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Sensors 2018, 18(2), 625; https://doi.org/10.3390/s18020625

The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization

1
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 3 January 2018 / Revised: 14 February 2018 / Accepted: 15 February 2018 / Published: 20 February 2018
(This article belongs to the Special Issue Sensors and Materials for Harsh Environments)

Abstract

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control. View Full-Text
Keywords: data-driven model; gas utilization ratio; TS fuzzy neural network (TS-FNN); particle swarm optimization (PSO) algorithm; blast furnace (BF) data-driven model; gas utilization ratio; TS fuzzy neural network (TS-FNN); particle swarm optimization (PSO) algorithm; blast furnace (BF)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhang, S.; Jiang, H.; Yin, Y.; Xiao, W.; Zhao, B. The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization. Sensors 2018, 18, 625.

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