Operation parameters optimization of butadiene extraction distillation based on neural network

2011 ◽  
Author(s):  
Fengqin Chen ◽  
Jianguo Zheng
Keyword(s):  
Neural Network ◽  
Parameters Optimization ◽  
Operation Parameters

scholarly journals Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3451 ◽  
Author(s):  
Sławomir Opałka ◽  
Bartłomiej Stasiak ◽  
Dominik Szajerman ◽  
Adam Wojciechowski
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Parameters Optimization ◽  
Alternative Methods ◽  
Signal Frequency ◽  
Eeg Signal ◽  
Mental Tasks Classification ◽  
Mental Tasks

Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

ANALYSIS AND PREDICTION OF TRENDS IN THE USE OF TERMS IN COMPUTER SCIENCE BASED ON NEURAL NETWORK MODELS

2021 ◽  
pp. 24-38
Author(s):  
T. V. Nguyen ◽  
Q. H. T. Duong ◽  
A. G. Kravets
Keyword(s):  
Neural Network ◽  
Computer Science ◽  
Information And Communication Technologies ◽  
Digital Libraries ◽  
Network Models ◽  
Parameters Optimization ◽  
Research Trend ◽  
Sudden Increase ◽  
Neural Network Models ◽  
Usage Frequency

The widespread use of information and communication technologies, database technologies and the Internet has led to the development of specialized digital libraries. These digital libraries serve a huge number of different users and play an important role as repositories and providers of information and knowledge. Therefore, the automatic extraction of useful information from texts stored in digital libraries is becoming an increasingly important research topic in the field of data mining. The article discusses the statistical analysis of texts in the digital library arXiv.org to identify the most common terms, bigrams and trigrams. After the hyper-parameters optimization process of neural network models, the trend prediction results in the use of terms in the field of computer sciences are presented. By analyzing statistics and predicting usage frequency of bigram and trigram terms our findings provide evidence that papers concerned with machine learning, reinforcement learning, generative adversarial network, convolutional neural network and recurrent neural network can be seen as main future research trend in Computer science in the next 3 years. Moreover, topics related to will experience a sudden increase in usage frequency. Being able to predict scientific trends in advance could potentially revolutionize the way science is done, for instance, by enabling funding agencies to optimize allocation of resources towards promising research areas.

Numerical Study on Mechanism and Parameters Optimization of Temporary Plugging by Particles in Wellbore

2021 ◽  
pp. 1-16
Author(s):  
Tao Zhang ◽  
Ming Li ◽  
Jianchun Guo ◽  
Haoran Gou ◽  
Kefan Mu
Keyword(s):  
Particle Size ◽  
Numerical Study ◽  
Optimal Operation ◽  
Parameters Optimization ◽  
Mass Ratio ◽  
Stimulated Reservoir Volume ◽  
Reservoir Volume ◽  
Pump Rate ◽  
Single Well ◽  
Operation Parameters

Summary The temporary plugging by particles in the wellbore can open new perforation clusters and increase stimulated reservoir volume, but the temporary plugging process of particles is not clear. Therefore, in this paper, we take an ultradeep well in the Tarim Basin as the research object and establish a numerical model based on the coupled computational fluid dynamics-discrete element technology (CFD-DEM) approach, which accurately describes the movement process and mechanism of the temporary plugging particles in the wellbore. Furthermore, the influence of flow rate, concentration of injected particles, and the injected mass ratio of particle size on the temporary plugging effect were studied, respectively. In addition, based on the results of the orthogonal experimental analysis, we obtained the pump rate as the primary factor affecting the effect of temporary plugging, and we recommended the optimal operation parameters for temporary plugging by particles in the field: The pump rate is 2 m3/min, the concentration of the injected temporary plugging particles is 20%, and the ratio of the mass of the injected temporary plugging particles with particle size 1 to 5 mm to the mass of the temporary plugging particles with particle size 5 to 10 mm is 3:1. Finally, a single well that had implemented temporary plugging by particles was used to verify the recommended optimal temporary plugging operation parameters. The research results of this paper provide important guidance and suggestions for the design of temporary plugging schemes on the field.

Prediction and Optimization of WAG Flooding by Using LSTM Neural Network Model in Middle East Carbonate Reservoir

2021 ◽  
Author(s):  
Ruijie Huang ◽  
Chenji Wei ◽  
Baozhu Li ◽  
Jian Yang ◽  
Suwei Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Short Term Memory ◽  
Oil Production ◽  
Simulation Method ◽  
Analysis Data ◽  
Carbonate Reservoir ◽  
Parameters Optimization ◽  
Model Based

Abstract Production prediction continues to play an increasingly significant role in reservoir development adjustment and optimization, especially in water-alternating-gas (WAG) flooding. As artificial intelligence continues to develop, data-driven machine learning method can establish a robust model based on massive data to clarify development risks and challenges, predict development dynamic characteristics in advance. This study gathers over 15 years actual data from targeted carbonate reservoir and establishes a robust Long Short-Term Memory (LSTM) neural network prediction model based on correlation analysis, data cleaning, feature variables selection, hyper-parameters optimization and model evaluation to forecast oil production, gas-oil ratio (GOR), and water cut (WC) of WAG flooding. In comparison to traditional reservoir numerical simulation (RNS), LSTM neural networks have a huge advantage in terms of computational efficiency and prediction accuracy. The calculation time of LSTM method is 864% less than reservoir numerical simulation method, while prediction error of LSTM method is 261% less than RNS method. We classify producers into three types based on the prediction results and propose optimization measures aimed at the risks and challenges they faced. Field implementation indicates promising outcome with better reservoir support, lower GOR, lower WC, and stabler oil production. This study provides a novel direction for application of artificial intelligence in WAG flooding development and optimization.

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