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.

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.

Classification of High Quality Carbon Steel Based on BP Neural Network

2013 ◽  
Vol 873 ◽  
pp. 54-59
Author(s):  
Lan Lan Liu ◽  
Tao Hong Zhang ◽  
Yong Hong Xie ◽  
Li Li ◽  
De Zheng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Steel ◽  
Bp Neural Network ◽  
Comprehensive Evaluation ◽  
Practical Application ◽  
High Quality ◽  
Standard Data ◽  
The Neural Network ◽  
Neural Network Algorithm

Now carbon steel is used in the engineering aspects and it is the oldest and the largest amount of basic materials. How to determine whether they are high-quality carbon steel? In this paper the standard data of high quality carbon steel by using the classical BP neural network algorithm is researched. Then it is simulated and predicted. The final comprehensive evaluation and analysis show that the neural network model can be used to decide whether it is a high quality carbon steel. Further, it has a good practical application value for utilizing high-quality carbon steel rationally.

BP Neural Network Research Based on Three Convergence Improved LM Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1543-1546 ◽  
Author(s):  
Xiu Cai Guo ◽  
Sai Hua Shang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Goodness Of Fit ◽  
Optimization Theory ◽  
Mathematical Optimization ◽  
National Sample ◽  
Convergence Time ◽  
Practical Application ◽  
Application Problem ◽  
Lm Algorithm

In order to solve the practical application problem, which traditional neural network takes too long and compute complexly, on the basis of the LM algorithm, combined with mathematical optimization theory, identify the three convergence Improved LM algorithm applied to BP neural network , that improved LMBP algorithm. Simulation results show that the improved LMBP algorithm in convergence time and goodness of fit both have better results, and the algorithm is general and can be produced by obtaining national sample of various scenarios, using the algorithm to predict, to better guidance on production.

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.

Research on Fixed Route Speed Optimization Based on Deep Neural Network and Genetic Algorithm

2020 ◽  
Author(s):  
Ziming Wang ◽  
Shunhuai Chen ◽  
Liang Luo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Bp Neural Network ◽  
Optimization Model ◽  
Power Model ◽  
Multi Objective ◽  
Speed Optimization ◽  
Main Engine ◽  
Engine Power

Abstract In the downturn of the shipping industry, optimizing the speed of ships sailing on fixed routes has important practical significance for reducing operating costs. Based on the ship-engine-propeller matching relationship, this paper uses BP neural network to build main engine power model, and correction factors are introduced into the main engine power model to reflect the influence of wind and wave. The Kalman filter algorithm is used to filter the data collected by a river-sea direct ship during the voyage from Zhoushan to Zhangjiagang. The filtered data and the meteorological data obtained from the European Medium-Range Weather Forecast Center are used as the data set of the BP neural network to predict the main engine power. Based on the main engine power model, a multi-objective optimization model of ship speed under the influence of actual wind and waves was established to solve the conflicting goals of reducing sailing time and reducing main engine fuel consumption. This multi-objective model is solved by a non-dominated fast sorting multi-objective genetic algorithm to obtain the Pareto optimal solution set, thereby obtaining the optimal speed optimization scheme. Compared with the original navigation scheme, the navigation time is reduced by 8.83%, and the fuel consumption of the main engine is reduced by 12.95%. The results show that the optimization model can effectively reduce the fuel consumption and control the sailing time, which verifies the effectiveness of the algorithm.

Research on Risk Assessment of Farmers’ Microcredit Based on BP Neural Network

2011 ◽  
Vol 121-126 ◽  
pp. 1068-1072
Author(s):  
Wei Ji ◽  
Xue Fang Zhang
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Index System ◽  
Practical Application ◽  
Assessment Index ◽  
Rural Credit ◽  
Assessment Index System ◽  
Essential Support

Our government always pushes hard forward the farmers’ microcredit of the rural credit cooperation. But actually, its risk is higher than other kinds of loan. Based on analyzing the risk of farmers’ microcredit, this paper builds risk assessment index system of farmers’ microcredit by using BP neural network. Through the paper, it can provide essential support and practical application for relative departments of farmers’ microcredit.

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

2021 ◽  
pp. 095440702110674
Author(s):  
Bingjiao Liu ◽  
Qin Shi ◽  
Zejia He ◽  
Yujiang Wei ◽  
Duoyang Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Driving Cycle ◽  
Electric System ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Hybrid Electric

This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

Prediction of Emission Performance in a Diesel Engine Fuelled with Bio-Diesel Based on Double-Hidden Layer BP Neural Network

2013 ◽  
Vol 278-280 ◽  
pp. 370-373
Author(s):  
Dong Tang ◽  
Ya Chao Xu ◽  
Shu De Yao ◽  
Chang Yuan Li ◽  
Nan Li
Keyword(s):  
Neural Network ◽  
Diesel Engine ◽  
Fuel Consumption ◽  
Bp Neural Network ◽  
Consumption Rate ◽  
Emission Performance ◽  
Fuel Consumption Rate ◽  
Soot Emissions ◽  
Predicted Values ◽  
Hidden Layer

Based on BP neural network relevant theories, using the fuel consumption, the load and the diesel blended rate as input parameters and measured CO, HC, NOx and soot emission data from bench tests of 180FA diesel engine under various operating conditions as training samples, a double-hidden layer BP neural network model for emission performance in a diesel engine fuelled with bio-diesel was established. The results show that the prediction results of CO, HC, NOx and soot emissions have a good agreement with their experimental ones, and correlation coefficients (R) are very high. It is further shown that the predicted values of HC and CO emissions increase as fuel consumption rate increase, and the predicted values of NOx and soot emissions decrease with the increase of fuel consumption rate.

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