scholarly journals Modeling and Optimization of Propane Selective Oxidation to Acrylic Acid Over Mo 1 V 0.3 Te 0.23 NB 0.12 O X Catalyst Using Artificial Neural Network and Box-Behnken Design

2020 ◽  
Vol 21 (1) ◽  
pp. 1-19
Author(s):  
Golshan Mazloom
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Acrylic Acid ◽  
Selective Oxidation ◽  
Propane Conversion ◽  
Box Behnken Design ◽  
Ann Models ◽  
Artificial Neural ◽  
Propane Selective Oxidation

The prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for propane selective oxidation to acrylic acid (AA) over Mo1V0.3Te0.23Nb0.12Ox catalyst was investigated in this work. 15 experimental runs based on the Box-Behnken design (BBD) were employed to study the effects of temperature (380 to 500 °C), superficial velocity (33.3 to 66.7 mL (min gcat)-1), (O2)/(C3H8) ratio (1 to 3) and their interactions on propane conversion, AA selectivity and COx selectivity. The quadratic polynomial BBD equations and the feed-forward back propagation ANN models were developed based on the designed experimental data. Statistical analysis; coefficient of determination (R2), mean absolute error (MAE) and analysis of variance (ANOVA) illustrated that there was acceptable adjustment between BBD and ANN predicted responses as compared to experimental data. While, the ANN model showed a clear preference and generalization capability over BBD model in the case of experimental data set which were not used to training the models. In addition the optimum conditions were found to be temperature (461.7 °C), GHSV (51.9 mL (min gcat)-1) and (O2)/(C3H8) ratio (2.1) which were determined by desirability function approach. In these conditions, propane conversion of 15.2%, AA selectivity of 32% and COx selectivity of 44% which obtained experimentally were in reasonable agreement with predicted responses. Chemical Engineering Research Bulletin 21(2019) 1-19

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

KNT-artificial neural network model for flux prediction of ultrafiltration membrane producing drinking water

2004 ◽  
Vol 50 (8) ◽  
pp. 103-110 ◽  
Author(s):  
H.K. Oh ◽  
M.J. Yu ◽  
E.M. Gwon ◽  
J.Y. Koo ◽  
S.G. Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Correlation Coefficient ◽  
Network Model ◽  
Operating Conditions ◽  
Transmembrane Pressure ◽  
Feed Water ◽  
Permeate Flux ◽  
Artificial Neural

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

scholarly journals Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel

2020 ◽  
pp. 238-238
Author(s):  
Adel Bouali ◽  
Salah Hanini ◽  
Brahim Mohammedi ◽  
Mouloud Boumahdi
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Flow Boiling ◽  
Low Flow ◽  
Inclined Channel ◽  
Artificial Neural

The flow and heat transfer characteristics in a nuclear power plant in the event of a serious accident are simulated by boiling water in an inclined rectangular channel. In this study an artificial neural network model was developed with the aim of predicting heat transfer coefficient (HTC) for flow boiling of water in inclined channel, the network was designed and trained by means of 520 experimental data points that were selected from within the literature. orientation ,mass flux, quality and heat flow which were employed to serve as variables of input of multiple layer perceptron (MLP) neural network, whereas the analogous HTC was selected to be its output. Via the method of trial-and-error, MLP network with 30 neurons in the hidden layer was attained as optimal ANN structure. The fact that is was enabled to predict accurately the HTC. For the training set, the mean relative absolute error (MRAE) is about 0.68 % and the correlation coefficient (R) is about 0.9997. As for the testing and validation set they are respectively about 0.60 % and 0.9998 and about 0.79 % and 0.9996. The comparison of the developed ANN model with experimental data and empirical correlations in vertical channel under the low flow rate and low quality shows a good agreement.

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

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