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 Research Progress in the Application of Artificial Neural Networks in Catalyst Optimization

2021 ◽  
pp. 34-46
Author(s):  
Zhiqiang Liu ◽  
Wentao Zhou
Keyword(s):  
Neural Network ◽  
Economic Benefits ◽  
Machine Learning Algorithms ◽  
Research Progress ◽  
Target Product ◽  
Learning Capabilities ◽  
Speed Up ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Self Learning

The catalyst can speed up the chemical reaction and increase the selectivity of the target product, playing an important role in the chemical industry. By improving the performance of the catalyst, the economic benefits can be greatly improved. Artificial Neural Network (ANN), as one of the most popular machine learning algorithms, has parallel processing and self-learning capabilities as well as good fault tolerance, and has been widely used in various fields. By optimizing the catalyst through ANN, time and resource consumption can be greatly reduced, and greater economic benefits can be obtained. This article reviews how CNN technology can help people solve highly complex problems and accelerate progress in the catalytic world.

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.

Construction of first-class university course based on artificial intelligence and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Yingli Duan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Vocational Training ◽  
Network Algorithms ◽  
Teaching Efficiency ◽  
Neural Network Algorithm ◽  
Solid Foundation ◽  
University Level ◽  
University Course

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.

Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting

2011 ◽  
Vol 3 (1) ◽  
pp. 45-68 ◽  
Author(s):  
Rashedur M. Rahman ◽  
Ruppa K. Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Price Forecasting ◽  
Insurance Company ◽  
Memory Architecture ◽  
Backpropagation Algorithm ◽  
Network Algorithms ◽  
Training Time ◽  
Stock Price Forecasting ◽  
Neural Network Algorithm

The neural network is popular and used in many areas within the financial field, such as credit authorization screenings, regularities in security price movements, simulations of market behaviour, and so forth. In this research, the authors use a neural network technique for stock price forecasting of Great West Life, an insurance company based in Winnipeg, Canada. The Backpropagation algorithm is a popular algorithm to train a neural network. However, one drawback of traditional Backpropagation algorithm is that it takes a substantial amount of training time. To expedite the training process, the authors design and develop different parallel and multithreaded neural network algorithms. The authors implement parallel neural network algorithms on both shared memory architecture using OpenMP and distributed memory architecture using MPI and analyze the performance of those algorithms. They also compare the results with traditional auto-regression model to establish accuracy.

scholarly journals Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower

2021 ◽  
pp. 19-29
Author(s):  
Ruyang Mo ◽  
Huihui Wang
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Simulation Software ◽  
Network Algorithms ◽  
System Description ◽  
Software Interface ◽  
Operation Speed ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Rectification Process

For some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology.

scholarly journals Review of Neural Network Algorithm and its Application in Temperature Control of Distillation Tower

2021 ◽  
pp. 50-61
Author(s):  
Ningrui Zhao ◽  
Jinwei Lu
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Network Control ◽  
Neural Network Control ◽  
Distillation Tower ◽  
Network Algorithms ◽  
Advantages And Disadvantages ◽  
Nonlinear Hysteresis ◽  
Complex Process ◽  
Neural Network Algorithm

Distillation process is a complex process of conduction, mass transfer and heat conduction, which is mainly manifested as follows: The mechanism is complex and changeable with uncertainty; the process is multivariate and strong coupling; the system is nonlinear, hysteresis and time-varying. Therefore, traditional control methods are difficult to accurately control, but neural networks can greatly improve this problem. This article introduces the basic concepts of distillation tower temperature control, comprehensively introduces the application of various neural network algorithms in distillation tower temperature control, and compares their advantages and disadvantages and their effect. At present, there are many researches on neural network control of distillation tower temperature. The methods are different and each has its own merits. This article has carried out a systematic review to provide reference for the development of related industries.

scholarly journals Multisource Heterogeneous Data Fusion Analysis of Regional Digital Construction Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Mengmeng Jiang ◽  
Qiong Wu ◽  
Xuetao Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Data Fusion ◽  
Heterogeneous Data ◽  
False Alarms ◽  
Network Algorithms ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Fusion Analysis

In modern urban construction, digitalization has become a trend, but the single source of information of traditional algorithms can not meet people’s needs, so the data fusion technology needs to draw estimation and judgment from multisource data to increase the confidence of data, improve reliability, and reduce uncertainty. In order to understand the influencing factors of regional digitalization, this paper conducts multisource heterogeneous data fusion analysis based on regional digitalization of machine learning, using decision tree and artificial neural network algorithm, compares the management efficiency and satisfaction of school population under different algorithms, and understands the data fusion and construction under different algorithms. According to the results, decision-making tree and artificial neural network algorithms were more efficient than traditional methods in building regional digitization, and their magnitude was about 60% higher. More importantly, the machine learning-based methods in multisource heterogeneous data fusion have been better than traditional calculation methods both in computational efficiency and misleading rate with respect to false alarms and missed alarms. This shows that machine learning methods can play an important role in the analysis of multisource heterogeneous data fusion in regional digital construction.

scholarly journals The application of neural network algorithm and embedded system in computer distance teach system

2022 ◽  
Vol 31 (1) ◽  
pp. 148-158
Author(s):  
Qin Qiu
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Learning Ability ◽  
Distance Teaching ◽  
Teaching System ◽  
Network Computer ◽  
Neural Network Algorithm ◽  
Bus Structure ◽  
Self Learning ◽  
Distance Education System

Abstract The computer distance teaching system teaches through the network, and there is no entrance threshold. Any student who is willing to study can log in to the network computer distance teaching system for study at any free time. Neural network has a strong self-learning ability and is an important part of artificial intelligence research. Based on this study, a neural network-embedded architecture based on shared memory and bus structure is proposed. By looking for an alternative method of exp function to improve the speed of radial basis function algorithm, and then by analyzing the judgment conditions in the main loop during the algorithm process, these judgment conditions are modified conditionally to reduce the calculation scale, which can double the speed of the algorithm. Finally, this article verifies the function, performance, and interface of the computer distance education system.

A Comparative Sensor Based Multi-Classes Neural Network Classifications for Human Activity Recognition

2018 ◽  
Vol 22 (5) ◽  
pp. 711-717 ◽  
Author(s):  
Ramtin Aminpour ◽  
◽  
Elmer Dadios
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Real Life ◽  
Human Activity Recognition ◽  
Network Algorithms ◽  
Remote Server ◽  
The People ◽  
Neural Network Algorithm ◽  
And Performance

Human activity recognition with the smartphone could be important for many applications, especially since most of the people use this device in their daily life. A smartphone is a portable gadget with internal sensors and enough hardware power to accommodate this problem. In this paper, three neural network algorithms were compared to detect six major activities. The data are collected by a smartphone in real life and simulated on the remote server. The results show that MLP and GMDH neural network have better accuracy and performance compared with the LVQ neural network algorithm.

An Improved Neural Network Algorithm and its Application in Fault Diagnosis

2013 ◽  
Vol 765-767 ◽  
pp. 2355-2358
Author(s):  
Tai Shan Yan ◽  
Guan Qi Guo ◽  
Wu Li ◽  
Wei He
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Convergence Speed ◽  
Bp Algorithm ◽  
Network Algorithms ◽  
Convergence Error ◽  
Neural Network Algorithm ◽  
Transformer Fault ◽  
Improved Bp Algorithm

Aiming at BP neural network algorithms limitation such as falling into local minimum easily and low convergence speed, an improved BP algorithm with two times adaptive adjust of training parameters (TA-BP algorithm) was proposed. Besides the adaptive adjust of training rate and momentum factor, this algorithm can gain appropriate permitted convergence error by adaptive adjust in the course of training. TA-BP algorithm was applied in fault diagnosis of power transformer. A fault diagnosis model for power transformer was founded based on neural network. The illustrational results show that this algorithm is better than traditional BP algorithm in both convergence speed and precision. We can realize a fast and accurate diagnosis for power transformer fault by this algorithm.

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