Improved Wavelet Neural Network to Predict Blast Furnace Gas Production in Iron and Steel Enterprises

Dynamic optimization scheduling of the gas in iron and steel enterprises has great significance to reduce gas emission and the short-term forecast is the premise to realize the energy dynamic scheduling. Based on the characteristics that the influencing factors of blast furnace gas amount are complex and difficult to collect, a grey radial basis function (RBF) neural network forecast model is proposed to predict the gas amount for blast furnace in this paper. Combining grey theory, which is used to preprocess the historical data and obtain abundant information, with RBF neural network makes the effective trend forecast in the next 30 minutes come true. The model proposed in this paper is proved to be more accurate according to control experiments against the grey BP neural network.

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Supply and Demand Forecasting of Blast Furnace Gas Based on Artificial Neural Network in Iron and Steel Works

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.443-444.183 ◽

2012 ◽

Vol 443-444 ◽

pp. 183-188 ◽

Cited By ~ 2

Author(s):

Qi Zhang ◽

Yan Liang Gu ◽

Wei Ti ◽

Jiu Ju Cai

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Blast Furnace ◽

Supply And Demand ◽

Demand Forecasting ◽

Iron And Steel ◽

Blast Furnace Gas ◽

Artificial Neural ◽

Iron And Steel Works ◽

Steel Works

Abstract.Blast Furnace Gas (BFG) system of an iron and steel works was considered. The relationship of gas amount and factors about BFG generation and consumption was analyzed by grey correlationand the BP neural network prediction model of blast furnace gaswas established based on artificial neural network for forecasting thesupply and demandof BFGinthe iron and steel-making processes.The scientific forecasting of BFG generation and consumption in each process was discussed undernormal production and accidental maintenance condition. The results show that established forecasting model is high precision, small errors, and can solve effectively actual production of BFG prediction problem and decreasing BFG flare, providing theoretical basis for establishing reasonable plans in the iron and steel works.

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Experimental Study on Zinc Leaching of Blast Furnace Gas Ash by Ammonia Leaching

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.962-965.780 ◽

2014 ◽

Vol 962-965 ◽

pp. 780-783

Author(s):

Jie Wang ◽

Wei Xiong ◽

Bao Ping Zhang

Keyword(s):

Blast Furnace ◽

Removal Rate ◽

Influence Factors ◽

Environmental Benefits ◽

Main Task ◽

Leaching Rate ◽

Iron And Steel ◽

Leaching Process ◽

Blast Furnace Gas ◽

Ammonia Leaching

Blast furnace gas ash is one of the main solid wastes in iron and steel enterprise. The recovery of zinc from gas ash can result in considerable economic and environmental benefits. The effect of NH3/NH4+, L/S, [NH3]T and leaching time on the zinc leaching rate of blast furnace gas ash by using ammonia leaching process had been investigated in this paper. The results show that L/S and NH3/NH4+ are the main influence factors under the experimental condition. The optimal conditions for leaching process are: NH3/NH4+=2:1, L/S=4:1, [NH3]T =5mol/L, and the leaching time is 3 hours. The zinc leaching rate is 82.84% under the optimized conditions. The lead content in leaching solution is high, so the main task is to improve the removal rate of lead in the purification process.

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Dynamic Analysis of Blast Furnace Sensor Data using Cross-recurrence Quantification Strategies

Journal of Physics Conference Series ◽

10.1088/1742-6596/2132/1/012024 ◽

2021 ◽

Vol 2132 (1) ◽

pp. 012024

Author(s):

X C Sun ◽

B Wei ◽

J h Gao ◽

J C Fu ◽

Z G Li

Keyword(s):

Time Series ◽

Blast Furnace ◽

Dimensional Space ◽

Recurrence Plot ◽

Analytical Framework ◽

Gas Production ◽

Sensor Data ◽

Blast Furnace Gas ◽

Recurrence Quantification ◽

Cross Recurrence Quantification

Abstract This paper investigates impact degree of blast furnace related elements towards blast furnace gas (BFG) production. BFG is a by-product in the steel industry, which is one of the enterprise’s most essential energy resources. While because multiple factors affect BFG production it has characteristics of large fluctuations. Most works focus on finding a satisfactory method or improving the accuracy of existing methods to predict BFG production. There are no special studies on the factors that affect the production of BFG. Finding the elements that affect BFG production is benefit to production of BFG, which has a significance in economy. We propose a novel framework, combining cross recurrence plot (CRP) and cross recurrence quantification analysis (CRQA). Moreover, it supplies a general method to convert time series of BFG related data into high-dimensional space. This is the first analytical framework that attempts to reveal the inherent dynamic similarities of blast furnace gas-related elements. The experimental results demonstrate that this framework can realize the visualization of the time series. In addition, the results also identify the factor that has the greatest impact on blast furnace gas production by quantitative analysis.

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A hybrid gene algorithm for byproduct blast furnace gas scheduling in iron and steel production

2011 IEEE International Conference on Computer Science and Automation Engineering ◽

10.1109/csae.2011.5952829 ◽

2011 ◽

Cited By ~ 2

Author(s):

Cantao Shi ◽

Ningning Wang ◽

Tieke Li

Keyword(s):

Blast Furnace ◽

Steel Production ◽

Hybrid Gene ◽

Iron And Steel ◽

Blast Furnace Gas ◽

Iron And Steel Production

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CO2 Recycling in the Iron and Steel Industry via Power-to-Gas and Oxy-Fuel Combustion

Energies ◽

10.3390/en14217090 ◽

2021 ◽

Vol 14 (21) ◽

pp. 7090

Author(s):

Jorge Perpiñán ◽

Manuel Bailera ◽

Luis M. Romeo ◽

Begoña Peña ◽

Valerie Eveloy

Keyword(s):

Blast Furnace ◽

Steel Industry ◽

Carbon Capture ◽

Base Case ◽

Iron And Steel Industry ◽

Iron And Steel ◽

Blast Furnace Gas ◽

Coal Fuel ◽

Steel Sector ◽

Power To Gas

The iron and steel industry is the largest energy-consuming sector in the world. It is responsible for emitting 4–5% of the total anthropogenic CO2. As an energy-intensive industry, it is essential that the iron and steel sector accomplishes important carbon emission reduction. Carbon capture is one of the most promising alternatives to achieve this aim. Moreover, if carbon utilization via power-to-gas is integrated with carbon capture, there could be a significant increase in the interest of this alternative in the iron and steel sector. This paper presents several simulations to integrate oxy-fuel processes and power-to-gas in a steel plant, and compares gas productions (coke oven gas, blast furnace gas, and blast oxygen furnace gas), energy requirements, and carbon reduction with a base case in order to obtain the technical feasibility of the proposals. Two different power-to-gas technology implementations were selected, together with the oxy blast furnace and the top gas recycling technologies. These integrations are based on three strategies: (i) converting the blast furnace (BF) process into an oxy-fuel process, (ii) recirculating blast furnace gas (BFG) back to the BF itself, and (iii) using a methanation process to generate CH4 and also introduce it to the BF. Applying these improvements to the steel industry, we achieved reductions in CO2 emissions of up to 8%, and reductions in coal fuel consumption of 12.8%. On the basis of the results, we are able to conclude that the energy required to achieve the above emission savings could be as low as 4.9 MJ/kg CO2 for the second implementation. These values highlight the importance of carrying out future research in the implementation of carbon capture and power-to-gas in the industrial sector.

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Gas Production Strategy of Underground Coal Gasification Based on Multiple Gas Sources

The Scientific World JOURNAL ◽

10.1155/2014/154197 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Duan Tianhong ◽

Wang Zuotang ◽

Zhou Limin ◽

Li Dongdong

Keyword(s):

Power Generation ◽

Blast Furnace ◽

Coal Gasification ◽

Field Tests ◽

Coke Oven ◽

Gas Production ◽

Underground Coal Gasification ◽

Coke Oven Gas ◽

Blast Furnace Gas ◽

Converter Gas

To lower stability requirement of gas production in UCG (underground coal gasification), create better space and opportunities of development for UCG, an emerging sunrise industry, in its initial stage, and reduce the emission of blast furnace gas, converter gas, and coke oven gas, this paper, for the first time, puts forward a new mode of utilization of multiple gas sources mainly including ground gasifier gas, UCG gas, blast furnace gas, converter gas, and coke oven gas and the new mode was demonstrated by field tests. According to the field tests, the existing power generation technology can fully adapt to situation of high hydrogen, low calorific value, and gas output fluctuation in the gas production in UCG in multiple-gas-sources power generation; there are large fluctuations and air can serve as a gasifying agent; the gas production of UCG in the mode of both power and methanol based on multiple gas sources has a strict requirement for stability. It was demonstrated by the field tests that the fluctuations in gas production in UCG can be well monitored through a quality control chart method.

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