Modeling and Simulation of Ultra Supercritical Unit Using A Composite Weighted Human Learning Network

doi:10.16182/j.issn1004731x.joss.22-0259

Abstract

Abstract:

Intermediate point temperature is an important parameter in ultra supercritical (USC) unit. However, due to strong nonlinearity, it is difficult to determine the form and coefficients of the corresponding model by using traditional methods. In order to get a better control effect, a novel composite weighted human learning optimization network (CWHLON) is proposed to tackle the above-mentioned problems. Though the real-time dynamic linear model, the characteristics of the object are accurately simulated. In the simulation experiment, CWHLON is compared with the traditional recursive least squares and other three meta heuristic methods. The data show that the proposed method improves the model accuracy by 77.93% on average and 78.65% on maximum, effectively improving the identification accuracy.

Key words: intermediate point temperature, strong nonlinearity, modeling, CWHLON, USC

CLC Number:

TP391.9

Chuanliang Cheng, Chen Peng, Deliang Zeng, Tengfei Zhang. Modeling and Simulation of Ultra Supercritical Unit Using A Composite Weighted Human Learning Network[J]. Journal of System Simulation, 2022, 34(7): 1430-1438.

Figures/Tables 10

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Table 1

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Table 3

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算法	区域
算法		1	2	3	4	5	6	7
HLO	平均值	2 115.12	9 796.88	8 626.31	2 844.95	3 372.69	15 180.81	9 456.23
HLO	最优值	2 092.03	9 652.79	8 227.71	2 754.01	3 299.48	14 820.49	8 901.65
GA	平均值	2 183.91	11 151.97	10 507.71	2 952.17	3 507.09	17 059.41	10 104.06
	最优值	2 168.95	10 160.47	9 319.92	2 831.99	3 420.46	15 506.13	9 976.25
	与HLO的对比/%	-3.54	-4.99	-11.71	-2.75	-3.53	-6.66	-10.44
FOA	平均值	2 159.90	10 563.81	10 028.80	3 080.06	3 483.02	16 109.71	9 858.62
	最优值	2 132.61	10 059.84	8 595.51	2 929.17	3 401.07	15 507.88	9 559.49
	与HLO的对比/%	-1.90	-4.04	-4.27	-5.97	-2.98	-6.68	-6.39
PSO	平均值	2 210.42	13 244.72	13 356.23	3 447.11	3 623.23	18 425.82	10 145.52
	最优值	2 159.55	13 169.40	12 265.53	3 438.49	3 612.51	17 318.87	9 995.49
	与HLO的对比/%	-3.12	-26.7	-32.92	-19.90	-8.66	-24.28	-10.63
RLS	平均/最优	2 212.00	13 302.77	17 131.33	3 916.18	4 292.47	20 560.36	10 306.90
RLS	与HLO的对比/%	-5.42	-27.43	-51.97	-29.67	-23.13	-55.79	-13.66

算法	平均值	最优值
CWHLON	28 589.04	28 589.04
HLO	127 332.19	126 686.65
GA	128 905.70	128 187.74
FOA	127 796.35	126 798.73
PSO	129 830.12	127 881.61
RLS	133 919.77	133 919.77

算法	平均值	最优值
CWHLON	7 961.99	7 961.99
HLO	15 482.03	15 360.01
GA	15 666.85	15 391.30
FOA	15 769.38	15 447.75
PSO	16 232.63	15 666.78
RLS	17 975.07	17 975.07

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