Neural Network Based Breakout Prediction System in Continuous Casting Shop

2015 ◽  
Vol 813-814 ◽  
pp. 550-556
Author(s):  
Md Obaidullah Ansari ◽  
Rajashree Samantray ◽  
Joyjeet Ghose
Keyword(s):  
Neural Network ◽  
Continuous Casting ◽  
Network Model ◽  
Molten Steel ◽  
Neural Network Model ◽  
Confusion Matrix ◽  
Excellent Result ◽  
Back Propagation ◽  
Prediction System ◽  
The Neural Network

— Continuous casting of steel is a process in which liquid steel is continuously solidified into semi-finished or finished product (slabs, blooms or billets). There are many problems associated with continuous casting shop which affect the casting process. A major problem is associated in continuous casting shop is breakout of molten steel. Breakout leads to temporary shutdown of caster, damage of machinery due to splash of molten steel, capital loss, safety hazards etc. In Bokaro steel plant a logical based breakout prediction system is used to predict the breakout. This system sometimes generates false alarm and sometimes even fail to generate an alarm before breakout. Also the logical model has lot of dependence on specific equipment, process and calibration. Neural network can be implemented for a better breakout prediction system. So, in this paper a back-propagation neural network model is developed for predicting the existence of primary cracks that might lead to a breakout. The network gets its input temperatures from thermocouples which are attached to the wide and narrow sides of the mould. The output of the neural network is either logic 1 (for presence of crack) or logic 0 (for no crack). Testing the network shows excellent result as evident from the confusion matrix and performance plot. The neural network model is validated by simulating in MatLab/Simulink. The developed network may be used effectively in predicting breakouts during continuous casting. Such effective prediction can go a long way in reducing production losses in steel manufacturing.

Modelling of viscosity of fluorine-free mold fluxes using neural network

2019 ◽  
Vol 116 (2) ◽  
pp. 201
Author(s):  
Xiaoli Yuan ◽  
Lin Wang ◽  
Jianqiang Zhang ◽  
Oleg Ostrovski ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Viscosity Data ◽  
The Neural Network ◽  
Mold Fluxes ◽  
Experimental Values ◽  
Bp Neural Network Model

Viscosity is an important property of mold fluxes for steel continuous casting. However, direct measurement of viscosity of multi-component systems in a broad range of temperatures and compositions is an onerous work and has some limitations. This paper developed a model using the back propagation (BP) neural network to describe the viscosity of fluorine-free mold fluxes. The BP neural network model was developed and validated using 70 experimental values of viscosity of fluorine-free mold fluxes CaO-SiO2-Al2O3-B2O3-Na2O-TiO2-MgO-Li2O-MnO-ZrO2; 51 of them were used for developing the neural network model and the rest 19 viscosity data for the model validation. Calculated viscosities were in a good agreement with the experimental data. Based on the developed model, the effects of temperature and composition on the viscosity of fluorine-free fluxes were predicted and discussed.

Application of ANN Back-Propagation for Fracture Design Parameters of Middle Carbon Steel in Extra-Low Cycle Bend Torsion Loading

2013 ◽  
Vol 345 ◽  
pp. 272-276 ◽  
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Zhi Jia Sun ◽  
Yang Yang Zhang
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
The Neural Network

The fracture problems of medium carbon steel (MCS) under extra-low cycle bend torsion loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Application of ANN Back-Propagation for An Alloy Reinforced Ceramics/Metal Composite under Extra-Low Cycle Bending Fatigue Loading

2010 ◽  
Vol 105-106 ◽  
pp. 108-111
Author(s):  
Zhi Yuan Rui ◽  
Hong Yan Duan ◽  
Chun Li Lei ◽  
Xing Chun Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Propagation Model ◽  
Training Data ◽  
Design Parameters ◽  
Metal Composite ◽  
Ann Model ◽  
The Neural Network

Artificial neural network (ANN) back-propagation model was developed to predict the fracture design parameters in reinforced ceramic matrix composites (CMCS).Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, the presetting deflection and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The ANN model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used.

scholarly journals BP Neural Network Algorithms for Fault Diagnosis of Microwave Components

2019 ◽  
Vol 95 ◽  
pp. 04008
Author(s):  
Gao Kun ◽  
Wang Aimin ◽  
Ge Yan
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Main Trend ◽  
Intelligent Fault Diagnosis ◽  
The Neural Network ◽  
Training Samples

Intelligent diagnosis is the main trend of modern fault diagnosis technology. The emergence of artificial neural network technology provides a new way for this kind of intellectualization. Aiming at the problem of microwave module fault diagnosis, an intelligent fault diagnosis method based on BP(Back Propagation) neural network is proposed in this paper. In this paper, the process of determining the neural network model and the operation flow of BP algorithm are introduced, and the network is trained with training samples. By applying the neural network model to an AQ module for testing, the feasibility, accuracy and efficiency of the fault diagnosis of the microwave module are verified, which provides a new method for intelligent fault diagnosis of this kind of microwave module.

scholarly journals Classification and Forecasting of Bollywood Movies by Commercial Success using Back-Propagation Neural Network model

2019 ◽  
Vol 9 (2) ◽  
pp. 3600-3607
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Filtering ◽  
Commercial Success ◽  
The Neural Network ◽  
Proposed Model

Forecasting commercial success of motion pictures remained challenging for producers, critics and other industry leaders in this changing world of web and online media. In this study, the author has explored a back-propagation neural network model with 23 numeric input (BPNN-N23) for classification of Bollywood movies released during the years 2014 through 2017. The proposed model classifies movies in three classes namely “HIT”, “AVERAGE” and “FLOP”. Common procedures like data filtering, data cleaning and data normalization have been followed prior to feeding those data to the neural network. After comparing the performance of the proposed model with the benchmark models and works, the results show that the said model shows performance that is comparable to the published ones with respect to the assumed Indian empirical settings. This research reveals the extent of the effects and roles of the considered factors as well as the proposed model in predicting the fate of a Bollywood movie in India.

Research of Gasoline Engine Ignition Control Based on Neural Network

2009 ◽  
Vol 626-627 ◽  
pp. 501-504
Author(s):  
Y.Y. Wang ◽  
Jian Guo Yang ◽  
B.Y. Song
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Performance ◽  
Gasoline Engine ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
The Neural Network

In order to realize the precise ignition control of gasoline engine, an ignition advance angle BP (Back Propagation) neural network model is built. The improved LM (Levenberg-Marquardt) learning algorithm is used in the model to increase the neural network performance. The neural network model is trained and tested by matlab program. For a variety of inputs, the trained ignition advance angle neural network can carry out correct outputs. Compared with the experimental ignition advance angle, the maximum error of the neural network ignition advance angle is less than 5%. Compared with the experimental map method, the ignition advance angle neural network has the advantage of online modifying the value of ignition advance angle, so it can make the gasoline engine acquire the best ignition advance angle on various working conditions. The results show that the ignition advance angle neural network model established in this paper is effective and accurate. The performance of gasoline engine can be improved ultimately.

Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning

2019 ◽  
Vol 2673 (4) ◽  
pp. 108-116 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Safety Management ◽  
Estimation Method ◽  
Estimation Model ◽  
The Neural Network ◽  
Annual Average Daily Traffic

Safety Performance Functions (SPFs) are regression models used to predict the expected number of collisions as a function of various traffic and geometric characteristics. One of the integral components in developing SPFs is the availability of accurate exposure factors, that is, annual average daily traffic (AADT). However, AADTs are not often available for minor roads at rural intersections. This study aims to develop a robust AADT estimation model using a deep neural network. A total of 1,350 rural four-legged, stop-controlled intersections from the Province of Alberta, Canada, were used to train the neural network. The results of the deep neural network model were compared with the traditional estimation method, which uses linear regression. The results indicated that the deep neural network model improved the estimation of minor roads’ AADT by 35% when compared with the traditional method. Furthermore, SPFs developed using linear regression resulted in models with statistically insignificant AADTs on minor roads. Conversely, the SPF developed using the neural network provided a better fit to the data with both AADTs on minor and major roads being statistically significant variables. The findings indicated that the proposed model could enhance the predictive power of the SPF and therefore improve the decision-making process since SPFs are used in all parts of the safety management process.

Sign in / Sign up

Export Citation Format

Share Document