Data-driven soft sensor approach for online quality prediction using state dependent parameter models

Bidar, Bahareh; Sadeghi, Jafar; Shahraki, Farhad; Khalilipour, Mir Mohammad

doi:10.1016/j.chemolab.2017.01.004

Cited by 77 publications

(27 citation statements)

References 64 publications

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“…However, there is another class under the name of hybrid (gray box) which is a combination of model‐driven and data‐based methods. It is noted that the search for the optimal model structure should be approached more systematically, and therefore, the development of identification procedures and data‐based mechanistic (DBM) has received significant attention from different modelers . The statistical methods or soft computing have been applied in different data‐based soft sensors.…”

Section: Introductionmentioning

confidence: 99%

“…It is noted that the search for the optimal model structure should be approached more systematically, and therefore, the development of identification procedures and data-based mechanistic (DBM) has received significant attention from different modelers. [5][6][7][8] The statistical methods or soft computing have been applied in different data-based soft sensors. The most popular of them are multivariate statistical regression techniques include multiple linear regressions (MLRs), 9 partial least squares (PLS), [10][11][12] principal component analysis (PCA) model, [13][14][15] genetic fuzzy model, 16 support vector machine method, 17 artificial neural networks (ANN), 18 a combination with PCA model and ANN, 9,19,20 a PLS-radial basis function neural network-based model, 21 and a combination with linear regressions (LRs) model and ANN.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of time‐variable‐parameter model for data‐based soft sensor of industrial debutanizer

Moghadam

Sadeghi

Shahraki

2019

Optim Control Appl Methods

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Summary The enhancement of modern process control methods has caused the popularity of soft sensors in online quality prediction. It is significant to consider the reduction of model complexity, the performance increment, and decrement of input variables in soft sensor design, simultaneously. The aim of this paper is designing and applying a new data‐based soft sensor with minimum input variables for the enhancement of product quality estimation. Time‐varying‐parameter model by employing the Kalman filter and fixed interval smoothing algorithms has been developed to determine the dynamic transfer function and parameters setting based on time. A novel hybrid method with a dynamic autoregressive exogenous variable model and genetic algorithm has been presented for both state identification and parameter prediction. The combinatorial optimization problem has constructed based on a selection of input variables and an evaluation of Akaike information criterion as a fitness function. An industrial debutanizer column has been used for soft sensor performance validation. The result has indicated that the final soft sensor model in comparison to other presented soft sensing methods for this case has less complexity, fewer input variables, more robust and higher predictive performance. Due to fewer input variables, rapid convergence, and low complexity of this model, it can be efficient in industrial processes control, time‐saving, and improvement of quality prediction.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of time‐variable‐parameter model for data‐based soft sensor of industrial debutanizer

Moghadam

Sadeghi

Shahraki

2019

Optim Control Appl Methods

Self Cite

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“…To meet the demand for petrochemical products with high quality and low price in modern petrochemical enterprises, quality prediction plays a more and more important role in advanced industrial systems by predicting and monitoring key variables for petrochemical processes. [1][2][3][4][5] Melt index (MI) of polypropylene that determines the grade of polymer product is considered as 1 of the most crucial indicators in quality monitoring for industrial propylene polymerization processes. 6,7 To obtain the value of MI, a traditional method is to analyze the fluidity property of polypropylene in the laboratory, which is costly and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Quality prediction via semisupervised Bayesian regression with application to propylene polymerization

Sun

Liu

Zhang

2018

Journal of Chemometrics

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Statistical learning techniques are widely used for quality prediction in polymerization processes during the last decades. However, compared to operation variables, quality variables of polypropylene process are usually difficult to acquire resulting from the absence of measuring units. A semisupervised Bayesian regression method is therefore presented to improve the prediction accuracy by sufficient usage of unlabeled sampling data for melt index prediction in polypropylene processes. The developed model consists of Bayesian inference to predict quality variables and neighborhood kernel density estimation for finding relationships between unlabeled data and labeled samples, which takes advantages of a probability framework with iterative maximum likelihood techniques for parameter estimation and a sparse constraint for avoiding overfitting. The quality prediction regression method is compared with published models by applying to a real dataset of industrial propylene polymerization. The experiment results demonstrate the effectiveness of the proposed semisupervised Bayesian method.

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“…Soft-sensing is to establish a mathematical relation model between easily measured process variables and difficultly measured process variables based on mechanism analysis and sensor data mining [10]. The existing soft-sensing modeling approach can be classified into three types: mechanism modeling, identification modeling, and artificial intelligence-based modeling [11][12][13]. The mechanism modeling approach is to obtain a mathematical expression based on the analysis of the system's internal relations, which adopts the basic physical and chemical laws, such as material, energy, or momentum conservation relation [14].…”

Section: Introductionmentioning

confidence: 99%

A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

Wang

Jia

et al. 2018

Journal of Chemistry

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Water environment protection is of great significance for both economic development and improvement of people's livelihood, where modeling of water environment evolution is indispensable in water quality analysis. However, many water quality indexes related to water quality model cannot be measured online, and some model parameters always vary among different water areas. Thus, this paper proposes a water quality soft-sensing method based on the water quality mechanism model to simulate evolution of water quality indexes online, where unscented Kalman filter is utilized to estimate model parameters. Furthermore, a modified fuzzy comprehensive evaluation method is presented to evaluate the level of water eutrophication condition. Finally, the water quality data collected from Taihu Lake and Beihai Lake are used to validate the effectiveness and generality of the proposed method. The results show that the proposed soft-sensing method is able to describe the variation of related water quality indexes, with better accuracy compared to nonlinear least squares based method and traditional trial-and-error based method. On this basis, the water eutrophication condition can be also accurately evaluated.

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Data-driven soft sensor approach for online quality prediction using state dependent parameter models

Cited by 77 publications

References 64 publications

Optimization of time‐variable‐parameter model for data‐based soft sensor of industrial debutanizer

Optimization of time‐variable‐parameter model for data‐based soft sensor of industrial debutanizer

Quality prediction via semisupervised Bayesian regression with application to propylene polymerization

A Fusion Water Quality Soft-Sensing Method Based on WASP Model and Its Application in Water Eutrophication Evaluation

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