Modeling of the residence time distribution in a buss kneader with a back-propagation neural network

2008 ◽  
Vol 109 (4) ◽  
pp. 2224-2231 ◽  
Author(s):  
Chao Bi ◽  
Bo Jiang ◽  
Ao Li
Keyword(s):  
Neural Network ◽  
Residence Time ◽  
Time Distribution ◽  
Residence Time Distribution ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Buss Kneader

Neural network prediction of residence time distribution for quasi-2D pebble flow

2021 ◽  
pp. 117363
Author(s):  
Yujia Liu ◽  
Jeremy Marquardt ◽  
Sifan Peng ◽  
Liang Ge ◽  
Nan Gui ◽  
...  
Keyword(s):  
Neural Network ◽  
Residence Time ◽  
Time Distribution ◽  
Residence Time Distribution ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Pebble Flow

scholarly journals Solids Residence Time Distribution in a Three-Phase Bubble Column Reactor: An Artificial Neural Network Analysis

2008 ◽  
Vol 2 (1) ◽  
pp. 73-78 ◽  
Author(s):  
V.K. Pareek ◽  
R. Sharma ◽  
C.G. Cooper ◽  
A.A. Adesina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Residence Time ◽  
Time Distribution ◽  
Residence Time Distribution ◽  
Bubble Column ◽  
Back Propagation Algorithm ◽  
Validation Data ◽  
Three Phase ◽  
Artificial Neural

Residence time distribution (RTD) study of solids in a three-phase pilot-scale bubble column photoreactor has been carried out in order to provide data for the development of an artificial neural network model usable for process optimisation. The experimental data indicated that the RTD of solids was a complex nonlinear function of gas and liquid velocities as well as the contacting pattern (co-current and countercurrent flow of gas and liquid). In this study, the solid particle RTD data were modeled using feed forward artificial neural networks (ANN). The networks were trained with 250- sets of input-output patterns using back-propagation algorithm. The trained networks were tested using 50-sets of RTD data previously unknown to the networks. Out of several configurations, a 3-layered network with 6-neurons in its hidden layer yielded optimal results with respect to the validation data. The optimal model and empirical data exhibited good agreement with a correlation coefficient of 0.995.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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