Prediction of Chemical Production Based on Neural Network

Liang, Fei; Zhang, Taowen

doi:10.9734/ajacr/2021/v10i3-430241

Cited by 3 publications

(3 citation statements)

References 22 publications

(24 reference statements)

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“…In 2016, the advent of AlphaGo brought research enthusiasm for deep learning to a new height. Now ANN has been widely applied in various fields, such as image recognition [6][7][8], wireless signal processing [9][10][11][12], chemical process control and optimization [13][14][15][16], forecasting [17,18], security risk assessment [19], traditional Chinese medicine processing [20][21][22], aquatic products [23], intelligent driving [24,25], and so on.…”

Section: Research Progress Of Artificial Neural Networkmentioning

confidence: 99%

Advances in Neural Networks for Pharmaceutical Applications

Yang,

Chen

2024

AJARR

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Artificial neural networks (ANNs) are rapidly changing the landscape of the pharmaceutical industry. Their unique capabilities, including collective computing, adaptive learning, and fault tolerance, make them ideal for tackling complex challenges in drug discovery, analysis, and personalized medicine. This article summarizes the latest research progress in ANNs for pharmacy, highlighting breakthroughs in areas like QSAR modeling for drug design, pharmacokinetic prediction, and optimization of pharmaceutical preparations. With their immense potential to accelerate drug development, improve drug efficacy, and personalize healthcare, ANNs are poised to revolutionize the future of pharmaceuticals.

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Section: Research Progress Of Artificial Neural Networkmentioning

confidence: 99%

Advances in Neural Networks for Pharmaceutical Applications

Yang,

Chen

2024

AJARR

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“…Information is passed as indicated by the arrows in the directed diagram, so the data flows in one direction. The most important feature of this neural network is that it can learn and store a large number of highly nonlinear mappings without building a mathematical equation to describe the mapping relationships in advance, exhibiting excellent nonlinear matching and generalization capabilities [47,48]. The structure of MLP consists of input layers, hidden layers, and output layers.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Application of Artificial Neural Networks in Chemical Process Control

Wang

Chen

2022

AJRCoS

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An important data-driven model is the artificial neural network. Artificial neural networks have been widely used in many domains of chemical processes due to its robustness, fault tolerance, self-adaptive capability, and self-learning ability. For the chemical process with nonlinearity and strong coupling, artificial neural networks can model and control the process well and make up for the lack of traditional PID control technology. As a result, ANN has emerged as a significant positive trend for chemical process control. In this paper, the principle, development history, and common structure of artificial neural networks are first outlined. Then the role of artificial neural networks in chemical process control is introduced in three aspects: improved PID control, improved model predictive control, and for hybrid models. The important effect of artificial neural networks in chemical process control is reflected by comparison. Finally, it is proposed that chemical process control can be more developed by applying more deep learning algorithms and developing multiple neural networks and hybrid models in chemical process control.

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“…According to the difference of their functions, artificial neural networks can be divided into feedback network and feedforward network [6]. Feedback neural network generally includes input layer, hidden layer, undertaking layer and output layer [11] [16,17], automatic control [18,19], market analysis [20,21], chemical industry [22][23][24], game theory [25], medicine diagnosis [26][27][28], signal processing [29][30][31], troubleshooting [32,33], machine Learning [34][35][36] and other fields. The construction of artificial neural networks is realized by the simulation of human brain function, rather than by complex mathematical models.…”

Section: Artificial Neural Network and Its Developmentmentioning

confidence: 99%

Application of Artificial Neural Network in Optimal Design of Reactor

Si²

2022

AJRCoS

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Reactor is widely used in biology, chemical industry, metallurgy, environmental protection and other fields, playing an irreplaceable role. With the development of science and technology and the concept of green development, the application of artificial neural network to optimize the reactor reaction conditions has become a trend. Artificial neural network plays an important role in reactor optimization because of its strong fault tolerance, the ability to express complex nonlinear relations and perform complex operations. This paper will briefly describe the basic principle and research progress of artificial neural network, and its application in reactor design.

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Prediction of Chemical Production Based on Neural Network

Cited by 3 publications

References 22 publications

Advances in Neural Networks for Pharmaceutical Applications

Advances in Neural Networks for Pharmaceutical Applications

Application of Artificial Neural Networks in Chemical Process Control

Application of Artificial Neural Network in Optimal Design of Reactor

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