Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

Liang, Fei; Zhang, Taowen

doi:10.9734/ajrcos/2021/v12i430291

Cited by 5 publications

(3 citation statements)

References 29 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The commonly used radial basis functions are Gaussian function, thin-slab spline function, etc. Considering that the linear combination of signals from the hidden layer is sufficient to model any nonlinear function, the output nodes adopt linear activation functions [45,46,51].…”

Section: Radial Basis Function (Rbf) Networkmentioning

confidence: 99%

Application of Artificial Neural Networks in Chemical Process Control

Wang

Chen

2022

AJRCoS

View full text Add to dashboard Cite

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.

show abstract

Section: Radial Basis Function (Rbf) Networkmentioning

confidence: 99%

Application of Artificial Neural Networks in Chemical Process Control

Wang

Chen

2022

AJRCoS

View full text Add to dashboard Cite

show abstract

“…In the field of construction engineering, artificial neural networks are used to predict concrete strength and find the nonlinear input-output relationship between concrete strength and its influencing factors [9,10]. In addition, artificial neural networks are used in the field of plant diseases control [11][12][13], process control and optimization [14][15][16], troubleshooting [17][18][19], intelligent control of industrial product assembly line [20][21][22], robotic surgery [23][24][25], intelligent driving [26][27][28], chemical product development [29][30][31], signal processing [32][33][34], and so on.…”

Section: The Origin and Development Of Artificial Neural Networkmentioning

confidence: 99%

Optimization of Extraction Process Based on Neural Network

Sun

Chen

2022

AJOCS

View full text Add to dashboard Cite

Liquid-liquid extraction is a chemical unit operation that utilizes the difference in solubility or distribution ratio of target components in two immiscible solvents to achieve separation, extraction or purification. There are many factors that affect the extraction efficiency, and it is difficult to quickly optimize the process using traditional methods. Artificial neural network is a system structure composed of multiple artificial neuron models, with functions such as self-learning, associative storage and fault tolerance. It can be used for optimization or control of multi-variable complex systems, and has been successfully applied to the extraction process of various products. optimization. This paper discusses the basic situation of artificial neural network, and analyzes the research progress of extraction process optimization based on neural network.

show abstract

“…In 2016, the emergence of AlphaGo brought people's research enthusiasm for deep learning to a new height. So far, artificial neural networks have been widely used in various fields, such as deep learning and game theory [21][22][23], process control and optimization [24][25][26], face recognition [27][28][29], forecasting [30][31][32], fault detection [33][34][35], image processing [36][37][38] and other fields [39,40]. The application of artificial neural network in the field of chemical process control and optimization will be reviewed below.…”

Section: Fig 1 the Structure Of Artificial Neural Networkmentioning

confidence: 99%

Research Progress of Chemical Process Control and Optimization Based on Neural Network

Zhao

2021

JERR

View full text Add to dashboard Cite

Chemical process is usually regarded as a comprehensive system and optimized as a whole because of the interaction and restriction between its operation units. The classical control technology is limited to the control system dealing with single variable. Artificial neural network (ANN) is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural networks for information processing. It has the advantages of nonlinear, large-scale, and strong parallel processing capabilities, as well as robustness. This article summarizes the basic principles and development history of ANN, and analyzes the research progress of chemical process control and optimization based on artificial neural networks in recent years.

show abstract

Research on Chemical Process Optimization Based on Artificial Neural Network Algorithm

Cited by 5 publications

References 29 publications

Application of Artificial Neural Networks in Chemical Process Control

Application of Artificial Neural Networks in Chemical Process Control

Optimization of Extraction Process Based on Neural Network

Research Progress of Chemical Process Control and Optimization Based on Neural Network

Contact Info

Product

Resources

About