Prediction of Converter Gas Generation Based on Intermission Production Improved Elman

doi:10.16182/j.issn1004731x.joss.23-0022

Abstract

Abstract:

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:

TP391.9

Fei Jiajie, Wu Dinghui, Fan Junyan, Wang Jing. Prediction of Converter Gas Generation Based on Intermission Production Improved Elman[J]. Journal of System Simulation, 2024, 36(5): 1179-1188.

Figures/Tables 16

Fig. 1

Fig. 2

Table 1

Fig. 3

Fig. 4

Table 2

Table 3

Experimental results of algorithm parameters

种群规模	$c 1 、 c 2$	MAE/ $(k m 3 / h)$	MAPE/%
30	2.00	8.46	3.35
30	1.49	7.89	2.46
40	2.00	7.44	1.98
40	1.49	7.49	2.42
50	2.00	10.10	4.54
50	1.49	9.49	1.99

Table 3

Table 4

Algorithm parameter setting under various types of intermittent

参数	短	中	长
种群规模	30	40	50
$c 1$ 、 $c 2$	1.49	2.00	1.49

Table 4

Fig. 5

Table 5

Fig. 6

Fig. 7

Table 6

Prediction error of LDG generation with no change of intermittent duration

模型	MAE/( $k m 3 / h)$	MAPE/%
Elman	7.66	7.28
PSO-Elman	5.45	5.32
CPSO-Elman	4.28	4.13

Table 6

Fig. 8

Fig. 9

Table 7

Prediction error of LDG generation under intermittent duration variation

模型	MAE/( $k m 3 / h$ )	MAPE/%
Elman	10.01	9.16
PSO-Elman	7.55	6.79
CPSO-Elman	6.47	5.14

Table 7

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间歇类别	个数	占比/%	持续时间/min
总间歇	1 390	100	20~100
短间歇	6	0.4	<25
中间歇	971	69.9	25~45
长间歇	413	29.7	>45

优化算法	迭代次数	适应度值
PSO	26	464.68
CPSO	23	451.55

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