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.

Natural Gas Energy Consumption Strategy by Iron and Steel Industry Enterprises

2020 ◽  
Vol 9 (3) ◽  
pp. 59-64
Author(s):  
V. Koksharov ◽  
I. Kirshina
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Steel Industry ◽  
Energy Resource ◽  
Iron And Steel Industry ◽  
Conceptual Approach ◽  
Iron And Steel ◽  
Key Factor ◽  
Gas Consumption ◽  
Natural Gas Consumption

The existing modern conceptual approaches to the formation of an assessment of the natural gas consumption strategy in the steel industry do not allow assessing comprehensively all the processes that occur in the enterprise affecting the efficiency of natural gas consumption. Due to the fact that natural gas acts an important role as a universal energy resource, both in the economy and in international politics, the modern strategy for assessing the consumption of natural gas in the steel industry is becoming a key factor in increasing competitiveness and guaranteeing sustainable economic growth of the country's steel industry. The article proposes a conceptual approach to the development of the model in order to assess the strategy of natural gas consumption at iron and steel enterprises, which allows timely management decisions to be taken to increase the organization of efficient energy consumption at the enterprise. Based on the proposed conceptual approach to assessing the strategy of natural gas consumption at the iron and steel industry, it can be stated that this assessment is interconnected with the assessment of the integral criterion for the implementation of the goals of organizing effective energy management.

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.

Research on Energy Consumption Analysis Method Based on Improved Fuzzy Analytic Hierarchy Process (FAHP) in Iron and Steel Industry

2012 ◽  
Vol 490-495 ◽  
pp. 2396-2399
Author(s):  
Dong Qiu ◽  
Xiao Bo Wang ◽  
Ying Ying Fu
Keyword(s):  
Energy Consumption ◽  
Production Process ◽  
Energy Saving ◽  
Steel Industry ◽  
Production System ◽  
Iron And Steel Industry ◽  
Analytic Hierarchy ◽  
Iron And Steel ◽  
Reduce Energy Consumption ◽  
Hierarchy Process

The relations between production process and energy consumption are made an in-depth application analysis and theory study with theory of systematic energy-saving , technique of intelligence analysis and optimization based on the background of study on energy-saving and reduce energy consumption. Making intensive research on theories of systematic energy-saving and analyzed with function mechanism and quantitative relation of the main production system, auxiliary production system and energy conversion system. It put forward the energy flow direction and energy consumption in each production process and the influence of material flow on energy consumption in iron and steel industry. The sequence of all kinds of material influence comprehensive energy consumption per ton steel and energy quantitative target of all factors influence energy consumption are worked out with improved fuzzy AHP(Analytic Hierarchy Process) which study of comprehensive energy consumption in typical iron and steel production process from the point of system. It has great immediate significance to guide drawing the scientific plan of economy-saving and reduce energy consumption in iron and steel industry

Application of Elman Neural Network with HP Filter in the Trend Supply of Self-Provided Power Plant Forecasting in the Iron and Steel Industry

2013 ◽  
Vol 712-715 ◽  
pp. 3211-3214 ◽  
Author(s):  
Hong Juan Li ◽  
Jian Jun Wang ◽  
Hua Wang ◽  
Hua Meng
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Relative Error ◽  
Steel Industry ◽  
Average Relative Error ◽  
Iron And Steel Industry ◽  
Elman Neural Network ◽  
Iron And Steel ◽  
Hp Filter ◽  
Gas Supply

Aiming at the power plant energy consumption and gas balance influenced serious with the affluent gas fluctuate frequently of byproduct gas system in an iron and steel industry, which is very difficult to be modeled using the mechanism modeling, a forecast trend sequence of the gas supply HP-ENN model was established based on the characteristics of self-provided power plant energy utilization and the properties of HP filter, Elman neural network. The prediction results using practical production data show that using the proposed HP-Elman method that sample A 48, 60 points trend forecast average relative error are 0.37%, 0.47% and sample B 48, 60 points trend forecast average relative error are 0.82%, 1.03%,which can effectively for the trend forecast of self-provided power plant gas supply with a reliable prediction capacity.

scholarly journals Green Development Efficiency and Its Influencing Factors in China’s Iron and Steel Industry

2021 ◽  
Vol 13 (2) ◽  
pp. 510
Author(s):  
Junfeng Zhang ◽  
Jianxu Liu ◽  
Jing Li ◽  
Yuyan Gao ◽  
Chuansong Zhao
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Steel Industry ◽  
Capital Investment ◽  
Driving Forces ◽  
Iron And Steel Industry ◽  
Eastern Region ◽  
Iron And Steel ◽  
Positive Effects ◽  
Green Development

Analyzing the potential for green development and its influencing factors is an important part of the energy savings and low-carbon economic growth of China’s iron and steel industry (ISI). Many studies have concentrated on improving the ISI’s energy use and pollution control efficiencies, analyzing the influencing factors from the perspectives of regions and firms. However, no study has focused on measuring the provincial green development efficiency (GDE) in the ISI. The selected driving forces of the GDE do not consider regional or industrial characteristics. In this study, based on provincial panel data for 2006–2015 in China, the GDE of the Chinese ISI was evaluated using the super-slack-based measure (super-SBM) model. China’s 28 provinces were divided into different groups through cluster analysis. Then, a Tobit model was constructed to explore the factors influencing the GDE. The key results show the following: (1) The GDE values decline, fluctuating from 0.628 in 2006 to 0.571 in 2015, decreasing by 1.1% annually. Among the provinces, wide differences exist in the GDE values for the ISI, with the highest average GDE value being observed in Beijing and the lowest in Shanxi. (2) The provinces with high R&D expenditure inputs and high GDE values are mostly located in the eastern region, while the provinces with low R&D expenditure inputs and low GDE values are located in the central and western regions. (3) The export demand, property structure, and capital investment have significant positive effects on the ISI’s GDE in the eastern and western regions, while the energy consumption structure and industry scale have negative impacts on the improvement of the GDE in the central region. (4) Specific policy recommendations for sustainable development in the ISI mainly include further strengthening investment in R&D, expanding exports, adjusting energy consumption structures, and deepening the reform of stated-own enterprises.

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