scholarly journals Neural Network Algorithm and Its Application in Supercritical Extraction Process

2021 ◽  
pp. 19-28
Author(s):  
Yu Qi ◽  
Zhaolan Zheng
Keyword(s):  
Neural Network ◽  
Separation Efficiency ◽  
Supercritical Extraction ◽  
Research Progress ◽  
Process Conditions ◽  
Network Algorithms ◽  
Network Algorithm ◽  
Optimization And Control ◽  
Neural Network Algorithm ◽  
And Control

Artificial neural network （ANN）algorithms can be used for multi-parameter optimization and control by simulating the mechanisms of the human brain. Therefore, ANN is widely used in many fields such as signal processing, intelligent driving, face recognition, and optimization and control of chemical processes. As a green and efficient chemical separation process, supercritical extraction is especially suitable for the separation and purification of active ingredients in natural substances. Because there are many parameters that affect the separation efficiency of the process, the neural network algorithm can be used to quickly optimize the process parameters based on limited experimental data to determine the appropriate process conditions. In this work, the research progress of neural network algorithms and supercritical extraction are reviewed, and the application of neural network algorithms in supercritical extraction is discussed, aiming to provide references for researchers in related fields.

scholarly journals Review of Neural Network Algorithm and Its Application in Reactive Distillation

2021 ◽  
pp. 20-29
Author(s):  
Huihui Wang ◽  
Ruyang Mo
Keyword(s):  
Neural Network ◽  
Reactive Distillation ◽  
Research Progress ◽  
Paper Briefly ◽  
Network Algorithms ◽  
Highly Nonlinear ◽  
Learning Capabilities ◽  
Neural Network Algorithm ◽  
Self Learning ◽  
Nonlinear Robust

Artificial Neural Networks (ANN) can accurately identify and learn the potential relationship between input and output, and have self-learning capabilities and high fault tolerance, which can be used to predict or optimize the performance of complex systems. Reactive distillation integrates reaction and rectification into one device, so that the two processes occur at the same time and at the same place, but at the same time it also produces highly nonlinear robust behavior, making its process control and optimization unable to use conventional methods. Instead, neural network algorithms must be used. This paper briefly describes the research progress of neural network algorithms and reactive distillation technology, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.

scholarly journals Probabilistic Neural Network based Managing Algorithm for Building Automation System

2021 ◽  
Vol 3 (4) ◽  
pp. 272-283
Author(s):  
Subarna Shakya
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Probabilistic Neural Network ◽  
Automation System ◽  
Building Automation ◽  
Network Algorithms ◽  
Network Algorithm ◽  
Building Automation System ◽  
Software Modules ◽  
Neural Network Algorithm

A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural network algorithm which has the ability to take decisions same as like of human brains in an efficient way. Hence a building automation system is proposed in the work based on PNN for verifying the effectiveness of neural network algorithms over the traditional pre-determined decision making algorithms. The experimental work is further extended to verify the performances of the basic neural network algorithm called Convolution Neural Network (CNN).

scholarly journals A Study on the Application and the Advancement of Deep Neural Network Algorithm

2022 ◽  
Vol 2146 (1) ◽  
pp. 012001
Author(s):  
Jiulin Song ◽  
Yansheng Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Real Life ◽  
Research Area ◽  
Network Algorithms ◽  
Network Algorithm ◽  
New Type ◽  
Neural Network Algorithm ◽  
Global And Local

Abstract Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.

scholarly journals Study on the Simulation Control of Neural Network Algorithm in Thermally Coupled Distillation

2021 ◽  
pp. 53-64
Author(s):  
Zhao Lan Zheng ◽  
Yu Qi
Keyword(s):  
Neural Network ◽  
Dynamic Performance ◽  
Optimization Method ◽  
Network Control ◽  
Research Progress ◽  
Thermally Coupled Distillation ◽  
Fast Learning ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Distillation Simulation

Thermally coupled distillation is a new energy-saving method, but the traditional thermally coupled distillation simulation calculation process is complicated, and the optimization method based on the traditional simulation process is difficult to obtain a good feasible solution. The neural network algorithm has the advantages of fast learning and can approach nonlinear functions arbitrarily. For the problems in complex process control systems, neural network control does not require cumbersome control structures or precise mathematical models. When training the network, only the input and output samples it needs are given, so that the dynamics of the system can be controlled. In this way, the dynamic performance of the system can be approximated. This method can effectively solve the mathematical model of the thermally coupled distillation process, and quickly obtain the solution of the optimized variables and the objective function. This article summarizes the research progress of artificial neural network and the optimization control of thermally coupled distillation and the application of neural network in thermally coupled distillation.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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