A Three-Layer Hybrid Model for Wind Power Prediction

Gao, Jian; Chongfuangprinya, Panitarn; Ye, Yanzhu; Yang, Bo

doi:10.1109/pesgm41954.2020.9281489

Cited by 21 publications

(18 citation statements)

References 14 publications

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“…Some studies have confirmed the advantages of BP neural network in prediction. For example, Gao et al [26] proposed a threelayer WPP model considering the data from historical power measurements and numerical weather prediction (NWP) systems. They compared the proposed approach against the state-of-the-art algorithm as well as several neural network models.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Application of Neural Network Algorithm in Propylene Distillation

Zhao

2021

JERR

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Artificial neural network modeling does not need to consider the mechanism. It can map the implicit relationship between input and output and predict the performance of the system well. At the same time, it has the advantages of self-learning ability and high fault tolerance. The gas-liquid two phases in the rectification tower conduct interphase heat and mass transfer through countercurrent contact. The functional relationship between the product concentration at the top and bottom of the tower and the process parameters is extremely complex. The functional relationship can be accurately controlled by artificial neural network algorithms. The key components of the propylene distillation tower are the propane concentration at the top of the tower and the propylene concentration at the bottom of the tower. Accurate measurement of them plays a key role in increasing propylene yield in ethylene production enterprises. This article mainly introduces the development process of neural network model and its application progress in propylene distillation tower.

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Section: Bp Neural Networkmentioning

confidence: 99%

Application of Neural Network Algorithm in Propylene Distillation

Zhao

2021

JERR

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“…The unique structure and information processing method of ANN make it have obvious advantages in many aspects and have a wide range of applications. The main application areas are image processing [7][8][9][10], robot control, automatic control of power systems [11][12][13], signal processing [14][15][16], intelligent driving [17,18], health care and medical treatment [19][20][21], game theory [22,23], process control and optimization [24 -27], etc.…”

Section: The Structure and Characteristics Of Artificial Neural Networkmentioning

confidence: 99%

Review of Artificial Neural Network and Its Application Research in Distillation

Sun

Tang

2021

JERR

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With the development of rectification technology, the scale of its production equipment has continued to expand, and its calculation requirements have become more complex. The use of traditional optimized control methods can no longer meet the requirements. Artificial neural networks imitate the human brain for self-learning and optimization, intelligently process various complex information, and have been widely used in various chemical processes. Because the artificial neural network has the advantages of self-learning, associative storage, and high-speed search for optimized solutions, it can perform high-precision simulation and prediction of rectification operations, and has been widely used in the optimal control of rectification towers. This article gives a basic overview of artificial neural networks, and introduces the application research of artificial neural networks in distillation at home and abroad.

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“…The application of artificial neural networks has gradually begun to shine in all walks of life. The main application areas are signal processing [14][15][16], plant diseases and insect pests and irrigation control [17,18], intelligent control of industrial product assembly line [19,20], intelligent driving [21,22], chemical product development [23][24][25], image processing [26][27][28], robotic surgery [29][30][31], automatic control of power systems [32][33][34], troubleshooting [35,36], process control and optimization [37][38][39], etc.…”

Section: The Development History Of Artificial Neural Networkmentioning

confidence: 99%

Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

Liang

Zhang

2021

AJRCoS

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Artificial Neural Network (ANN) is established by imitating the human brain's nerve thinking mode. Because of its strong nonlinear mapping ability, fault tolerance and self-learning ability, it is widely used in many fields such as intelligent driving, signal processing, process control and so on. This article introduces the basic principles, development history and three common neural network types of artificial neural networks, BP neural network, RBF neural network and convolutional neural network, focusing on the research progress of the practical application of neural networks in chemical process optimization.

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A Three-Layer Hybrid Model for Wind Power Prediction

Cited by 21 publications

References 14 publications

Application of Neural Network Algorithm in Propylene Distillation

Application of Neural Network Algorithm in Propylene Distillation

Review of Artificial Neural Network and Its Application Research in Distillation

Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

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