Journal of System Simulation ›› 2024, Vol. 36 ›› Issue (5): 1179-1188.doi: 10.16182/j.issn1004731x.joss.23-0022

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Prediction of Converter Gas Generation Based on Intermission Production Improved Elman

Fei Jiajie1(), Wu Dinghui1(), Fan Junyan1, Wang Jing2   

  1. 1.College of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
    2.Shanghai Baosight Software Co. Ltd, Shanghai 201203, China
  • Received:2023-01-06 Revised:2023-02-13 Online:2024-05-15 Published:2024-05-21
  • Contact: Wu Dinghui;


Aiming at large fluctuations of intermission and low prediction accuracy in iron and steel industry, based on the classification of intermission characteristics, a converter gas generation predicting model(CPSO-Elman) based on Elman neural network(ENN) optimized by chaoticPSO(CPSO) algorithm is proposed. The intermittent characteristics of converter gas generation time series are extracted and raw data is classified according to intermittent duration. The PSO algorithm improved by chaotic disturbance is introduced to optimize the initial weight and threshold of ENN and inertia weight of nonlinear updating is designed to balance global search ability and local search ability. Construct the combined prediction model of CPSO-Elman converter gas generation. Converter gas generation is predicted on the basis of predicting the intermission in the future time. Simulation results show that prediction accuracy of the proposed method is about 5% higher than that of the method without optimization.

Key words: converter gas, generation prediction, PSO algorithm, chaotic perturbation, Elman neural network, intermission classification

CLC Number: