Least Square-Support Vector Machine Based Modeling for Electric Arc Furnaces

Least square-support vector machine based modeling for electric arc furnaces

10.32920/ryerson.14664561 ◽

2021 ◽

Author(s):

Leyla Vakilian

Keyword(s):

Support Vector Machine ◽

Electric Arc ◽

Least Square ◽

Data Driven ◽

Electrical Network ◽

Support Vector ◽

Mathematical Methods ◽

Base Function ◽

Electric Arc Furnaces ◽

Arc Furnaces

The process of making steel from the scrap metal by means of electric arc furnaces (EAF) has been used extensively in the industry. Accurate modelling of EAFs is, therefore, desired to assess their operations and their impacts on the electrical network. A number of approaches have already been used to model the v-i behavior of electric arc furnaces including mathematical methods and data-driven models. The objective of this thesis is to investigate the data-driven modelling methodologies, in particular, least square support vector machine (LS-SVM). The results obtained show that the proposed method with radial base function kernel provides the model to predict both arc current and arc voltage of EAFs.

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Least square-support vector machine based modeling for electric arc furnaces

10.32920/ryerson.14664561.v1 ◽

2021 ◽

Author(s):

Leyla Vakilian

Keyword(s):

Support Vector Machine ◽

Electric Arc ◽

Least Square ◽

Data Driven ◽

Electrical Network ◽

Support Vector ◽

Mathematical Methods ◽

Base Function ◽

Electric Arc Furnaces ◽

Arc Furnaces

The process of making steel from the scrap metal by means of electric arc furnaces (EAF) has been used extensively in the industry. Accurate modelling of EAFs is, therefore, desired to assess their operations and their impacts on the electrical network. A number of approaches have already been used to model the v-i behavior of electric arc furnaces including mathematical methods and data-driven models. The objective of this thesis is to investigate the data-driven modelling methodologies, in particular, least square support vector machine (LS-SVM). The results obtained show that the proposed method with radial base function kernel provides the model to predict both arc current and arc voltage of EAFs.

Download Full-text

Least Square-Support Vector Machine Based Modeling for Electric Arc Furnaces

10.32920/ryerson.14647179.v1 ◽

2021 ◽

Author(s):

Leyla Vakilian

Keyword(s):

Support Vector Machine ◽

Electric Arc ◽

Least Square ◽

Data Driven ◽

Electrical Network ◽

Support Vector ◽

Mathematical Methods ◽

Base Function ◽

Electric Arc Furnaces ◽

Arc Furnaces

The process of making steel from the scrap metal by means of electric arc furnaces (EAF) has been used extensively in the industry. Accurate modelling of EAFs is, therefore, desired to assess their operations and their impacts on the electrical network. A number of approaches have already been used to model the v-i behavior of electric arc furnaces including mathematical methods and data-driven models. The objective of this thesis is to investigate the data-driven modelling methodologies, in particular, least square support vector machine (LS-SVM). The results obtained show that the proposed method with radial base function kernel provides the model to predict both arc current and arc voltage of EAFs.

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Modern Soft-Sensing Modeling Methods for Fermentation Processes

Sensors ◽

10.3390/s20061771 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1771 ◽

Cited By ~ 6

Author(s):

Xianglin Zhu ◽

Khalil Ur Rehman ◽

Bo Wang ◽

Muhammad Shahzad

Keyword(s):

Support Vector Machine ◽

Real Time ◽

Fermentation Process ◽

Optimization Techniques ◽

Least Square ◽

Data Driven ◽

Support Vector ◽

Model Parameters ◽

Soft Sensing ◽

Modeling Methods

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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