Support Vector Regression Prediction and Analysis of the Copper (II) Biosorption Efficiency

Abstract Support vector regression (SVR) has been used for the prediction of biosorption efficiency of Cu(II) using litter of natural trembling poplar (Populus tremula) forest (LNTPF) as a low-cost biosorbent. The proposed SVR model has been compared with the most widely used multiple linear regression (MLR) model based on statistical parameters for the unseen test data set in terms of coefficient of determination (R2), average absolute relative error, root mean square error and standard deviation. The SVR-based model is found to be superior to the MLR model. The effect of various parameters (adsorbent concentration, pH, particle size, initial Cu(II) concentration, agitating speed and temperature) on the Cu(II) biosorption efficiency using the SVR-based model has also been studied. The simulation results of the SVR-based model agree appreciably with the available experimental results. This novel application of SVR can be successfully applied to model other such systems with good accuracy and high generalization ability.

[1]  G. N. Pillai,et al.  Prediction of peak ground acceleration using ϵ-SVR, ν-SVR and Ls-SVR algorithm , 2017 .

[2]  Anantharaman Narayanan,et al.  A Comparative Study of ANN and CFD Modelling for Pressure Drop Prediction in a Fluidized Bed with Internals , 2017 .

[3]  Nhat-Duc Hoang,et al.  Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam , 2016, Int. J. Digit. Earth.

[4]  S. Zaidi.  Novel application of support vector machines to model the two phase boiling heat transfer coefficient in a vertical tube thermosiphon reboiler , 2015 .

[5]  Hao Peng,et al.  Predicting thermal–hydraulic performances in compact heat exchangers by support vector regression , 2015 .

[6]  S. Agarwal,et al.  Bioadsorbents for remediation of heavy metals: Current status and their future prospects , 2015 .

[7]  L. Ray,et al.  Biosorption of Malachite Green from Aqueous Solution Using Resting and Immobilised Biomass of Bacillus cereus M116 (MTCC 5521) , 2015 .

[8]  H. Singh,et al.  Comparison of Hexavalent Chromium Adsorption from Aqueous Solutions by Various Biowastes and Granulated Activated Carbon , 2014 .

[9]  P. Das,et al.  Optimization of Copper Adsorption by Soil of Polluted Wasteland using Response Surface Methodology , 2014 .

[10]  R. Padma Sree,et al.  Artificial Neural Network and Response Surface Methodology Approach for Modeling and Optimization of Chromium (VI) Adsorption from Waste Water using Ragi Husk Powder , 2013 .

[11]  Parag P. Bhagwat,et al.  Hydroclimatic streamflow prediction using Least Square-Support Vector Regression , 2013 .

[12]  N. Subrahmanyam,et al.  Comparing Four Compound-specific Cohesion Factor Relationships for Soave–Redlich–Kwong Equation of State , 2013 .

[13]  Ani Shabri,et al.  Streamflow forecasting using least-squares support vector machines , 2012 .

[14]  Ashok Kumar,et al.  Biosorption of Heavy Metals: A Review , 2012 .

[15]  S. Zaidi.  Development of support vector regression (SVR)-based model for prediction of circulation rate in a vertical tube thermosiphon reboiler , 2012 .

[16]  Okan Ozgonenel,et al.  The use of artificial neural networks (ANN) for modeling of adsorption of Cu(II) from industrial leachate by pumice , 2011 .

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[18]  S. B. Gogoi.  Adsorption of a Lignin-Based Surfactant on Nahorkatiya Porous Media , 2010 .

[19]  Madhu Agarwal,et al.  Prediction of Biodiesel Properties from Fatty Acid Composition using Linear Regression and ANN Techniques , 2010 .

[20]  Ensar Oguz,et al.  Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and Neural Network Modelling , 2010 .

[21]  S. A. Jabasingh,et al.  Optimization and Kinetic Studies of Nickel Treatment in the Electroplating Effluent with Activated Carbon Prepared from Rice Husk , 2010 .

[22]  Jyeshtharaj B. Joshi,et al.  Estimation of heat transfer coefficient in bubble column reactors using support vector regression , 2010 .

[23]  Abdolreza Moghadassi,et al.  Application of Artificial Neural Network for Prediction of Liquid Viscosity , 2010 .

[24]  L. Ray,et al.  Biosorption of Copper Ions by Bacillus cereus M1 16 from Aqueous Solution , 2010 .

[25]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[26]  Yong Pan,et al.  Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine. , 2009, Journal of hazardous materials.

[27]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[28]  V. Sugumaran,et al.  Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine , 2008, Expert Syst. Appl..

[29]  Y. Nuhoğlu,et al.  Biosorption of Cu(II) ions onto the litter of natural trembling poplar forest. , 2008, Journal of hazardous materials.

[30]  Can Chen,et al.  Biosorption of heavy metals by Saccharomyces cerevisiae: a review. , 2006, Biotechnology advances.

[31]  C. Rajagopal,et al.  Removal of heavy metal ions from aqueous solutions using carbon aerogel as an adsorbent. , 2005, Journal of hazardous materials.

[32]  Wenjian Wang,et al.  A heuristic training for support vector regression , 2004, Neurocomputing.

[33]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[34]  Sanjeev S. Tambe,et al.  Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst , 2004 .

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

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

[37]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[38]  N. Zahra.  Studies of Various Adsorbents for the Removal of Copper (II) from Drinking Water: A Review , 2014 .

[39]  Sunday O. Olatunji,et al.  Performance Comparison of SVM and ANN in Predicting Compressive Strength of Concrete , 2014 .

[40]  B. Suganthi,et al.  Biosorption of Heavy Metal Lead from Aqueous Solution by Non-living Biomass of Sargassum myriocystum , 2014 .

[41]  R. Samant,et al.  A study on Comparative Performance of SVM Classifier Models with Kernel Functions in Prediction of Hypertension , 2013 .

[42]  Rajeshri R. Shelke,et al.  Simplified Approach of ANN: Strengths and Weakness , 2012 .

[43]  Azlan Mohd Zain,et al.  Overview of Support Vector Machine in Modeling Machining Performances , 2011 .

[44]  M. Ashraf,et al.  Study of low cost biosorbent for biosorption of heavy metals , 2011 .

[45]  Soushan Wu,et al.  Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets , 2006 .

[46]  Kyung-shik Shin,et al.  An application of support vector machines in bankruptcy prediction model , 2005, Expert Syst. Appl..

[47]  T Viraraghavan,et al.  Fungal biosorption — an alternative treatment option for heavy metal bearing wastewaters: a review , 1995 .

[48]  A. Otero,et al.  Changes in the gross chemical composition of mass cultures of the marine microalga Dunaliella tertiolecta with different aeration rates , 1995 .