Development of a Soft Sensor for a Batch Distillation Column Using Support Vector Regression Techniques

Jain, Priyanka; Rahman, Imran; Kulkarni, Bhaskar D.

doi:10.1205/cherd05026

Cited by 57 publications

(36 citation statements)

References 13 publications

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“…Linear model includes principle component analysis (PCA) [1][2][3][4][5][6] and partial least square (PLS) [2][3][4][5][6][7][8]. While, non-linear method includes artificial neural network (ANN) [7][8][9][10][11][12][13][14][15], and support vector regression (SVR) [5,[16][17][18]. Whereas, [19] and [20] proposed the first soft sensors for online monitoring in reactive distillation column's (RDC).…”

Section: *Author For Correspondencementioning

confidence: 99%

ANN based soft sensor model for reactive distillation column

Kataria¹,

Singh²,

Dohare³

2016

IJATEE

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Section: *Author For Correspondencementioning

confidence: 99%

ANN based soft sensor model for reactive distillation column

Kataria¹,

Singh²,

Dohare³

2016

IJATEE

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“…An overview of different inferential sensor modelling approaches is given in [7]. The most common techniques used in designing the inferential data-driven sensor are the Artificial Neural Network (ANN) [8], Fuzzy systems including clustering method [9], Partial Least Square (PLS) [10], Neuro-Fuzzy systems (NFS) [11], Principle Component Analysis (PCA) [12] and Support Vector Regression (SVR) [13,14]. Additionally, the data-driven inferential sensor can be designed using hybrid techniques [15].…”

Section: Introductionmentioning

confidence: 99%

Inferential Sensor for Estimation of the Concentration of Benzene in the Distillation Column Using TSK Fuzzy System Based on Modified Clustering Approach

Ghanaati¹

2017

AJCHE

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An inferential sensor is a computer program used for inferring the process variables, which are very hard to measure from the available measurement data. Measurement noises can affect the quality of the data which can be improved by wavelet denoising method. The objective of this paper is to design an inferential sensor for estimation of Benzene concentration in a typical distillation column. Selection of the most relevant input variables for estimation can improve the performance of inferential sensor which is done by Principal Component Analysis (PCA) technique. In this paper an inferential sensor is proposed based on a novel modification of the nearest neighbor distance-based clustering for developing a TakagiSugeno-Kang (TSK) fuzzy model optimized by the Particle Swarm Optimization (PSO) algorithm. The proposed technique was compared against the conventional nearest neighbor distance-based clustering approach optimized by PSO. The simulation results confirm that the designed inferential sensor based on the proposed method is more accurate even for a noisy data set.

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“…The SVM (Vapnik, 1998), suggested by Vapnik, was emerged as a desirable tool for nonlinear modeling because of its advantages over the existing techniques (Jain et al, 2007). This model can work with uncertainties, noisy data, and nonlinear relationships (Khalfe, 2009).…”

Section: Introductionmentioning

confidence: 99%