Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management

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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Application of Echo State Neural Networks to forecast blast furnace gas production: pave the way to off-gas optimized management

Energy Procedia ◽

10.1016/j.egypro.2019.01.835 ◽

2019 ◽

Vol 158 ◽

pp. 4037-4042 ◽

Cited By ~ 3

Author(s):

Ismael Matino ◽

Stefano Dettori ◽

Valentina Colla ◽

Valentine Weber ◽

Sahar Salame

Keyword(s):

Neural Networks ◽

Blast Furnace ◽

Gas Production ◽

Blast Furnace Gas ◽

The Way

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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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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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Prediction of Blast Furnace Gas Output Based on GA-Elman Neural Network

10.1109/ccdc52312.2021.9602694 ◽

2021 ◽

Author(s):

Yong Zhu ◽

Ruijie Ma ◽

Dinghui Wu ◽

Yanxia Shen

Keyword(s):

Neural Network ◽

Blast Furnace ◽

Elman Neural Network ◽

Blast Furnace Gas

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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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