Using BP Neural Network to Predict the Sinter Comprehensive Performance: FeO and Sinter Yield

2013 ◽  
Vol 771 ◽  
pp. 209-212
Author(s):  
Wei Chen ◽  
Bao Xiang Wang ◽  
Ying Chen ◽  
Hui Juan Zhang ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Chemical Composition ◽  
Bp Neural Network ◽  
High Accuracy ◽  
Raw Material ◽  
Production Data ◽  
Prediction System ◽  
Accuracy Rate ◽  
Actual Production ◽  
Comprehensive Performance

Sinter is the main raw material for ironmaking. It is very important to control sinter chemical composition and comprehensive performance. In this paper, a predictive system for sinter chemical composition FeO and the sinter yield was established based on BP neural network, which was trained by actual production data. The MATLAB m file editor was used to write code directly in this paper.The application results show that the prediction system has high accuracy rate, stability and reliability, the sintering productivity was improved effectively.

Using BP Neural Network to Predict the Sinter Comprehensive Performance: TFe and Fuel Consumption

2013 ◽  
Vol 771 ◽  
pp. 213-216
Author(s):  
Wei Chen ◽  
Bao Xiang Wang ◽  
Ying Chen ◽  
Hui Juan Zhang ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Chemical Composition ◽  
Fuel Consumption ◽  
Bp Neural Network ◽  
Raw Material ◽  
High Rate ◽  
Production Data ◽  
Practical Application ◽  
High Quality ◽  
Comprehensive Performance

The principal objective of blast furnace is to produce high quality molten iron at a high rate with a low consumption. It is very important to control sinter chemical composition and comprehensive performance. This is because the sinter is the main raw material for ironmaking. In this paper, a predictive system for sinter chemical composition TFe and the solid fuel consumption was established based on BP neural network, which was trained by actual production data. The MATLAB m file editor was used to write code directly in this paper. Practical application shows the applications of the system not only can reduce the work difficulty of technical personnel, but also can improve the hit ratio of production index and the productivity.

Prediction Model of the Sinter Comprehensive Performance Based on Neural Network

2013 ◽  
Vol 753-755 ◽  
pp. 62-65 ◽  
Author(s):  
Wei Chen ◽  
Hui Juan Zhang ◽  
Bao Xiang Wang ◽  
Ying Chen ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Chemical Composition ◽  
Bp Neural Network ◽  
Steel Industry ◽  
Iron And Steel Industry ◽  
Accuracy Rate ◽  
Iron And Steel ◽  
Yield Quality ◽  
The Stability

The sinter quality and the stability of composition could directly affect the yield, quality and energy consumption of ironmaking production. It is important for iron and steel industry to steadily control sinter chemical composition and analyze sintering energy consumption. The MATLAB m file editor was used to write code directly in this paper. A predictive system for two important sinter chemical composition (TFe and FeO), sinter output and sintering solid fuel consumption of was established based on BP neural network, which was trained by actual production data.) The application results show that the prediction system has high accuracy rate, stability and reliability, the sintering productivity was improved effectively.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

A Short-Term Power Load Forecasting Method Based on BP Neural Network

2014 ◽  
Vol 494-495 ◽  
pp. 1647-1650 ◽  
Author(s):  
Ling Juan Li ◽  
Wen Huang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
High Speed ◽  
Load Forecasting ◽  
High Accuracy ◽  
Back Propagation Algorithm ◽  
Short Term ◽  
Power Load ◽  
Forecasting Method ◽  
Power Load Forecasting

Short-term power load forecasting is very important for the electric power market, and the forecasting method should have high accuracy and high speed. A three-layer BP neural network has the ability to approximate any N-dimensional continuous function with arbitrary precision. In this paper, a short-term power load forecasting method based on BP neural network is proposed. This method uses the three-layer neural network with single hidden layer as forecast model. In order to improve the training speed of BP neural network and the forecasting efficiency, this method firstly reduces the factors which affect load forecasting by using rough set theory, then takes the reduced data as input variables of the BP neural network model, and gets the forecast value by using back-propagation algorithm. The forecasting results with real data show that the proposed method has high accuracy and low complexity in short-term power load forecasting.

Fabric Sewability Prediction with Kernel PCA Based on SFC-RBFNN

2011 ◽  
Vol 332-334 ◽  
pp. 1143-1153
Author(s):  
Yong Hui Pan ◽  
Fang Bao ◽  
Mao Gang Wu
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Fuzzy Clustering ◽  
Basis Function ◽  
Evaluation System ◽  
High Accuracy ◽  
Kernel Pca ◽  
Prediction System ◽  
Woolen Fabric ◽  
Seam Pucker

By extracting five kernel principal components of fabric FAST (Fabric Assurance by Simple Testing) low mechanical data, this paper proposed a supervised fuzzy clustering radial basis function neural network to construct fabric sewability prediction system. Our experimental results demonstrate that the proposed system could efficiently be used as an objective seam pucker evaluation system with high accuracy and is robust for various structures and mechanical properties of middle-thickness woolen fabric.

Research on Closed-Loop Safety Production System of Hot Oil Pipeline Based on Big Data Mining

2019 ◽  
Author(s):  
Yu Tao ◽  
Li Chuanxian ◽  
Liu Lijun ◽  
Chen Hongjun ◽  
Guo Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Long Distance ◽  
Oil Pipeline ◽  
Big Data Mining ◽  
Actual Production

Abstract The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as large error and complex application. This research relies on actual production data and uses big data mining algorithms such as BP neural network, ARMA, seq2seq to establish oil temperature prediction model. The prediction result is less than 0.5 C, which solves the problem of accurate prediction of dynamic oil temperature during pipeline operation. Combined with pigging, the friction prediction model of standard pipeline section is established by BP neural network, and then the economic pigging period of 80 days is given; and after the friction database is established, the historical friction data are analyzed by using the Gauss formula, and 95% of the friction is set as the threshold data to effectively monitor the variation of the friction due to the long period of waxing in pipelines. The closed loop operation system of hot oil pipeline safety and optimization was formed to guide the daily process adjustment and production arrangement of pipeline with energy saving up to 92.4%. The prediction model and research results based on production big data have good adaptability and generalization, which lays a foundation for future intelligent control of pipelines.

Material procedure quality forecast based on genetic BP neural network

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740080 ◽  
Author(s):  
Bao-Hua Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
High Accuracy ◽  
Process Quality ◽  
Quality Prediction ◽  
Fast Convergence Rate ◽  
Material Process ◽  
Quality Forecast

Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

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