.6109 and 0.5547 were recorded in this work which are lower compared to 0.997, 0.995 and 0.995166 that were reported in the literature (Aklilu and Waday, 2021;Sathish and Vivekanandan, 2016;Tetteh et al, 2018) The interactive effect of the selected parameters on methane yield was investigated by plotting 3D surface plots as shown in Figures 3 and 4. The 3D statistical analysis shows the interactive effects of the selected process parameters for oDMMY (Figure 3 temperature was noticed to be directly proportional to the yield.…”

Section: Interactive Effect Of Process Parameters On Methane Yieldmentioning

confidence: 75%

Modelling the effects of particle size pretreatment method on biogas yield of groundnut shells

et al. 2022

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“…Between each connected neuron there is a specific weight applied to the signal transmitted by the neuron, and these weight values are transmitted backward after each layer acting on the neuron, and finally linearly coupled in the output layer.When input signals form output signals through the dip network model, the relationship between them is shown in the following figure [11]:…”

Section: Artificial Neural Network (Ann) Prediction Model Comparisonmentioning

confidence: 99%

Research on Fertility Policy Under the Background of Three-Child Policy

Ma¹,

Zhang²

2022

BCPBM

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Reasonable analysis and formulate relevant policy to control population stable growth is of great significance to social development and economic development in this paper, combining with the relevant data search conducted fertility policy mathematical modeling and analysis, introduces both short-term and long-term memory neural networks with time characteristics (LSTM), considering the population forecast may be is more sensitive to high-dimensional feature, The multi-layer stacked long and short-term memory neural network (DLSTM) was used as the prediction model, and the multi-layer stacked structure significantly improved the robustness and accuracy of prediction.

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“…ANNs have been previously used to model an anaerobic fermentation process [ 17 , 18 ], for biogas production integrated with wastewater purification considering both technological aspects of the process and treated wastewater quality [ 22 ], or for biogas production from food, fruits, and vegetables wastes [ 23 ], as well as from mixed lignocellulosic co-substrates [ 21 ]. ANN model was mainly used defining the optimum region in biogas production [ 24 , 25 , 26 ]. ANN combined with non-linear regression models were developed to predict the biogas production rate from anaerobic hybrid reactor [ 20 , 21 , 27 , 28 ] and as an element of the optimization strategy for biogas production from wastes [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…ANN model was mainly used defining the optimum region in biogas production [ 24 , 25 , 26 ]. ANN combined with non-linear regression models were developed to predict the biogas production rate from anaerobic hybrid reactor [ 20 , 21 , 27 , 28 ] and as an element of the optimization strategy for biogas production from wastes [ 24 ]. Concerning biogas from animal wastes, ANNs were used for the optimization of production from poultry [ 26 ] or cattle manure [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland

Wawrzyniak

Przybylak

Sujak

et al. 2022

Sensors

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In the presented study, data on the size and structure of cattle herds in Wielkopolskie, Podlaskie, and Mazowieckie voivodeships in 2019 were analyzed and subjected to modelling with the use of artificial intelligence, namely artificial neural networks (ANNs). The potential amount of biogas (m3) from cattle manure and slurry for the analyzed provinces was as follows: for the Mazowieckie Voivodeship, 800,654,186 m3; for the Podlaskie voivodeship, 662,655,274 m3; and for the Wielkopolskie voivodeship, 657,571,373 m3. Neural modelling was applied to find the relationship between the structure of the herds and the amount of generated slurry and manure (biomethane potential), as well as to indicate the most important animal types participating in biogas production. In each of the analyzed cases, the three-layer MLP perceptron with a single hidden layer proved to be the most optimal network structure. Sensitivity analysis of the generated models concerning herd structure showed a significant contribution of dairy cows to the methanogenic potential for both slurry and manure. The amount of slurry produced in the Mazowieckie and Wielkopolskie voivodeships was influenced in turn by heifers (both 6–12 and 12–18 months old) and bulls 12–24 months old, and in the Podlaskie voivodeship by calves and heifers 6–12 months old. As for manure, in addition to cows, bulls 12–24 months old and heifers 12–18 represented the main factor for Mazowieckie and Wielkopolskie voivodeships, and heifers (both 6–12 and 12–18 months old) for Podlaskie voivodeship.

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Optimizing the process parameters to maximize biogas yield from anaerobic co-digestion of alkali-treated corn stover and poultry manure using artificial neural network and response surface methodology

Cited by 20 publications

References 41 publications

Modelling the effects of particle size pretreatment method on biogas yield of groundnut shells

Modelling the effects of particle size pretreatment method on biogas yield of groundnut shells

Research on Fertility Policy Under the Background of Three-Child Policy

Neural Modelling in the Exploration of the Biomethane Potential from Cattle Manure: A Case Study on Herds Structure from Wielkopolskie, Podlaskie, and Mazowieckie Voivodeships in Poland

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