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

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 pro...

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Published inMathematical biosciences and engineering : MBE Vol. 20; no. 11; pp. 19941 - 19962
Main Authors Qian, Xiaoshan, Xu, Lisha, Yuan, Xinmei
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2023
Subjects
evaporation process
grey relational analysis (gra)
least squares support vector machine (lssvm)
ps-de algorithm
soft sensor
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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.
AbstractList 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.
Author Qian, Xiaoshan
Yuan, Xinmei
Xu, Lisha
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Snippet The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of...
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SubjectTerms evaporation process
grey relational analysis (gra)
least squares support vector machine (lssvm)
ps-de algorithm
soft sensor
Title Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine
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