A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide

Mehdizadeh, Bahman; Movagharnejad, Kamyar

doi:10.1016/j.fluid.2010.12.010

Cited by 48 publications

(30 citation statements)

References 40 publications

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“…Input data were randomized into three sets: learning, validation, and testing. Usually, 30% of data are used for testing, and the remaining 70% for training and validation (Mehdizadeh and Movagharnejad 2011). The experimental data included 92 data points.…”

Section: Artificial Neural Network (Ann) Modelingmentioning

confidence: 99%

Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production

Uzuner

Çekmecelioğlu

2016

BioResources

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The artificial neural network (ANN) method was used in comparison with the adaptive neuro-fuzzy inference system (ANFIS) to describe polygalacturonase (PG) production by Bacillus subtilis in submerged fermentation. ANN was evaluated with five neurons in the input layer, one hidden layer with 7 neurons, and one neuron in the output layer. Five fermentation variables (pH, temperature, time, yeast extract concentration, and K2HPO4 concentration) served as the input of the ANN and ANFIS models, and the polygalacturonase activity was the output. Coefficient of determination (R 2 ) and root mean square values (RMSE) were calculated as 0.978 and 0.060, respectively for the best ANFIS structure obtained in this study. The R 2 and RMSE values were computed as 1.00 and 0.030, respectively for the best ANN model. The results showed that the ANN and ANFIS models performed similarly in terms of prediction accuracy.

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Section: Artificial Neural Network (Ann) Modelingmentioning

confidence: 99%

Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production

Uzuner

Çekmecelioğlu

2016

BioResources

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“…The type of network used in this work is the MLP network. MLP networks are one of the most popular and successful neural network architectures, which are suited to a wide range of applications such as prediction and process modeling (Sastri and Rao, 1995;Movagharnejad and Nikzad, 2007;Mehdizadeh and Movagharnejad, 2011;Movagharnejad et al, 2011).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of the Surface Tension of Hydrocarbons

Movagharnejad

Zareei

Mehdizadeh

et al. 2015

Petroleum Science and Technology

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Prediction of the surface tension of hydrocarbons is required in many chemical engineering calculations. In this work a robust artificial neural network code has been used in MATLAB software (The MathWorks, Natick, MA) to predict the surface tension prediction of 61 hydrocarbons. Experimental data is divided into two parts (70% for training and 30% for testing). Optimal configuration of network is obtained with minimization of prediction error on testing data. The accuracy of our proposed model is compared to four well-known empirical equations. Results showed that artificial neural network was more accurate than these empirical equations. Average relative deviation of our artificial neural network model is 0.93 while average relative deviation of the Brock-Bird, Pitzer, Zuo-Stenby, and Sastri-Rao equations are 6.30, 6.48, 5.73, and 6.33, respectively.

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“…Neurons are connected by weights that are modified during the learning phase [14]. All neural networks have three main layers which are called input, hidden and output layers [16]. Many classes of neural networks exist in the literature such as, feed forward back propagation, recurrent neural networks, cascade correlation neural networks and radial basis function neural networks.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network

Esfahanian

Nikzad

Najafpour

et al. 2013

CI&CEQ

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In this study, the capabilities of response surface methodology (RSM) and artificial neural networks (ANN) for modeling and optimization of ethanol production from glucoseusing Saccharomyces cerevisiae in batch fermentation process were investigated. Effect of three independent variables in a defined range of pH (4.2-5.8), temperature (20-40ºC) and glucose concentration (20-60 g/l) on the cell growth and ethanol production was evaluated. Results showed that prediction accuracy of ANN was apparently similar to RSM. At optimum condition of temperature (32°C), pH (5.2) and glucose concentration (50 g/l) suggested by the statistical methods, the maximum cell dry weight and ethanol concentration obtained from RSM were 12.06 and 16.2 g/l whereas experimental values were 12.09 and 16.53 g/l, respectively. The present study showed that using ANN as fitness function, the maximum cell dry weight and ethanol concentration were 12.05 and 16.16 g/l, respectively. Also, the coefficients of determination for biomass and ethanol concentration obtained from RSM were 0.9965 and 0.9853 and from ANN were 0.9975 and 0.9936, respectively. The process parameters optimization was successfully conducted using RSM and ANN; however prediction by ANN was slightly more precise than RSM. Based on experimental data maximum yield of ethanol production of 0.5 g ethanol/g substrate (97 % of theoretical yield) was obtained

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A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide

Cited by 48 publications

References 40 publications

Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production

Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production

Prediction of the Surface Tension of Hydrocarbons

Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network

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