Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine

Xiaoshan Qian; Lisha Xu; Xinmei Yuan; Xiaoshan Qian; Lisha Xu; Xinmei Yuan

doi:10.3934/mbe.2023883

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 11: 19941-19962. doi: 10.3934/mbe.2023883

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Research article

Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine

Xiaoshan Qian ^{1
,
,},
Lisha Xu ^{2
,
,},
Xinmei Yuan ¹

1.
College of Physical Science and Engineering Technology, Yichun University, Yichun 336000, China
2.
College of Information Science and Engineering, Hunan Women's University, Changsha 410004, China

Academic Editor: Yang Kuang

Received: 01 October 2023 Revised: 16 October 2023 Accepted: 16 October 2023 Published: 02 November 2023

The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.
- grey relational analysis (GRA),
- evaporation process,
- least squares support vector machine (LSSVM),
- PS-DE algorithm,
- soft sensor
Citation: Xiaoshan Qian, Lisha Xu, Xinmei Yuan. Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine[J]. Mathematical Biosciences and Engineering, 2023, 20(11): 19941-19962. doi: 10.3934/mbe.2023883

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Abstract

The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.

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