Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

Serrano, Daniel; Golpour, Iman; Sánchez-Delgado, S.

doi:10.1016/j.fuel.2020.117021

Cited by 64 publications

(25 citation statements)

References 40 publications

(49 reference statements)

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“…The evaluation process of ANNs for each output parameter was done individually for facilitating the training process of the neural networks (NNs) and analysis of the obtained results. The transfer functions used to obtain the best network structure were linear function (PUR), logarithmic sigmoid (LOG) and hyperbolic tangent sigmoid (TAN), according to the following equations [ 15 ]:

where Xj is computed as follows:

where m is the number of neurons in output layer, W ij is the corresponding weight between i th and j th layers, Y i is the i th output neuron, X j is the j th input neuron and b j is the bias of the j th neuron for the related networks.…”

Section: Methodsmentioning

confidence: 99%

“…For improving the capability and performance of the ANN model in recognizing relations among related inputs and outputs, guaranteeing the convergence and process stability, data normalization was done in the first step in the ANN modelling to forecast the outputs with respect to the following equation [ 15 , 28 ]:

where X r and X norm , represent the values of measured and normalized data, respectively, and X min and X max are the minimum and maximum values of the measured factors, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Data Normalization and Error Evaluationmentioning

confidence: 99%

“…One of the important opportunities for researchers, faced with time-consuming and costly methodologies, to acquire reputable information for various operating conditions involves new products and processes obtained through developing the science of soft computing. Artificial intelligence systems (AISs) like artificial neural networks (ANNs) could be a powerful tool to predict nonlinear system data for overcoming these concerns [ 15 ]. ANN as an intelligent alternative approach for solving engineering problems has been used to adjust multi-variable nonlinear functions [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

Golpour

Ferrão

Gonçalves

et al. 2021

Foods

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This research study focuses on the evaluation of the total phenolic compounds (TPC) and antioxidant activity (AOA) of strawberries according to different experimental extraction conditions by applying the Artificial Neural Networks (ANNs) technique. The experimental data were applied to train ANNs using feed- and cascade-forward backpropagation models with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms. Three independent variables (solvent concentration, volume/mass ratio and extraction time) were used as ANN inputs, whereas the three variables of total phenolic compounds, DPPH and ABTS antioxidant activities were considered as ANN outputs. The results demonstrate that the best cascade- and feed-forward backpropagation topologies of ANNs for the prediction of total phenolic compounds and DPPH and ABTS antioxidant activity factors were the 3-9-1, 3-4-4-1 and 3-13-10-1 structures, with the training algorithms of trainlm, trainbr, trainlm and threshold functions of tansig-purelin, tansig-tansig-tansig and purelin-tansig-tansig, respectively. The best R2 values for the predication of total phenolic compounds and DPPH and ABTS antioxidant activity factors were 0.9806 (MSE = 0.0047), 0.9651 (MSE = 0.0035) and 0.9756 (MSE = 0.00286), respectively. According to the comparison of ANNs, the results showed that the cascade-forward backpropagation network showed better performance than the feed-forward backpropagation network for predicting the TPC, and the FFBP network, in predicting the DPPH and ABTS antioxidant activity factors, had more precision than the cascade-forward backpropagation network. The ANN technique is a potential method for estimating targeted total phenolic compounds and the antioxidant activity of strawberries.

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where Xj is computed as follows:

Section: Methodsmentioning

confidence: 99%

where X r and X norm , represent the values of measured and normalized data, respectively, and X min and X max are the minimum and maximum values of the measured factors, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Data Normalization and Error Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

Golpour

Ferrão

Gonçalves

et al. 2021

Foods

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, a hyperbolic tangent sigmoid was used as an activation function in the hidden layer and for this task in the output layer, linear functions were employed. The performance of these functions has been proved in other research works [39,42,44,45,[89][90][91][92][93].…”

Section: Training and Testing Of The Ann-based Modelmentioning

confidence: 95%

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

et al. 2021

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In order to accurately anticipate the proficiency of downdraft biomass gasification linked with a water–gas shift unit to produce biohydrogen, a model based on an artificial neural network (ANN) approach is established to estimate the specific mass flow rate of the biohydrogen output of the plant based on different types of biomasses and diverse operating parameters. The factors considered as inputs to the models are elemental and proximate analysis compositions as well as the operating parameters. The model structure includes one layer for input, a hidden layer and output layer. One thousand eight hundred samples derived from the simulation of 50 various feedstocks in different operating situations were utilized to train the developed ANN model. The established ANN in the case of product biohydrogen presents satisfactory agreement with input data: absolute fraction of variance (R2) is more than 0.999 and root mean square error (RMSE) is lower than 0.25. In addition, the relative impact of biomass properties and operating parameters on output are studied. At the end, to have a comprehensive evaluation, variations of the inputs regarding hydrogen-content are compared and evaluated together. The results show that almost all of the inputs show a significant impact on the smhydrogen output. Significantly, gasifier temperature, SBR, moisture content and hydrogen have the highest impacts on the smhydrogen with contributions of 19.96, 17.18, 15.3 and 10.48%, respectively. In addition, other variables in feed properties, like C, O, S and N present a range of 1.28–8.6% and proximate components like VM, FC and A present a range of 3.14–7.67% of impact on smhydrogen.

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Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

Altikat

Alma

2021

Intl J of Energy Research

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Summary Biochar, bio‐oil, and synthesis gas are products obtained from the pyrolysis process, which have alternative usage areas. Biochar is used for adsorption of pollutants, soil conditioner, and as an alternative heating source. Bio‐oil is used as a fuel for conventional engines after various upgrading methods, and the synthesis gas is a heat source in energy production. Biochar, bio‐oil, and synthesis gas yields obtained from the Atriplex nitens S. plant at the end of the pyrolysis process were modeled using artificial neural networks (ANNs) and hybrid models in this study. Multiple linear regression, ANNs, principal component analysis + multiple linear regression, and principal component analysis + ANN models were used. In addition, 48 different network architectures in ANNs were tested. At the end of the study, the best prediction results of biochar, bio‐oil, and synthesis gas were obtained from the ANN 35 (R2 = 0.977), ANN 17 (R2 = 0.985), and ANN 44 (R2 = 0.969) ANN architecture, respectively. The sensitivity analyses were performed using these best models for biochar, bio‐oil, and synthesis gas. As a result of the sensitivity analysis, it was determined that the most effective factor in the production of biochar and bio‐oil was the gas flow rate, and this was followed by the holding time and pyrolysis temperature. However, the most effective factor in synthesis gas yield was determined as the holding time, and this was followed by gas flow rate and pyrolysis temperature parameters.

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Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach

Cited by 64 publications

References 40 publications

Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

Extraction of Phenolic Compounds with Antioxidant Activity from Strawberries: Modelling with Artificial Neural Networks (ANNs)

Modeling of Hydrogen Production by Applying Biomass Gasification: Artificial Neural Network Modeling Approach

Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S.

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