scholarly journals Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1631 ◽  
Author(s):  
Zhuo Zhang ◽  
Fayu Sun ◽  
Qingling Li ◽  
Weiqiang Wang ◽  
Dedong Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Generalized Regression ◽  
Supercritical Carbon

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

scholarly journals Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour

2019 ◽  
Vol 6 (7) ◽  
pp. 190485 ◽  
Author(s):  
Xudong Sun ◽  
Junbin Liu ◽  
Ke Zhu ◽  
Jun Hu ◽  
Xiaogang Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Benzoic Acid ◽  
Wheat Flour ◽  
Terahertz Spectroscopy ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Powder Samples ◽  
Generalized Regression

Investigations were initiated to develop terahertz (THz) techniques associated with machine learning methods of generalized regression neural network (GRNN) and back-propagation neural network (BPNN) to rapidly measure benzoic acid (BA) content in wheat flour. The absorption coefficient exhibited a maximum absorption peak at 1.94 THz, which generally increased with the content of BA additive. THz spectra were transformed into orthogonal principal component analysis (PCA) scores as the input vectors of GRNN and BPNN models. The best GRNN model was achieved with three PCA scores and spread value of 0.2. Compared with the BPNN model, GRNN model to powder samples could be considered very successful for quality control of wheat flour with a correlation coefficient of prediction ( r p ) of 0.85 and root mean square error of prediction of 0.10%. The results suggest that THz technique association with GRNN has a significant potential to quantitatively analyse BA additive in wheat flour.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Solubility of the Ketoconazole (an Antifungal Drug) in Supercritical Carbon Dioxide and Menthol as a Cosolvent (Ternary System): Experimental Data and Empirical Correlations

2021 ◽  
Author(s):  
Gholamhossein Sodeifian ◽  
Seyed Ali Sajadian ◽  
Fariba Razmimanesh ◽  
Seyed Mojtaba Hazaveie
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Binary System ◽  
Supercritical Carbon Dioxide ◽  
Ternary System ◽  
Drug Solubility ◽  
Analysis Method ◽  
Empirical Correlations ◽  
Semiempirical Models ◽  
Supercritical Carbon

Abstract One of the main steps in choosing the drug nanoparticle production processes by supercritical carbon dioxide (SC-CO2) is determining the solubility of the solid solute. For this purpose, the solubility of Ketoconazole (KTZ) in the SC-CO2, binary system, as well as in the SC-CO2-menthol (cosolvent), ternary system, was measured at 308–338 K and 12–30 MPa using the static analysis method. The KTZ solubility in the SC-CO2 ranged between 1.70×10− 6 and 8.02×10− 4, while drug solubility in the SC-CO2 with cosolvent varied from 2.7×10− 5 to 1.96×10− 4. This difference indicated the significant effect of menthol cosolvent on KTZ solubility in the SC-CO2. Moreover, KTZ solubilities in the two systems were correlated by several empirical and semiempirical models. Among them, Sodeifian et al., Bian et al., MST, and Bartle et al. models can more accurately correlate experimental data for the binary system than other used models. Also, the Sodeifian and Sajadian model well fitted the solubility data of the ternary system with AARD,%= 6.45, Radj= 0.995.

scholarly journals Estimation of fastener pull-through resistance of composite laminates based on generalized regression neural network

2020 ◽  
Vol 29 ◽  
pp. 2633366X2096884
Author(s):  
Sheng Mingjian ◽  
Chen Puhui ◽  
Chen Cheng
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Composite Laminates ◽  
Engineering Application ◽  
Transverse Load ◽  
Generalized Regression Neural Network ◽  
Element Analysis ◽  
Research Attention ◽  
Generalized Regression ◽  
Pull Through

The fastener pull-through resistance is a key performance index of composite laminates used for engineering application, and increasing research attention is being paid to developing methods for its calculation or estimation. The currently available research methods mainly focus on the standard test and the finite element analysis for determining the pull-through resistance of composite laminates suffering transverse load by the fasteners. Based on the results of the fastener pull-through resistance experiment performed on X850 composite laminates, a model for estimating the maximum affordable load of composite laminates for the fastener pull-through resistance is proposed, using generalized regression neural network technology. The inputs of this model are simplified to six parameters: the proportion of the ±45° layer of the laminates, the number of the layers, the thickness of the laminates, the bolt head shape, whether the bolt has a washer or not, and the nominal diameter of the bolt; the Gauss function is used as the hidden layer function. The model uses a large portion of the experimental data to train for finding the optimal smoothness factor, which is used to reconstruct the model, and simulation is performed with the remainder of the experimental data. The comparison between the estimated results using the model and the experimental results shows that the generalization ability of the proposed model can meet the estimation requirements. Moreover, the pull-through resistance of composite laminates under transverse load from a fastener can be estimated with high accuracy after some standard fastener pull-through resistance tests of the composite laminates.

scholarly journals Experimental Data and Modelling of the Solubility of High-Carotenoid Paprika Extract in Supercritical Carbon Dioxide

Molecules ◽  
2019 ◽  
Vol 24 (22) ◽  
pp. 4174 ◽  
Author(s):  
Dorota Kostrzewa ◽  
Agnieszka Dobrzyńska-Inger ◽  
August Turczyn
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Static Method ◽  
Coefficient Of Determination ◽  
High Concentration ◽  
Relative Deviation ◽  
Measurement Results ◽  
Supercritical Carbon

The studies of solubility of the paprika extract with a high concentration of carotenoids in carbon dioxide under the pressure of 20–50 MPa and at temperatures of 313.15–333.15 K were carried out using the static method. The highest solubility of paprika extract was achieved at the temperature of 333.15 K and under the pressure of 50 MPa. The obtained experimental data were correlated with five density-based models, applied for prediction of solubility in the supercritical carbon dioxide (the Chrastil, del Valle and Aguilera, Adachi and Lu, Sparks et al. and Bian et al. models). The accuracy of particular models with reference to measurement results was specified with the average absolute relative deviation (AARD) and coefficient of determination (R2). Results showed that solubility calculated based on the selected models was compliant with experimental data.

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