Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modelling for nickel adsorption onto agro-wastes and commercial activated carbon

Raval, 2016, Adsorptive removal of nickel(II) ions from aqueous environment: A review, J. Environ. Manage., 179, 1, 10.1016/j.jenvman.2016.04.045

Hoseinian, 2017, The nickel ion removal prediction model from aqueous solutions using a hybrid neural genetic algorithm, J. Environ. Manage., 204, 311, 10.1016/j.jenvman.2017.09.011

Ong, 2017, Utilization of groundwater treatment plant (GWTP) sludge for nickel removal from aqueous solutions: Isotherm and kinetic studies, J. Environ. Chem. Eng., 5, 5746, 10.1016/j.jece.2017.10.046

Baseri, 2017, Treatment of nickel ions from contaminated water by magnetite based nanocomposite adsorbents: Effects of thermodynamic and kinetic parameters and modeling with Langmuir and Freundlich isotherms, Process Saf. Environ. Prot., 109, 465, 10.1016/j.psep.2017.04.022

Kavand, 2017, R.Bardestani. Film–Pore–[Concentration–Dependent] Surface Diffusion model for heavy metal ions adsorption: Single and multi–component systems, Process Saf. Environ. Prot., 107, 486, 10.1016/j.psep.2017.03.017

Abd El Salam, 2018, Removal of hazardous cationic organic dyes from water using nickel–based metal–organic frameworks, Inorg. Chim. Acta., 471, 203, 10.1016/j.ica.2017.10.040

Vanderheyden, 2018, Enhanced cesium removal from real matrices by nickel–hexacyanoferrate modified activated carbons, Chemosphere., 202, 569, 10.1016/j.chemosphere.2018.03.096

Jabłońska, 2015, Removing heavy metals from wastewaters with use of shales accompanying the coal beds, J. Environ. Manage., 155, 58, 10.1016/j.jenvman.2015.02.015

Gupta, 2018, Adsorption of Cu(II) by low cost adsorbents and the cost analysis, Environ. Techno. Innov., 10, 91, 10.1016/j.eti.2018.02.003

Hashemian, 2014, Preparation of activated carbon from agricultural wastes (almond shell and orange peel) for adsorption of 2–pic from aqueous solution, J. Ind. Eng. Che., 20, 1892, 10.1016/j.jiec.2013.09.009

Dotto, 2016, Comparison between Brazilian agro–wastes and activated carbon as adsorbents to remove Ni2+ from aqueous solutions, Water Sci. Technol., 73, 2713, 10.2166/wst.2016.095

Mendoza-Castillo, 2018, Insights and pitfalls of artificial neural network modeling of competitive multi–metallic adsorption data, J. Mol. Liq., 251, 15, 10.1016/j.molliq.2017.12.030

Tanzifi, 2018, Adsorption of Amido Black 10B from aqueous solution using polyaniline/SiO2 nanocomposite: Experimental investigation and artificial neural network modeling, J. Colloid. Interf. Sci., 510, 246, 10.1016/j.jcis.2017.09.055

Mendoza–Castillo, 2015, Neural network modeling of heavy metal sorption on lignocellulosic biomasses: effect of metallic ion properties and sorbent characteristics, Ind. Eng. Chem. Res., 54, 443, 10.1021/ie503619j

Ghaedi, 2017, Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review, Adv. Colloid. Interfac., 245, 20, 10.1016/j.cis.2017.04.015

Ghaedi, 2013, Principal component analysis– adaptive neuro–fuzzy inference system modeling and genetic algorithm optimization of adsorption of methylene blue by activated carbon derived from Pistacia khinjuk, Ecotoxicol. Environ. Saf., 96, 110, 10.1016/j.ecoenv.2013.05.015

Aghajani, 2017, Adaptive Neuro–Fuzzy Inference system analysis on adsorption studies of Reactive Red 198 from aqueous solution by SBA–15/CTAB composite, Spectrochim, Acta A., 171, 439, 10.1016/j.saa.2016.08.025

Dolatabadi, 2018, Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS, Chemom. Intell. Lab. Syst., 181, 72, 10.1016/j.chemolab.2018.07.012

Karri, 2018, Process optimization and adsorption modeling using activated carbon derived from palm oil kernel shell for Zn (II) disposal from the aqueous environment using differential evolution embedded neural network, J. Mol. Liq., 265, 592, 10.1016/j.molliq.2018.06.040

Moreno–Pérez, 2018, Artificial neural network-based surrogate modeling of multi-component dynamic adsorption of heavy metals with a biochar, J. Environ. Chem. Eng., 6, 5389, 10.1016/j.jece.2018.08.038

Khandanlou, 2016, Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN), Ecol. Eng., 91, 249, 10.1016/j.ecoleng.2016.03.012

Singha, 2015, The use of artificial neural network (ANN) for modeling of Pb(II) adsorption in batch process, J. Mol. Liq., 211, 228, 10.1016/j.molliq.2015.07.002

Iplikci, 2010, Support vector machines based neuro–fuzzy control of nonlinear systems, Neurocomputing, 73, 2097, 10.1016/j.neucom.2010.02.008

Karri, 2017, Optimal isotherm parameters for phenol adsorption from aqueous solutions onto coconut shell based activated carbon: Error analysis of linear and non-linear methods, J. Taiwan. Inst. Chem. Eng., 80, 472, 10.1016/j.jtice.2017.08.004

Goldstein, 2003

Silverstein, 2005

Thommes, 2015, Performance of an Activated Carbon–Ammonia Adsorption Refrigeration System, Pure Appl, Chem., 87, 1051

Lima, 2017, Application of ultrasound modified corn straw as adsorbent for malachite green removal from synthetic and real effluents, Environ. Sci. Pollut. Res., 24, 21484, 10.1007/s11356-017-9802-y

Mahmoodi, 2018, Mesoporous activated carbons of low-cost agricultural bio-wastes with high adsorption capacity: Preparation and artificial neural network modeling of dye removal from single and multicomponent (binary and ternary) systems, J. Mol. Liq., 269, 217, 10.1016/j.molliq.2018.07.108

Chakraborty, 2013, Artificial neural network (ANN) modeling of dynamic adsorption of crystal violet from aqueous solution using citric–acid–modified rice (Oryza sativa) straw as adsorbent, Clean. Technol. Envir., 15, 255, 10.1007/s10098-012-0503-4

Franco, 2018, Adaptive neuro–fuzzy interference system (ANIFS) and artificial neuronal network (ANN) applied for indium (III) adsorption on carbonaceous materials, Submitted to Chemical Engineering Communications

Karri, 2018, Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network, J. Eviron. Manage., 223, 517

Ahmad, 2015, Chemically oxidized pineapple fruit peel for the biosorption of heavy metals from aqueous solutions, Desalin. Water Treat., 57, 6432, 10.1080/19443994.2015.1005150

Norouzi, 2018, Preparation, characterization and Cr (VI) adsorption evaluation of NaOH–activated carbon produced from Date Press Cake; an agro–industrial waste, Bioresour. Technol., 258, 48, 10.1016/j.biortech.2018.02.106

Esquerdo, 2015, Kinetics and mass transfer aspects about the adsorption of tartrazine by a porous chitosan sponge, React. Kinet. Mech. Cat., 116, 10.1007/s11144-015-0893-5

Zanchetta, 2018, Temperature dependent cellulase adsorption on lignin from sugarcane bagasse, Bioresour. Technol., 252, 143, 10.1016/j.biortech.2017.12.061

Feng, 2010, Enhanced Cu(II) adsorption by orange peel modified with sodium hydroxide, Trans. Nonferrous Met. Soc. China, 20, 146, 10.1016/S1003-6326(10)60030-1

Whang, 2016, Sorption behavior of Cr(VI) on pineapple–peel–derived biochar and the influence of coexisting pyrene, Int. Biodeterior. Biodegradation., 111, 78, 10.1016/j.ibiod.2016.04.029

Chao, 2014, Biosorption of heavy metals on Citrus maxima peel, passion fruit shell, and sugarcane bagasse in a fixed–bed column, J. Ind. Eng. Chem., 20, 3408, 10.1016/j.jiec.2013.12.027

Hao, 2004, The control of platinum impregnation by PZC alteration of oxides and carbon, J. Mol. Catal. A: Chem., 219, 97, 10.1016/j.molcata.2004.04.026

Ghosal, 2018, Adsorptive removal of arsenic by novel iron/olivine composite: Insights into preparation and adsorption process by response surface methodology and artificial neural network, J. Environ. Manage., 209, 176, 10.1016/j.jenvman.2017.12.040

Madan, 2016, Modeling the adsorption of benzeneacetic acid on CaO2 nanoparticles using artificial neural network, Resour. Tech., 2, S53

Yıldız, 2015, Prediction of gas storage capacities in metal organic frameworks using artificial neural network, Micropor. Mesopor. Mat., 208, 50, 10.1016/j.micromeso.2015.01.037

Mondal, 2017, Optimizing ranitidine hydrochloride uptake of Parthenium hysterophorus derived N–biochar through response surface methodology and artificial neural network, Process Saf. Environ. Prot., 107, 388, 10.1016/j.psep.2017.03.011

Ghaedi, 2014, Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticales-activated carbon, Spectrochim. Acta A, 131, 606, 10.1016/j.saa.2014.03.055

Baghban, 2017, Prediction of CO2 loading capacities of aqueous solutions of absorbents using different computational schemes, Int. J. Greenh. Gas. Con., 57, 143, 10.1016/j.ijggc.2016.12.010

Rego, 2018, Optimization of sugarcane bagasse pretreatment using alkaline hydrogen peroxide through ANN and ANFIS modelling, Bioresour. Technol., 267, 634, 10.1016/j.biortech.2018.07.087