Support Vector Machine based modeling of an industrial natural gas sweetening plant

Abstract In this study Support Vector Machine (SVM) is employed to develop a model to estimate process output variables of an industrial natural gas sweetening plant. The developed model is evaluated by process operating data of Hashemi Nejad natural gas refinery in Khorasan/Iran. A set of 13 input/output plant data each consisting of 145 data has been used to train, optimize, and test the model. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and 10% of available data respectively. Model estimations are compared with those obtained from an ANN based model developed using the same dataset as used for training and test of SVM based model. Test results from the SVM based model showed to be in better agreement with operating plant data compared to artificial neural networks based model. The minimum calculated squared correlation coefficient for estimated process variables is 0.99. Based on the results of this case study SVM proved that it can be a reliable accurate estimation method.

[1]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[2]  Xianfang Wang,et al.  Hybrid modeling of penicillin fermentation process based on least square support vector machine , 2010 .

[3]  Luis Fernando Roca Castillo,et al.  Influence of the plot area in an economical analysis for selecting small scale LNG technologies for remote gas production , 2010 .

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Marti A. Hearst.  Intelligent Connections: Battling with GA-Joe. , 1998 .

[6]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[7]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[8]  Massimiliano Pontil,et al.  Properties of Support Vector Machines , 1998, Neural Computation.

[9]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[10]  The Use of MDEA and Mixtures of Amines for Bulk CO 2 Removal , 2006 .

[11]  Luis Puigjaner,et al.  Performance assessment of a novel fault diagnosis system based on support vector machines , 2009, Comput. Chem. Eng..

[12]  P. Keshavarz,et al.  Development of an artificial neural network model for the prediction of hydrocarbon density at high-pressure, high-temperature conditions , 2013 .

[13]  G. Aroca,et al.  Biofiltration of high concentration of hydrogen sulphide using Thiobacillus thioparus , 2003 .

[14]  Emre Çomak,et al.  A new training method for support vector machines: Clustering k-NN support vector machines , 2008, Expert Syst. Appl..

[15]  Xinjun Peng,et al.  TPMSVM: A novel twin parametric-margin support vector machine for pattern recognition , 2011, Pattern Recognit..

[16]  Jian Ding,et al.  A hybrid support vector machine and fuzzy reasoning based fault diagnosis and rescue system for stable glutamate fermentation , 2012 .

[17]  Der-Chiang Li,et al.  An algorithm to cluster data for efficient classification of support vector machines , 2008, Expert Syst. Appl..

[18]  M. K. Salooki,et al.  Modeling and simulation of condensed sulfur in catalytic beds of CLAUS process: rapid estimation , 2010 .

[19]  Javier Lafuente,et al.  Biological sweetening of energy gases mimics in biotrickling filters. , 2008, Chemosphere.

[20]  M. Saidi,et al.  Modeling and optimization of Fischer–Tropsch synthesis in the presence of Co (III)/Al2O3 catalyst using artificial neural networks and genetic algorithm , 2013 .

[21]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[22]  Qi Li,et al.  Sphere Support Vector Machines for large classification tasks , 2013, Neurocomputing.

[23]  X. Y. Zhang,et al.  Application of support vector machine (SVM) for prediction toxic activity of different data sets. , 2006, Toxicology.

[24]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[25]  Akbar Shahsavand,et al.  Application of artificial neural networks for simulation of experimental CO2 absorption data in a packed column , 2011 .

[26]  C. R. Nagarajah,et al.  Fluid level measurement in dynamic environments using a single ultrasonic sensor and Support Vector Machine (SVM) , 2010 .

[27]  Moses O. Tadé,et al.  Artificial neural network-based prediction of hydrogen content of coal in power station boilers , 2005 .

[28]  Gatze Lettinga,et al.  Biotechnological sulphide removal from effluents. , 1991 .

[29]  Harish Karnick,et al.  Kernel-based online machine learning and support vector reduction , 2008, ESANN.

[30]  Jie Yu,et al.  A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses , 2012, Comput. Chem. Eng..

[31]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[32]  W. Hong,et al.  Solubilities of Carbon Dioxide in Aqueous Mixtures of Diethanolamine and 2-Amino-2-methyl-1-Propanol , 1996 .

[33]  Sakir Tasdemir,et al.  Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque–Hilsch vortex tubes using artificial neural networks , 2008 .

[34]  S. M. Mousavi,et al.  Modeling and simulation of sour gas membrane-absorption system: Influence of operational parameters on species removal , 2009 .

[35]  Jerry A. Bullin,et al.  Using Mixed Amine Solutions for Gas Sweetening , 2006 .

[36]  Abdallah S. Berrouk,et al.  Using mixed tertiary amines for gas sweetening energy requirement reduction , 2013 .

[37]  Roman M. Balabin,et al.  Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? , 2011, Physical chemistry chemical physics : PCCP.

[38]  Ali Eslamimanesh,et al.  Phase Equilibrium Modeling of Structure H Clathrate Hydrates of Methane + Water "Insoluble" Hydrocarbon Promoter Using QSPR Molecular Approach , 2011 .

[39]  Mahdi Koolivand Salooki,et al.  Design of neural network for manipulating gas refinery sweetening regenerator column outputs , 2011 .

[40]  Millaray Curilem,et al.  Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding , 2011 .

[41]  A. Trejo,et al.  Gas solubility of CO2 in aqueous solutions of N-methyldiethanolamine and diethanolamine with 2-amino-2-methyl-1-propanol , 2004 .

[42]  Euntai Kim,et al.  A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function , 2011, Expert Syst. Appl..

[43]  John M. Campbell,et al.  Gas conditioning and processing , 1970 .

[44]  G. Versteeg,et al.  The removal of hydrogen sulfide from gas streams using an aqueous metal sulfate absorbent Part I. The absorption of hydrogen sulfide in metal sulfate solutions , 2005 .

[45]  Ali Eslamimanesh,et al.  Phase equilibrium modeling of clathrate hydrates of methane, carbon dioxide, nitrogen, and hydrogen + water soluble organic promoters using Support Vector Machine algorithm , 2012 .

[46]  Luca Zanni,et al.  A parallel solver for large quadratic programs in training support vector machines , 2003, Parallel Comput..

[47]  Bingtao Zhao,et al.  Modeling pressure drop coefficient for cyclone separators: A support vector machine approach , 2009 .

[48]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[49]  K. Movagharnejad,et al.  A comparative study between LS-SVM method and semi empirical equations for modeling the solubility of different solutes in supercritical carbon dioxide , 2011 .

[50]  Bhaskar D. Kulkarni,et al.  Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process , 2005, Comput. Chem. Eng..

[51]  J. C. Kuo,et al.  Pros and cons of different Nitrogen Removal Unit (NRU) technology , 2012 .