Assessment of producer gas composition in air gasification of biomass using artificial neural network model

George, Joel; Arun, P.; Muraleedharan, C.

doi:10.1016/j.ijhydene.2018.04.007

Cited by 106 publications

(37 citation statements)

References 51 publications

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“…The predicted values were found to be in good agreement with the actual values based on the parity plots. In a similar study, George et al [23] applied the Leven-Marquardt algorithm for the predictive modeling of producer gas composition during biomass gasification. The study revealed that the predicted values of the producer gas were in good agreement with the actual values with an R value of 0.987.…”

Section: The Ann Model Predictive Analysismentioning

confidence: 99%

“…Studies have shown that the use of the Bayesian regulation algorithm for ANN modeling results in a good prediction of the targets. George et al [23] employed the Bayesian regularization algorithm for the ANN modeling of wheat output energy from a wheat production process. The study revealed that the use of the Bayesian regularization algorithm resulted in a good prediction of the wheat output energy which was in good agreement with the actual values.…”

Section: The Ann Model Predictive Analysismentioning

confidence: 99%

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Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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This study investigates the applicability of the Leven-Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H 2 production by methane dry reforming over a Co/Pr 2 O 3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH 4 partial pressure, CO 2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H 2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H 2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R 2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H 2 production were strongly correlated with the observed values.To overcome these challenges, several supported metal-based catalysts have been developed and tested. An extensive review by Abdullah et al. [10] revealed that supported nickel (Ni) catalysts have been mostly investigated for methane dry reforming due to its high catalytic performance. Nevertheless, the Ni-based catalysts are very prone to sintering and carbon deposition [11]. On the other hand, cobalt (Co)-based catalysts which have a comparative activity to Ni have been reported to show superior stability compare to Ni under the same process condition [12,13]. In our previous studies, the use of rare-earth metal oxide-supported Co catalysts for CO-rich hydrogen production showed considerable activity and stability [14][15][16]. However, one major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H 2 and CO production due to variations in the chemical composition of the various catalysts [17]. This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. Processes with non-linear and complex relationships between the input and the output parameters are often encountered in real life processes. The better understanding of the non-linear relationship between the input and the output parameters of the process can further be utilized to optimize the process operation and create the basis for the theoretical framework, process automation, and upscaling [20].An artificial intelligence modeling approach using an artificial neural network (ANN) has been widely employed for different catalytic processes, such as hydrodesulfurization [20], methanol steam reforming, glycerol steam reforming [2...

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Section: The Ann Model Predictive Analysismentioning

confidence: 99%

Section: The Ann Model Predictive Analysismentioning

confidence: 99%

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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“…They observed that increasing in oxygen ratio from 0.366 to 0.41, gasification efficiency and higher heating value decreased but carbon conversion efficiency increased . For bubbling fluidized bed gasifier, George et al supported their experimental studies with an ANN model. Li et al developed an artificial neural network (ANN) to determine the gasifier length and influence of heating rate on hydrogen yields and hydrogen efficiency in their study.…”

Section: Introductionmentioning

confidence: 99%

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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“…The activation functions of the input and output layers can be either linear or nonlinear, while the hidden layer can have only nonlinear activation functions [13]. Sigmoid functions are widely used as there exist simple relationship between the value of the function and derivative value at any point.…”

Section: Introductionmentioning

confidence: 99%

“…The study was to develop an optimized feed-forward ANN model employing back propagation algorithm suitable for predicting the air quality parameters for the South Indian industrial area of Ambalamugal. A multilayer feed-forward ANN model is characterized by one input, at least one hidden and one output layers, each composed of neurons [13]. ANN models with Levenberg-Marquardt backpropagation algorithm having one hidden layer is used for prediction models usually [12,15,17].…”

Section: Introductionmentioning

confidence: 99%

Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2

Unnikrishnan

Madhu

2019

SN Appl. Sci.

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Variations in meteorological parameters, different transportation mechanisms, complex reaction mechanisms and insufficient control measures have made control of ambient air pollution, a challenge. Prediction mechanisms for pollutant concentrations in advance have become necessary to regulate parameters within the acceptable limits. Artificial neural network (ANN) modelling can be used for predicting the concentrations of pollutants by establishing functional relationships between complex and nonlinear predictor variables and outputs. The application of time-series ANN models to predict air quality parameters was investigated by using six pollutant variables, five meteorological parameters and three time parameters to predict the concentration of SO 2. An industrial belt in the southern part of India was selected as the study area. Two years of input parameters and feed-forward back propagation algorithm were used to construct the ANN model. Input parameters were optimized using forward selection and backward elimination techniques. Mean squared error and coefficient of determination were used to evaluate the models. The developed model exhibited very promising performance evaluation characteristics with the best model resulting in MSE of 0.0115 and R 2 value of 0.8979. The hybrid ANN models employing input parameter optimization techniques resulted in better performance characteristic values than conventional models. Any model exhibited a minimum reduction in MSE by 9% and 3% improvement in correlation. Pollutant parameters were found to affect the ANN models when compared to the meteorological parameters. Predicted values compared to the National Ambient Air Quality Standards were as low as 55% of the maximum allowable concentration in ambient air.

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Assessment of producer gas composition in air gasification of biomass using artificial neural network model

Cited by 106 publications

References 51 publications

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

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

Comparative study on the effects of meteorological and pollutant parameters on ANN modelling for prediction of SO2

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