Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates

Ghatak, Manjula Das; Ghatak, Amitava

doi:10.1016/j.fuel.2018.05.051

Cited by 86 publications

(26 citation statements)

References 37 publications

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“…During the backpropagation of the data signals, in order to minimize the error signal data, the bias and weights at the neurons of the layers are reformed. This iterative procedure continues to reach the maximum epoch including one forward and backward pass …”

Section: Methodsmentioning

confidence: 99%

“…This iterative procedure continues to reach the maximum epoch including one forward and backward pass. 34 In Figure 2, general multilayer feed forward neural network architecture and NARX time series modeling architecture are shown with their multiple input and output variables. In addition to this, as a rule, at least one hidden layer must be found among the components of the model.…”

Section: Theoretical Aspects Of Ann-based Modelingmentioning

confidence: 99%

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Comparison of the different artificial neural networks in prediction of biomass gasification products

2019

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Summary In this study, artificial neural networks (ANNs) and a nonlinear autoregressive exogenous (NARX) neural network model were employed in order to model a fixed bed downdraft gasification. The relation between the feature group and the regression performance was investigated. First, feature group consists of the equivalence ratio (ER), air flow rate (AF), and temperature distribution (T0‐T5) obtained from the fixed bed downdraft gasifiers, while the second group includes ultimate and proximate values of biomasses, ER, AF, and the reduction temperature (T0). Models constructed to predict the syngas composition (H2, CO2, CO, CH4) and calorific value. Experimental gasification data that involve 3831 data samples that belong to pinecone and wood pellet were used for training the ANNs. Different ANN architecture and NARX time series model have been constructed to examine the prediction accuracy of the models. The results of the ANN models were consistent with the experimental data (R2 > 0.99). The overall score of NARX time series networks is found to be higher than other architecture types. A successful method is proposed to reduce the number of features, and the effect of the features on the prediction capability was examined by calculating the relative importance index using the Garson's equation.

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Section: Methodsmentioning

confidence: 99%

Section: Theoretical Aspects Of Ann-based Modelingmentioning

confidence: 99%

Comparison of the different artificial neural networks in prediction of biomass gasification products

2019

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“…The main objectives of these type of researches vary in increasing or estimating the product (liquid, gas, and char) yields, determine the effects of input parameters, prediction of outputs such as heating value, hydrogen-rich gas, kinetic parameters, and thermal behaviors. In the literature, there are studies on process modeling using the ANN [11][12][13], support vector machine (SVM) [14,15], response surface methodology (RSM) [16][17][18], and regression analysis [19] methods. Recently, the ANN has received attention in the modeling of production methods mentioned above.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

Özbay

Kökten

2020

Politeknik Dergisi

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“…In the past decade, different ML techniques (summarized in Table S1) have been leveraged to predict biogas production, including connectivism learning (e.g., artificial neural network) and statistical learning (e.g., random forest, extreme gradient boosting, support vector machine). 22,23,[27][28][29][30][31][32][33][34][35][36][37] Previous research either employed a single technique or aimed to compare several techniques to select the bestperforming approach (Table S1). These prior studies were also limited by the training data available to them.…”

Section: Introductionmentioning

confidence: 99%

Tree-based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste

Huntington

Scown

2021

Preprint

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The dynamics of microbial communities involved in anaerobic digestion of mixed organic waste are notoriously complex and difficult to model, yet successful operation of anaerobic digestion is critical to the goals of diverting high-moisture organic waste from landfills. Machine learning (ML) is ideally suited to capturing complex and nonlinear behavior that cannot be modeled mechanistically. This study uses 8 years of data collected from an industrial-scale anaerobic co-digestion (AcoD) operation at a municipal wastewater treatment plant in Oakland, California, combined with a powerful automated ML method, Tree-based Pipeline Optimization Tool, to develop an improved understanding of how different waste inputs and operating conditions impact biogas yield. The model inputs included daily input volumes of 31 waste streams and 5 operating parameters. Because different wastes are broken down at varying rates, the model explored a range of time lags ascribed to each waste input ranging from 0 to 30 days. The results suggest that the waste types (including rendering waste, lactose, poultry waste, and fats, oils, and greases) differ considerably in their impact on biogas yield on both a per-gallon basis and a mass of volatile solids basis, while operating parameters are not useful predictors in a carefully operated facility.

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Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates

Cited by 86 publications

References 37 publications

Comparison of the different artificial neural networks in prediction of biomass gasification products

Comparison of the different artificial neural networks in prediction of biomass gasification products

Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

Tree-based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste

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