Novel approach for estimating solubility of solid drugs in supercritical carbon dioxide and critical properties using direct and inverse artificial neural network (ANN)

2015 ◽  
Vol 28 (1) ◽  
pp. 87-99 ◽  
Author(s):  
A. Abdallah el hadj ◽  
M. Laidi ◽  
C. Si-Moussa ◽  
S. Hanini
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Supercritical Carbon Dioxide ◽  
Critical Properties ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Supercritical Carbon

scholarly journals Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

2019 ◽  
Author(s):  
Amin Bemani ◽  
Alireza Baghban ◽  
Shahab Shamshirband
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Supercritical Carbon Dioxide ◽  
Acid Dissociation ◽  
Support Vector ◽  
Acid Dissociation Constant ◽  
Inference System ◽  
Artificial Neural ◽  
Supercritical Carbon

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron artificial neural network, Least squares support vector machine and adaptive neuro-fuzzy inference system are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

scholarly journals Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

2019 ◽  
Author(s):  
Amin Bemani ◽  
Alireza Baghban ◽  
Shahaboddin Shamshirband ◽  
Amir Mosavi ◽  
Peter Csiba ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Supercritical Carbon Dioxide ◽  
Acid Dissociation ◽  
Support Vector ◽  
Acid Dissociation Constant ◽  
Inference System ◽  
Artificial Neural ◽  
Supercritical Carbon

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

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