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Neural network models for simulating adsorptive eviction of metal contaminants from effluent streams using natural materials (NMs)

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Abstract

With the rise in environmental-conscious research, natural materials (NMs) have drawn attention as eco-sustainable solution for removing hazardous pollutants via adsorption. Although adsorption processes are renowned for their simple implementation, the mechanisms involved in the adsorption of toxins can be complex due to the number of variables involved and their nonlinear interaction. Literature unveils numerous modelling procedures to optimize process variables for the successful metal ions adsorption; however, artificial neural networks’ (ANN) algorithmic approach has accelerated the adsorption propensity of adsorbents for metals ions in water. This review evaluates the ANN approaches (i.e., feedforward neural networks (FFNNs) and neural networks coupled with global optimizers) to simulate the adsorption of different metal ions ranging from heavy metals to highly toxic contaminants (e.g., Ur, Th, As, Cd, Cr, Co, etc.) on NMs. Further, the relative influence of process parameters (such as contact time, pH, initial metal concentration, and dose of NMs) on adsorption has also been outlined. An outlook for future development in the field is provided.

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Data availability

All data generated or analysed during this study are included in this published article (and its supplementary information files).

Abbreviations

AI:

Artificial intelligence

A.D:

Adsorbent dose

ANN:

Artificial neural networks

ANFIS:

Adaptive neuro-fuzzy inference system

ANN-GA:

Genetic algorithm hybridized neural network

ANN-COA:

Cuckoo-optimized hybridized neural network

ANN-SA:

Simulated annealing hybridized neural network

ANN-GWOA:

Wolf-optimized hybridized neural network

ARPE:

Average relative percentage error

BR:

Bayesian regularization

BD:

Bed depth

CT:

Contact time

Ce:

Equilibrium concentration

FR:

Flow rate

FFNN:

Feed forward neural network

GD:

Gradient descent

GA:

Genetic algorithm

LM:

Levenberg–Marquardt

IC:

Initial metal ion concentration

MAPE:

Mean absolute percentage error

MAE:

Mean average error

MSE:

Mean square error

MRE:

Mean relative error

MNLR:

Nonlinear multiple linear regression

NLR:

Nonlinear regression

PS:

Particle size

R2 :

Coefficient of determination

R:

Correlation coefficient

Rprop:

Resilient backpropagation

RMSE:

Root-mean-square error

RSM:

Response surface methodology

RSE%:

Residual error

SSE:

Sum of square error

SDR:

Standard deviation ratio

SD:

Standard deviation

SCG:

Scaled conjugate gradient

SA:

Simplex algorithm

T:

Temperature

References

  1. Vareda JP, Valente AJ, Durães L (2019) Assessment of heavy metal pollution from anthropogenic activities and remediation strategies: a review. J Environ Manag 246:101–118

    Google Scholar 

  2. Kumar V, Parihar RD, Sharma A, Bakshi P, Sidhu GPS, Bali AS, Karaouzas I, Bhardwaj R, Thukral AK, Gyasi-Agyei Y (2019) Global evaluation of heavy metal content in surface water bodies: a meta-analysis using heavy metal pollution indices and multivariate statistical analyses. Chemosphere 236:124364

    Google Scholar 

  3. Oehmen A, Viegas R, Velizarov S, Reis MA, Crespo JG (2006) Removal of heavy metals from drinking water supplies through the ion exchange membrane bioreactor. Desalination 199:405–407

    Google Scholar 

  4. Chen Q, Yao Y, Li X, Lu J, Zhou J, Huang Z (2018) Comparison of heavy metal removals from aqueous solutions by chemical precipitation and characteristics of precipitates. J Water Process Eng 26:289–300

    Google Scholar 

  5. Edzwald JK (1993) Coagulation in drinking water treatment: particles, organics and coagulants. Water Sci Technol 27:21–35

    Google Scholar 

  6. Mallevialle J, Odendaal PE, Wiesner MR (1996) Water treatment membrane processes, American Water Works Association.

  7. Lakherwal D (2014) Adsorption of heavy metals: a review. Int J Environ Res Dev 4:41–48

    Google Scholar 

  8. Shen C, Zhao Y, Li W, Yang Y, Liu R, Morgen D (2019) Global profile of heavy metals and semimetals adsorption using drinking water treatment residual. Chem Eng J 372:1019–1027

    Google Scholar 

  9. Febrianto J, Kosasih AN, Sunarso J, Ju Y-H, Indraswati N, Ismadji S (2009) Equilibrium and kinetic studies in adsorption of heavy metals using biosorbent: a summary of recent studies. J Hazard Mater 162:616–645

    Google Scholar 

  10. Rastogi S, Kandasubramanian B (2020) Progressive trends in heavy metal ions and dyes adsorption using silk fibroin composites. Environ Sci Pollut Res 27:210–237

    Google Scholar 

  11. Kaplan DL (1998) Introduction to biopolymers from renewable resources. In: Biopolymers from renewable resources, Springer. pp. 1–29

  12. Rajeswari A, Christy EJS, Pius A (2021) Biopolymer blends and composites: processing technologies and their properties for industrial applications. In: Biopolymers Their Industrial Applications, Elsevier. pp. 105–147

  13. Nighojkar A, Sangal VK, Dixit F, Kandasubramanian B (2022) Sustainable conversion of saturated adsorbents (SAs) from wastewater into value-added products: future prospects and challenges with toxic per- and poly-fluoroalkyl substances (PFAS). Environ Sci Pollut Res 29:78207–78227. https://doi.org/10.1007/s11356-022-23166-7

    Article  Google Scholar 

  14. Ali AE, Chowdhury ZZ, Rafique RF, Ikram R, Faisal ANM, Shibly S, Barua A, Wahab YA, Jan BM (2022) Science and technology roadmap for adsorption of metallic contaminants from aqueous effluents using biopolymers and its’ derivatives. In: Advanced industrial wastewater treatment and reclamation of water, Springer. pp. 165–196

  15. Xiang Z, Tang N, Jin X, Gao W (2021) Fabrications and applications of hemicellulose-based bio-adsorbents. Carbohydr Polym 278:118945

    Google Scholar 

  16. Zinge C, Kandasubramanian B (2020) Nanocellulose based biodegradable polymers. Eur Polym J 133:109758

    Google Scholar 

  17. Nighojkara AK, Agrawalb AK, Singhc B, Guptaa S, Satankara RK, Oommena JM, Davea L, Sharifd M, Soboyejoe ABO, Plappallya A (2019) Establishing correlations among pore structure, surface roughness, compressive strength, and fracture toughness of ceramic water filters local to Rajasthan, India. Water Treat 157:332–341

    Google Scholar 

  18. Nighojkar AK, Vijay A, Kumavat A, Gupta S, Satankar RK, Plappally A (2019) Use of marble and iron waste additives for enhancing arsenic and E. coli contaminant removal capacity and strength of porous clay ceramic materials for point of use drinking water treatment. Desalination Water Treat 157:290–302

    Google Scholar 

  19. Ali N, Khan A, Nawaz S, Bilal M, Malik S, Badshah S, Iqbal HM (2020) Characterization and deployment of surface-engineered chitosan-triethylenetetramine nanocomposite hybrid nano-adsorbent for divalent cations decontamination. Int J Biol Macromol 152:663–671

    Google Scholar 

  20. Gore PM, Naebe M, Wang X, Kandasubramanian B (2019) Progress in silk materials for integrated water treatments: fabrication, modification and applications. Chem Eng J 374:437–470

    Google Scholar 

  21. Gore PM, Khurana L, Dixit R, Balasubramanian K (2017) Keratin-Nylon 6 engineered microbeads for adsorption of Th (IV) ions from liquid effluents. J Environ Chem Eng 5:5655–5667

    Google Scholar 

  22. Franco DSP, Duarte FA, Salau NPG, Dotto GL (2020) Analysis of indium (III) adsorption from leachates of LCD screens using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANIFS). J Hazard Mater 384:121137. https://doi.org/10.1016/j.jhazmat.2019.121137

    Article  Google Scholar 

  23. Gomez-Gonzalez R, Cerino-Córdova FJ, Garcia-León AM, Soto-Regalado E, Davila-Guzman NE, Salazar-Rabago JJ (2016) Lead biosorption onto coffee grounds: comparative analysis of several optimization techniques using equilibrium adsorption models and ANN. J Taiwan Inst Chem Eng 68:201–210. https://doi.org/10.1016/j.jtice.2016.08.038

    Article  Google Scholar 

  24. Souza PR, Dotto GL, Salau NPG (2018) Artificial neural network (ANN) and adaptive neuro-fuzzy interference system (ANFIS) modelling for nickel adsorption onto agro-wastes and commercial activated carbon. J Environ Chem Eng 6:7152–7160

    Google Scholar 

  25. Pauletto PS, Dotto GL, Salau NPG (2020) Optimal artificial neural network design for simultaneous modeling of multicomponent adsorption. J Mol Liq 320:114418. https://doi.org/10.1016/j.molliq.2020.114418

    Article  Google Scholar 

  26. Nighojkar A, Zimmermann K, Ateia M, Barbeau B, Mohseni M, Krishnamurthy S, Dixit F, Kandasubramanian B (2022) Application of neural network in metal adsorption using biomaterials (BMs): a review. Environ Sci Adv 2:11–38

    Google Scholar 

  27. Braspenning PJ, Thuijsman F, Weijters AJMM (1995) Artificial neural networks: an introduction to ANN theory and practice, Springer Science & Business Media

  28. Goh AT (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9:143–151

    Google Scholar 

  29. Van Der Smagt PP (1994) Minimisation methods for training feedforward neural networks. Neural Netw 7:1–11

    Google Scholar 

  30. Kardam A, Raj KR, Arora JK, Srivastava S (2012) Artificial neural network modeling for biosorption of Pb (II) ions on nanocellulose fibers. Bionanoscience 2:153–160

    Google Scholar 

  31. Singh K, Arora JK, Sinha TJM, Srivastava S (2014) Functionalization of nanocrystalline cellulose for decontamination of Cr (III) and Cr (VI) from aqueous system: computational modeling approach. Clean Technol. Environ Policy 16:1179–1191

    Google Scholar 

  32. Sen S, Nandi S, Dutta S (2018) Application of RSM and ANN for optimization and modeling of biosorption of chromium (VI) using cyanobacterial biomass. Appl Water Sci 8:1–12

    Google Scholar 

  33. Esmaeili A, Beni AA (2015) Novel membrane reactor design for heavy-metal removal by alginate nanoparticles. J Ind Eng Chem 26:122–128

    Google Scholar 

  34. Tomczak E (2011) Application of ANN and EA for description of metal ions sorption on chitosan foamed structure—equilibrium and dynamics of packed column. Comput Chem Eng 35:226–235

    Google Scholar 

  35. Kowsari MR, Sepehrian H, Mahani M, Fasihi J (2016) Cobalt (II) adsorption from aqueous solution using alginate-SBA-15 nanocomposite: kinetic, isotherm, thermodynamic studies and neural network modeling. Mater Focus 5:91–99

    Google Scholar 

  36. Reynel-Avila HE, Bonilla-Petriciolet A, de la Rosa G (2015) Analysis and modeling of multicomponent sorption of heavy metals on chicken feathers using Taguchi’s experimental designs and artificial neural networks. Desalination Water Treat 55:1885–1899

    Google Scholar 

  37. Dil EA, Ghaedi M, Ghezelbash GR, Asfaram A, Ghaedi AM, Mehrabi F (2016) Modeling and optimization of Hg 2+ ion biosorption by live yeast Yarrowia lipolytica 70562 from aqueous solutions under artificial neural network-genetic algorithm and response surface methodology: kinetic and equilibrium study. RSC Adv 6:54149–54161

    Google Scholar 

  38. Heshmati H, Torab-Mostaedi M, Ghanadzadeh Gilani H, Heydari A (2015) Kinetic, isotherm, and thermodynamic investigations of uranium (VI) adsorption on synthesized ion-exchange chelating resin and prediction with an artificial neural network. Desalination Water Treat 55:1076–1087

    Google Scholar 

  39. Singha B, Bar N, Das SK (2014) The use of artificial neural networks (ANN) for modeling of adsorption of Cr (VI) ions. Desalination Water Treat 52:415–425

    Google Scholar 

  40. Nag S, Mondal A, Bar N, Das SK (2017) Biosorption of chromium (VI) from aqueous solutions and ANN modelling. Environ Sci Pollut Res 24:18817–18835

    Google Scholar 

  41. Banerjee M, Bar N, Basu RK, Das SK (2017) Comparative study of adsorptive removal of Cr (VI) ion from aqueous solution in fixed bed column by peanut shell and almond shell using empirical models and ANN. Environ Sci Pollut Res 24:10604–10620

    Google Scholar 

  42. Beigzadeh R, Rastegar SO (2020) Assessment of Cr (VI) biosorption from aqueous solution by artificial intelligence. Chem Methodol 4:181–190

    Google Scholar 

  43. Singh V, Singh J, Mishra V (2021) Development of a cost-effective, recyclable and viable metal ion doped adsorbent for simultaneous adsorption and reduction of toxic Cr (VI) ions. J Environ Chem Eng 9:105124

    Google Scholar 

  44. Yetilmezsoy K, Demirel S (2008) Artificial neural network (ANN) approach for modeling of Pb (II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. J Hazard Mater 153:1288–1300

    Google Scholar 

  45. Singha B, Bar N, Das SK (2015) The use of artificial neural network (ANN) for modeling of Pb (II) adsorption in batch process. J Mol Liq 211:228–232

    Google Scholar 

  46. Khandanlou R, Masoumi HRF, Ahmad MB, Shameli K, Basri M, Kalantari K (2016) Enhancement of heavy metals sorption via nanocomposites of rice straw and Fe3O4 nanoparticles using artificial neural network (ANN). Ecol Eng 91:249–256

    Google Scholar 

  47. Ashrafi M, Borzuie H, Bagherian G, Chamjangali MA, Nikoofard H (2020) Artificial neural network and multiple linear regression for modeling sorption of Pb2+ ions from aqueous solutions onto modified walnut shell. Sep Sci Technol 55:222–233

    Google Scholar 

  48. Narayana PL, Maurya AK, Wang X-S, Harsha MR, Srikanth O, Alnuaim AA, Hatamleh WA, Hatamleh AA, Cho KK, Reddy UM (2021) Artificial neural networks modeling for lead removal from aqueous solutions using iron oxide nanocomposites from bio-waste mass. Environ Res 199:111370

    Google Scholar 

  49. Giri AK, Patel RK, Mahapatra SS (2011) Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass. Chem Eng J 178:15–25

    Google Scholar 

  50. Raj KR, Kardam A, Arora JK, Srivastava S (2013) An application of ANN modeling on the biosorption of arsenic. Waste Biomass Valorization 4:401–407

    Google Scholar 

  51. Podder MS, Majumder CB (2016) The use of artificial neural network for modelling of phycoremediation of toxic elements As (III) and As (V) from wastewater using Botryococcus braunii. Spectrochim Acta A Mol Biomol Spectrosc 155:130–145

    Google Scholar 

  52. Altowayti WAH, Algaifi HA, Bakar SA, Shahir S (2019) The adsorptive removal of As (III) using biomass of arsenic resistant Bacillus thuringiensis strain WS3: characteristics and modelling studies. Ecotoxicol Environ Saf 172:176–185

    Google Scholar 

  53. Varshney S, Jain P, Arora JK, Srivastava S (2016) Process development for the removal of toxic metals by functionalized wood pulp: kinetic, thermodynamic, and computational modeling approach. Clean Technol. Environ Policy 18:2613–2623

    Google Scholar 

  54. Kiran RS, Madhu GM, Satyanarayana SV, Kalpana P, Rangaiah GS (2017) Applications of box-behnken experimental design coupled with artificial neural networks for biosorption of low concentrations of cadmium using Spirulina (Arthrospira) spp. Resour-Effic Technol 3:113–123

    Google Scholar 

  55. Fawzy M, Nasr M, Nagy H, Helmi S (2018) Artificial intelligence and regression analysis for Cd (II) ion biosorption from aqueous solution by Gossypium barbadense waste. Environ Sci Pollut Res 25:5875–5888

    Google Scholar 

  56. Takdastan A, Samarbaf S, Tahmasebi Y, Alavi N, Babaei AA (2019) Alkali modified oak waste residues as a cost-effective adsorbent for enhanced removal of cadmium from water: isotherm, kinetic, thermodynamic and artificial neural network modeling. J Ind Eng Chem 78:352–363

    Google Scholar 

  57. Prakash N, Manikandan SA, Govindarajan L, Vijayagopal V (2008) Prediction of biosorption efficiency for the removal of copper (II) using artificial neural networks. J Hazard Mater 152:1268–1275

    Google Scholar 

  58. Kiew PL, Ang CK, Tan KW, Yap SX (2016) Chicken eggshell as biosorbent: artificial intelligence as promising approach in optimizing study. In: MATEC Web Conference, EDP Sciences. p. 01007

  59. Fawzy M, Nasr M, Adel S, Helmi S (2018) Regression model, artificial neural network, and cost estimation for biosorption of Ni (II)-ions from aqueous solutions by Potamogeton pectinatus. Int J Phytoremediation 20:321–329

    Google Scholar 

  60. Esfandian H, Parvini M, Khoshandam B, Samadi-Maybodi A (2016) Artificial neural network (ANN) technique for modeling the mercury adsorption from aqueous solution using Sargassum Bevanom algae, Desalination. Water Treat 57:17206–17219

    Google Scholar 

  61. Chiter L (2006) DIRECT algorithm: a new definition of potentially optimal hyperrectangles. Appl Math Comput 179:742–749

    MathSciNet  MATH  Google Scholar 

  62. Oguz E, Ersoy M (2010) Removal of Cu2+ from aqueous solution by adsorption in a fixed bed column and neural network modelling. Chem Eng J 164:56–62

    Google Scholar 

  63. Oguz E, Ersoy M (2014) Biosorption of cobalt (II) with sunflower biomass from aqueous solutions in a fixed bed column and neural networks modelling. Ecotoxicol Environ Saf 99:54–60

    Google Scholar 

  64. Ahmad MF, Haydar S, Bhatti AA, Bari AJ (2014) Application of artificial neural network for the prediction of biosorption capacity of immobilized Bacillus subtilis for the removal of cadmium ions from aqueous solution. Biochem Eng J 84:83–90

    Google Scholar 

  65. Rahimpour F, Shojaeimehr T, Sadeghi M (2017) Biosorption of Pb (II) using Gundelia tournefortii: kinetics, equilibrium, and thermodynamics. Sep Sci Technol 52:596–607

    Google Scholar 

  66. Yurtsever U, Yurtsever M, Şengil İA, Kıratlı Yılmazçoban N (2015) Fast artificial neural network (FANN) modeling of Cd (II) ions removal by valonia resin. Desalination Water Treat 56:83–96

    Google Scholar 

  67. Yildiz S (2017) Artificial neural network (ANN) approach for modeling Zn (II) adsorption in batch process. Korean J Chem Eng 34:2423–2434

    Google Scholar 

  68. Fagundes-Klen MR, Ferri P, Martins TD, Tavares CRG, Silva EA (2007) Equilibrium study of the binary mixture of cadmium–zinc ions biosorption by the Sargassum filipendula species using adsorption isotherms models and neural network. Biochem Eng J 34:136–146

    Google Scholar 

  69. Mendoza-Castillo DI, Villalobos-Ortega N, Bonilla-Petriciolet A, Tapia-Picazo JC (2015) Neural network modeling of heavy metal sorption on lignocellulosic biomasses: effect of metallic ion properties and sorbent characteristics. Ind Eng Chem Res 54:443–453

    Google Scholar 

  70. Kalavathy H, Regupathi I, Pillai MG, Miranda LR (2009) Modelling, analysis and optimization of adsorption parameters for H3PO4 activated rubber wood sawdust using response surface methodology (RSM). Colloids Surf B Biointerfaces 70:35–45

    Google Scholar 

  71. Amiri MJ, Abedi-Koupai J, Eslamian S, Mousavi SF, Arshadi M (2013) Modelling Pb (II) adsorption based on synthetic and industrial wastewaters by ostrich bone char using artificial neural network and multivariate non-linear regression. Int J Hydrol Sci Technol 3:221–240

    Google Scholar 

  72. Oladipo AA, Gazi M (2015) Nickel removal from aqueous solutions by alginate-based composite beads: central composite design and artificial neural network modeling. J Water Process Eng 8:e81–e91

    Google Scholar 

  73. Allahkarami E, Igder A, Fazlavi A, Rezai B (2017) Prediction of Co (II) and Ni (II) ions removal from wastewater using artificial neural network and multiple regression models. Physicochem Probl Miner Process 53:1105–1118

    Google Scholar 

  74. Turan NG, Mesci B, Ozgonenel O (2011) Artificial neural network (ANN) approach for modeling Zn (II) adsorption from leachate using a new biosorbent. Chem Eng J 173:98–105

    Google Scholar 

  75. Ranjan D, Mishra D, Hasan SH (2011) Bioadsorption of arsenic: an artificial neural networks and response surface methodological approach. Ind Eng Chem Res 50:9852–9863

    Google Scholar 

  76. Bingöl D, Hercan M, Elevli S, Kılıç E (2012) Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. Bioresour Technol 112:111–115

    Google Scholar 

  77. Podstawczyk D, Witek-Krowiak A, Dawiec A, Bhatnagar A (2015) Biosorption of copper (II) ions by flax meal: empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation. Ecol Eng 83:364–379

    Google Scholar 

  78. Shandi SG, Ardejani FD, Sharifi F (2019) Assessment of Cu (II) removal from an aqueous solution by raw Gundelia tournefortii as a new low-cost biosorbent: experiments and modelling. Chin J Chem Eng 27:1945–1955

    Google Scholar 

  79. Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178:389–397

    Google Scholar 

  80. Abraham AK, Krzyzanski W, Mager DE (2007) Partial derivative—based sensitivity analysis of models describing target-mediated drug disposition. AAPS J 9:E181–E189

    Google Scholar 

  81. Gevrey M, Dimopoulos I, Lek S (2006) Two-way interaction of input variables in the sensitivity analysis of neural network models. Ecol Model 195:43–50

    Google Scholar 

  82. Srivastava PK, Gupta M, Singh U, Prasad R, Pandey PC, Raghubanshi AS, Petropoulos GP (2021) Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data. Environ Dev Sustain 23:5504–5519

    Google Scholar 

  83. Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264

    Google Scholar 

  84. Bottou L (1991) Stochastic gradient learning in neural networks. Proc Neuro-Nımes 91:12

    Google Scholar 

  85. Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Google Scholar 

  86. Sadeghizadeh A, Ebrahimi F, Heydari M, Tahmasebikohyani M, Ebrahimi F, Sadeghizadeh A (2019) Adsorptive removal of Pb (II) by means of hydroxyapatite/chitosan nanocomposite hybrid nanoadsorbent: ANFIS modeling and experimental study. J Environ Manage 232:342–353

    Google Scholar 

  87. Ronda A, Martín-Lara MA, Almendros AI, Pérez A, Blázquez G (2015) Comparison of two models for the biosorption of Pb (II) using untreated and chemically treated olive stone: experimental design methodology and adaptive neural fuzzy inference system (ANFIS). J Taiwan Inst Chem Eng 54:45–56

    Google Scholar 

  88. Bingöl D, Inal M, Çetintaş S (2013) Evaluation of copper biosorption onto date palm (Phoenix dactylifera L.) seeds with MLR and ANFIS models. Ind Eng Chem Res 52:4429–4435

    Google Scholar 

  89. Rebouh S, Bouhedda M, Hanini S (2016) Neuro-fuzzy modeling of Cu (II) and Cr (VI) adsorption from aqueous solution by wheat straw, desalination. Water Treat 57:6515–6530

    Google Scholar 

  90. Jafari SA, Cheraghi S (2014) Mercury removal from aqueous solution by dried biomass of indigenous vibrio parahaemolyticus PG02: kinetic, equilibrium, and thermodynamic studies. Int Biodeterior Biodegrad 92:12–19

    Google Scholar 

  91. Fawzy M, Nasr M, Adel S, Nagy H, Helmi S (2016) Environmental approach and artificial intelligence for Ni (II) and Cd (II) biosorption from aqueous solution using typha domingensis biomass. Ecol Eng 95:743–752

    Google Scholar 

  92. Nasr M, Mahmoud AED, Fawzy M, Radwan A (2017) Artificial intelligence modeling of cadmium (II) biosorption using rice straw. Appl Water Sci 7:823–831

    Google Scholar 

  93. Fawzy M, Nasr M, Abdel-Gaber A, Fadly S (2016) Biosorption of Cr (VI) from aqueous solution using agricultural wastes, with artificial intelligence approach. Sep Sci Technol 51:416–426

    Google Scholar 

  94. Mirjalili S (2019) Genetic algorithm. In: Evolutionary algorithms and neural networks, Springer. pp. 43–55

  95. Sutherland C, Marcano A, Chittoo B (2018) Artificial neural network-genetic algorithm prediction of heavy metal removal using a novel plant-based biosorbent banana floret: kinetic, equilibrium, thermodynamics and desorption studies. In Desalination water treatment, IntechOpen. pp. 385–411

  96. Zaferani SPG, Emami MRS, Amiri MK, Binaeian E (2019) Optimization of the removal Pb (II) and its Gibbs free energy by thiosemicarbazide modified chitosan using RSM and ANN modeling. Int J Biol Macromol 139:307–319

    Google Scholar 

  97. Nag S, Bar N, Das SK (2020) Cr (VI) removal from aqueous solution using green adsorbents in continuous bed column–statistical and GA-ANN hybrid modelling. Chem Eng Sci 226:115904

    Google Scholar 

  98. Broujeni BR, Nilchi A, Azadi F (2021) Adsorption modeling and optimization of thorium (IV) ion from aqueous solution using chitosan/TiO2 nanocomposite: application of artificial neural network and genetic algorithm. Environ Nanotechnol Monit Manag 15:100400

    Google Scholar 

  99. Prabhu AA, Chityala S, Jayachandran D, Deshavath NN, Veeranki VD (2021) A two step optimization approach for maximizing biosorption of hexavalent chromium ions (Cr (VI)) using alginate immobilized Sargassum sp in a packed bed column. Sep Sci Technol 56:90–106

    Google Scholar 

  100. Engin AB, Özdemir Ö, Turan M, Turan AZ (2008) Color removal from textile dyebath effluents in a zeolite fixed bed reactor: determination of optimum process conditions using Taguchi method. J Hazard Mater 159:348–353

    Google Scholar 

  101. van Laarhoven PJM, Aarts EHL (1987) Simulated annealing. In van Laarhoven PJM, Aarts EHL (eds) Simulated annealing theory applications, Springer Netherlands, Dordrecht, pp. 7–15. https://doi.org/10.1007/978-94-015-7744-1_2

  102. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  103. Moradi P, Hayati S, Ghahrizadeh T (2020) Modeling and optimization of lead and cobalt biosorption from water with Rajsathan pistachio shell, using experiment based models of ANN and GP, and the grey wolf optimizer. Chemom Intell Lab Syst 202:104041

    Google Scholar 

  104. Rajabioun R (2011) Cuckoo optimization algorithm. Appl Soft Comput 11:5508–5518

    Google Scholar 

  105. Khajeh M, Jahanbin E (2014) Application of cuckoo optimization algorithm–artificial neural network method of zinc oxide nanoparticles–chitosan for extraction of uranium from water samples. Chemom Intell Lab Syst 135:70–75

    Google Scholar 

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Acknowledgements

The authors would further like to acknowledge Dr. Balasubramanian Kandasubramanian, Professor at the Defence Institute of Advanced Technology Pune and Faculty of Computer Science, IIT Jodhpur, for reviewing the manuscript and providing valuable feedback.

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Authors and Affiliations

  1. Center for Emerging Technologies for Sustainable Development, Indian Institute of Technology Jodhpur, Jodhpur, India

    Amrita Nighojkar & Anand Plappally

  2. Department of Metallurgical and Materials Engineering, Defence Institute of Advanced Technology (D.U.), Pune, India

    Amrita Nighojkar

  3. Department of Mechanical Engineering, Worcester Polytechnic Institute (WPI), Worcester, USA

    Winston Soboyejo

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  1. Amrita Nighojkar
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Nighojkar, A., Plappally, A. & Soboyejo, W. Neural network models for simulating adsorptive eviction of metal contaminants from effluent streams using natural materials (NMs). Neural Comput & Applic 35, 5751–5767 (2023). https://doi.org/10.1007/s00521-023-08315-4

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