Robust data-driven soft sensor based on iteratively weighted least squares support vector regression optimized by the cuckoo optimization algorithm

Behnasr, Masoud; Jazayeri-Rad, Hooshang

doi:10.1016/j.jngse.2014.11.017

Cited by 22 publications

(6 citation statements)

References 21 publications

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“…Modeling a distillation column is a complex process as it involves various nonlinearities and includes multiple variables with interactions between them [73]. In the Tüpras refinery, there is an abundance of sensors to monitor the entire debutanization processes.…”

Section: Debutanization Processmentioning

confidence: 99%

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Rožanec

Trajkova

et al. 2021

Applied Sciences

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Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.

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Section: Debutanization Processmentioning

confidence: 99%

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Rožanec

Trajkova

et al. 2021

Applied Sciences

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“…The soft-sensing modeling method based on an accurate incremental online v-SVR learning algorithm suggested in [59] for the concentration of biomass during the fermentation process. An important data-driven soft-sensing model based on iteratively weighted LS-SVR by using a cuckoo search (CS) optimization algorithm presented in [60]. Comparisons of different SVM based soft-sensing modeling methods are provided in Table 2.…”

Section: Support Vector Machine-based Soft-sensing Modelsmentioning

confidence: 99%

“…It uses the Lévy flight concept to update the nest position, which follows a random walk that is based on a truncated probability distribution step size [126]. In [60], the authors presented a data-driven soft-sensing model based on iteratively weighted LS-SVR with a CS optimization algorithm.…”

Section: Optimization Techniquesmentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

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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%