scholarly journals Soft sensor development and optimization of the commercial petrochemical plant integrating support vector regression and genetic algorithm

2009 ◽  
Vol 15 (3) ◽  
pp. 175-187 ◽  
Author(s):  
S.K. Lahiri ◽  
Nadeem Khalfe
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Soft Sensor ◽  
Operating Conditions ◽  
Support Vector ◽  
Petrochemical Plant ◽  
Process Data ◽  
Soft Sensing ◽  
Modeling And Optimization ◽  
Sensor Modeling

Soft sensors have been widely used in the industrial process control to improve the quality of the product and assure safety in the production. The core of a soft sensor is to construct a soft sensing model. This paper introduces support vector regression (SVR), a new powerful machine learning method based on a statistical learning theory (SLT) into soft sensor modeling and proposes a new soft sensing modeling method based on SVR. This paper presents an artificial intelligence based hybrid soft sensormodeling and optimization strategies, namely support vector regression - genetic algorithm (SVR-GA) for modeling and optimization of mono ethylene glycol (MEG) quality variable in a commercial glycol plant. In the SVR-GA approach, a support vector regression model is constructed for correlating the process data comprising values of operating and performance variables. Next, model inputs describing the process operating variables are optimized using genetic algorithm with a view to maximize the process performance. The SVR-GA is a new strategy for soft sensor modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics etc.) is not required. Using SVR-GA strategy, a number of sets of optimized operating conditions were found. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the quality.

Soft Sensor Modeling via Support Vector Regression with KICA-Based Feature Extraction

2011 ◽  
Vol 186 ◽  
pp. 560-564
Author(s):  
Yi Liu ◽  
Hong Ying Deng ◽  
Zeng Liang Gao ◽  
Ping Li
Keyword(s):  
Support Vector Regression ◽  
Principal Component ◽  
Component Analysis ◽  
Soft Sensor ◽  
Modeling Method ◽  
Operating Conditions ◽  
Support Vector ◽  
Kernel Independent Component Analysis ◽  
Input Variables ◽  
Sensor Modeling

A novel two-level integrated soft sensor modeling method using kernel independent component analysis (KICA) and support vector regression (SVR) is proposed for chemical processes. In the first level, the KICA approach is adopted to extract information of input variables in the high dimensional feature space. Based on this strategy, the correlation of input variables can be eliminated and thus the complexity is reduced. Then, the model is established using SVR in the second level. The KICA-SVR soft sensor modeling method is applied to estimate product compositions in the Tennessee Eastman process. The obtained results show that it can exhibit better performance, compared to the traditional ICA, principal component analysis (PCA) and kernel PCA based information extraction methods, under different operating conditions.

Process Modeling and Optimization Strategies Integrating Support Vector Regression and Differential Evolution: A Study of Industrial Ethylene Oxide Reactor

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
Sandip Kumar Lahiri ◽  
Nadeem Khalfe
Keyword(s):  
Ethylene Oxide ◽  
Differential Evolution ◽  
Support Vector Regression ◽  
Process Modeling ◽  
Operating Conditions ◽  
Support Vector ◽  
Process Data ◽  
Support Vector Regression Model ◽  
Catalyst Selectivity ◽  
Modeling And Optimization

This paper presents artificial intelligence-based process modeling and optimization strategies, namely, support vector regression – differential evolution (SVR-DE) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR-DE approach, a support vector regression model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Differential Evolution (DE) with a view to maximize the process performance. DE possesses certain unique advantages over the commonly used gradient-based deterministic optimization algorithms. The SVR-DE is a new strategy for chemical process modeling and optimization. The major advantage of the strategy is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR-DE strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions, when verified in an actual plant, resulted in a significant improvement in the EO production rate and catalyst selectivity.

Data-driven soft sensor modeling based on twin support vector regression for cane sugar crystallization

2019 ◽  
Vol 241 ◽  
pp. 159-165 ◽  
Author(s):  
Yanmei Meng ◽  
Qiliang Lan ◽  
Johnny Qin ◽  
Shuangshuang Yu ◽  
Haifeng Pang ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Cane Sugar ◽  
Soft Sensor ◽  
Data Driven ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Sensor Modeling

Hybrid Support Vector Regression and Genetic Algorithm Technique - A Novel Approach in Process Modeling

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Critical Velocity ◽  
Operating Conditions ◽  
Superior Performance ◽  
Support Vector ◽  
Novel Approach ◽  
Wide Range ◽  
Liquid Slurry ◽  
Solid Liquid

This paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.

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