Multivariate optimization of Pb(II) removal for clinoptilolite-rich tuffs using genetic programming: A computational approach

Abstract In this study, a genetic programming (GP) model was developed to predict and optimize the Pb(II) removal capacity for natural, sodium, and acid-modified clinoptilolite-rich tuffs. Experimental process evaluated the sorption behavior of lead in aqueous solutions using unmodified and modified natural zeolite considering: the contact time, pH value, lead initial concentration, and sorbent dosage. The GP model was trained and tested with the experimental measurements and subsequently, compared with others multivariate analysis methods using three statistical criteria (coefficient of determination (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE)). The results indicate that GP getting the better performance achieving a fitness of R2 = 98.0%, RMSE = 5.06 × 10−2, and MAPE = 17.58%. Sensitivity analysis (SA) showed that the sorbent dosage was the most influential parameter with a sensitivity index of 0.219, following by the pH (0.059), and contact time (0.031). Based on GP model and SA, a multivariate optimization was conducted to compute the adequate conditions for a required sorption efficiency (98%). Optimize values were obtained at 0.10 g of sorbent mass, pH 5.0, 300.0 mg L−1, and 5.1 min contact time for natural clinoptilolite-rich tuffs; 0.65 g of sorbent mass, pH 5.0, 400.0 mg L−1, and 3.6 min contact time for sodium modified clinoptilolite-rich tuffs; and 0.65 g of sorbent mass, pH 3.0, 400.0 mg L−1, and 71.6 min contact time for acid modified clinoptilolite-rich tuffs. The computational approach presented can perform an assessment with errors less than 6%, indicating that it is a promising tool for the modeling and optimization of the sorption onto zeolite materials minimizing the time and operation cost. The proposed methodology can be used to take appropriate actions in the removing of this toxic heavy metal from the water. Besides, it can be implemented in studies corresponding to other sorption processes or similar.

[1]  M. Ghaedi,et al.  Artificial neural network (ANN) method for modeling of sunset yellow dye adsorption using zinc oxide nanorods loaded on activated carbon: Kinetic and isotherm study. , 2015, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[2]  L. K. Darusman,et al.  CARBON PASTE ELECTRODE HEXADECYLTRIMETHYLAMMONIUM BROMIDE MODIFIED NATURAL ZEOLITE FOR CHROMIUM(VI) DETECTION , 2013 .

[3]  Flavio Cannavó,et al.  Sensitivity analysis for volcanic source modeling quality assessment and model selection , 2012, Comput. Geosci..

[4]  YunaZhao,et al.  Review of the Natural, Modified, and Synthetic Zeolites for Heavy Metals Removal from Wastewater , 2016 .

[5]  S. Rashid,et al.  ADSORPTION OF COPPER FROM AQUEOUS SOLUTION BY ELAIS GUINEENSIS KERNEL ACTIVATED CARBON , 2008 .

[6]  R. A. Conde-Gutiérrez,et al.  Direct neural network modeling for separation of linear and branched paraffins by adsorption process for gasoline octane number improvement , 2014 .

[7]  Francesca Pianosi,et al.  A Matlab toolbox for Global Sensitivity Analysis , 2015, Environ. Model. Softw..

[8]  Alireza Bahadori,et al.  Toward genetic programming (GP) approach for estimation of hydrocarbon/water interfacial tension , 2017 .

[9]  Stefano Tarantola,et al.  A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output , 1999, Technometrics.

[10]  M. Ghaedi,et al.  Application of experimental design and derivative spectrophotometry methods in optimization and analysis of biosorption of binary mixtures of basic dyes from aqueous solutions. , 2017, Ecotoxicology and environmental safety.

[11]  T. Fujii,et al.  Removal of Pb(II) from water using keratin colloidal solution obtained from wool , 2013, Environmental Science and Pollution Research.

[12]  Arash Asfaram,et al.  Application of machine/statistical learning, artificial intelligence and statistical experimental design for the modeling and optimization of methylene blue and Cd(ii) removal from a binary aqueous solution by natural walnut carbon. , 2017, Physical chemistry chemical physics : PCCP.

[13]  Alireza Goudarzi,et al.  Ultrasound-assisted binary adsorption of dyes onto Mn@ CuS/ZnS-NC-AC as a novel adsorbent: Application of chemometrics for optimization and modeling , 2017 .

[14]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[15]  Alain Pétrowski,et al.  Evolutionary Algorithms , 2017 .

[16]  Nirjhar Bar,et al.  Removal of Pb(II) ions from aqueous solution using water hyacinth root by fixed-bed column and ANN modeling. , 2014, Journal of hazardous materials.

[17]  Leonardo Trujillo,et al.  Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming , 2017 .

[18]  M. Ghaedi,et al.  Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon. , 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[19]  Characterization of Raw Zeolite and Surfactant-Modified Zeolite and their use in Removal of Selected Organic Pollutants from Water , 2014 .

[20]  S. Das,et al.  Clarified sludge (basic oxygen furnace sludge)--an adsorbent for removal of Pb(II) from aqueous solutions--kinetics, thermodynamics and desorption studies. , 2009, Journal of hazardous materials.

[21]  Dilek İmren Koç,et al.  A genetic programming-based QSPR model for predicting solubility parameters of polymers , 2015 .

[22]  Tiago A. E. Ferreira,et al.  Time-series forecasting of pollutant concentration levels using particle swarm optimization and artificial neural networks , 2013 .

[23]  Mehrorang Ghaedi,et al.  Back propagation artificial neural network and central composite design modeling of operational parameter impact for sunset yellow and azur (II) adsorption onto MWCNT and MWCNT-Pd-NPs: Isotherm and kinetic study , 2016 .

[24]  M. Afzal,et al.  Effect of pH on the adsorption of Ce3+, Sm3+, Eu3+ and Gd3+ ions on activated charcoal , 1994 .

[25]  Jinhui Li,et al.  A new computational approach for estimation of wilting point for green infrastructure , 2017 .

[26]  Mohammad Hossein Habibi,et al.  Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study , 2014 .

[27]  Akhil Garg,et al.  An evolutionary framework in modelling of multi-output characteristics of the bone drilling process , 2018, Neural Computing and Applications.

[28]  Giovanni Squillero Industrial applications of evolutionary algorithms , 2013, GECCO '13 Companion.

[29]  Inderjeet Tyagi,et al.  Adsorption of Triamterene on multi-walled and single-walled carbon nanotubes: Artificial neural network modeling and genetic algorithm optimization , 2016 .

[30]  Alireza Rostami,et al.  Genetic Programming (GP) Approach for Prediction of Supercritical CO2 Thermal Conductivity , 2017 .

[31]  B. Walczak,et al.  Particle swarm optimization (PSO). A tutorial , 2015 .

[32]  Zhiliang Zhu,et al.  Adsorption characteristics of copper (II) ions from aqueous solution onto humic acid-immobilized surfactant-modified zeolite , 2011 .

[33]  B. Ali,et al.  Comparison of the divalent heavy metals (Pb, Cu and Cd) adsorption behavior by montmorillonite-KSF and their calcium- and sodium-forms , 2017, Superlattices and Microstructures.

[34]  O. May Tzuc,et al.  Artificial neural networks for modeling and optimization of phenol and nitrophenols adsorption onto natural activated carbon , 2017 .

[35]  Mohamed Barakat,et al.  New trends in removing heavy metals from industrial wastewater , 2011 .

[36]  K. Kadirvelu,et al.  Removal of lead(II) by adsorption using treated granular activated carbon: batch and column studies. , 2005, Journal of hazardous materials.

[37]  Leonardo Trujillo,et al.  Automatic modeling of a gas turbine using genetic programming: An experimental study , 2013, Appl. Soft Comput..

[38]  Guohua Chen Electrochemical technologies in wastewater treatment , 2004 .

[39]  Mohammad Hossein Ahmadi Azqhandi,et al.  Simultaneous removal of dyes onto nanowires adsorbent use of ultrasound assisted adsorption to clean waste water: Chemometrics for modeling and optimization, multicomponent adsorption and kinetic study , 2017 .

[40]  L. Kilinc,et al.  Influence of acid and heavy metal cation exchange treatments on methane adsorption properties of mordenite , 2015 .

[41]  Fenglian Fu,et al.  Removal of heavy metal ions from wastewaters: a review. , 2011, Journal of environmental management.

[42]  E. William,et al.  Genetic Programming Lab (GPLab) Tool Set Version 3.0 , 2008, 2008 IEEE Region 5 Conference.

[43]  Alireza Nezamzadeh-Ejhieh,et al.  Comparison of the efficiency of modified clinoptilolite with HDTMA and HDP surfactants for the removal of phosphate in aqueous solutions , 2015 .

[44]  Liang Gao,et al.  An application of evolutionary system identification algorithm in modelling of energy production system , 2018 .