Prediction of Gas Utilization Ratio Based on the Kernel Extreme Learning Machine

Gas utilization ratio (GUR) is a significant indicator to measure the operation status and energy consumption of blast furnaces (BFs). Accurately predicting the GUR can reflect the actual operating status of the BFs and the consumption of the charge in real time. Kernel extreme learning machine (KELM) algorithm not only has the characteristics of fast computation speed of extreme learning machine (ELM), but also has better stability and generalization ability. This study applies KELM to investigate the relationship between GUR and some significant factors which affect GUR. An improved fruit fly optimization algorithm (IFOA) is used to optimize the parameters in the KELM model. The experimental results demonstrate that the prediction model based on the KELM has better prediction effect in forecasting accuracy and modeling time needed.

[1]  Yin Han.  More Discussion on Evaluation Method for Productive Efficiency of Ironmaking Blast Furnace , 2013 .

[2]  Zuo Haibin,et al.  Scientific Development Strategy of Blast Furnace Ironmaking Technology in China , 2008 .

[3]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Wan-Yu Deng,et al.  Cross-person activity recognition using reduced kernel extreme learning machine , 2014, Neural Networks.

[5]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[6]  Lei Wang,et al.  Multiple kernel extreme learning machine , 2015, Neurocomputing.

[7]  Yong He,et al.  Chaotic prediction of carbon-monoxide utilization ratio in the blast furnace , 2016, CCC 2016.

[8]  Lin Shi,et al.  Recognition of blast furnace gas flow center distribution based on infrared image processing , 2016 .

[9]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[10]  D. Ni,et al.  Effect of superfine grinding on quality and antioxidant property of fine green tea powders , 2012 .

[11]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[12]  Huang Yalou.  Nonlinear network traffic prediction based on BP neural network , 2007 .

[13]  Zhang Jian.  Increasing Utilization Rate of Blast Furnace Gas to Decrease Energy Consumption of Ironmaking Process , 2005 .