Multi-model Soft Sensor Modeling under Help-training Strategy

doi:10.16182/j.issn1004731x.joss.22-0976

Abstract

Abstract:

Due to the strong nonlinearity, multi-stage coupling, and the small number of labeled samples in complex industrial processes, it is difficult for traditional global soft sensor models to accurately describe the whole process. Therefore, a multi-model soft sensor modeling method under the help-training strategy is proposed. This method uses a fuzzy C-means (FMC) clustering algorithm to mine similar samples in the sample set and build several sub-models. By introducing the help-training strategy, a collaborative training framework based on main and auxiliary learners is formed, and a confidence evaluation mechanism is designed to eliminate error samples and expand the modeling space of sub-models. Then the fuzzy membership degree is used as the probability distribution function of D-S evidence theory to calculate the weight of the sub-model, and the output of the sub-model is fused to obtain the final model prediction result. Through the modeling and simulation of the actual data of the debutanizer industrial process, the results show that this model has good prediction performance.

Key words: soft sensor modeling, multi-model, help-training, learner, debutanizer

CLC Number:

TP274

He Luosuyang, Xiong Weili. Multi-model Soft Sensor Modeling under Help-training Strategy[J]. Journal of System Simulation, 2024, 36(1): 249-259.

Figures/Tables 12

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模型	RMSE	COR
TSVR	0.467 1	0.797 4
H-TSVR	0.233 5	0.903 1
HFD-TSVR	0.217 8	0.915 6

主要变量	变量名称
x₁	顶层温度
x₂	顶层压力
x₃	回流流量
x₄	流向下个过程的流量
x₅	第六塔板温度
x₆	塔底温度1
x₇	塔底温度2

模型	RMSE	COR
TSVR	0.141 2	0.510 4
S-TSVR	0.137 4	0.589 7
H-TSVR	0.137 1	0.594 4
HF-TSVR	0.128 9	0.701 2
HFD-TSVR	0.112 8	0.720 5

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