Skip to main content
SpringerLink
Log in

Multifactor optimization for treatment of textile wastewater using complex salt–Luffa cylindrica seed extract (CS-LCSE) as coagulant: response surface methodology (RSM) and artificial intelligence algorithm (ANN–ANFIS)

  • Original Paper
  • Published:
Chemical Papers Aims and scope Submit manuscript

Abstract

The effectiveness of using complex salt–Luffa cylindrica seed extract (CS-LCSE) in a coagulation/flocculation (CF) method for the treatment of textile wastewater was investigated. Jar test procedure was used at different pH (2–10), dosage (1000–1800 mg/L) and stirring time (10–30 min). The optimum condition for the removal of chemical oxygen demand (COD) and color/total suspended solids (CTSS) from textile wastewater was determined. Response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict COD and CTSS removal efficiencies from textile wastewater under different conditions. The adequacy and predictive relevance of the three optimization methods were assessed using regression coefficient (R2), and mean square error (MSE). ANFIS (R2 0.9997, MSE 0.0002643), ANN (R2 0.9955, MSE 0.0845014) and RSM (R2 0.9474, MSE 1.049412) are the model indicators for CTSS removal, while for COD removal, the indicators are: ANFIS (R2 0.9996, MSE 0.0038472), ANN (R2 0.9885, MSE 0.0160658) and RSM (R2 0.9731, MSE 0.9083140). The suitability of ANFIS models over ANN and RSM in predicting COD and CTSS removal efficiency is demonstrated by the results obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

Adj R 2 :

Adjusted coefficient of determination

ANN:

Artificial neural network

ANFIS:

Adaptive neuro-fuzzy inference system

BBD:

Box–Behnken design

BOD:

Biochemical oxygen demand

CF:

Coagulation/flocculation

COD:

Chemical oxygen demand

CS:

Complex salt

CTSS:

Color/total suspended solids

FTIR:

Fourier transform infrared spectroscopy

GA:

Genetic algorithm

LCS:

Luffa cylindrica seed

LCSE:

Luffa cylindrica seed extract

MSE:

Mean square error

R 2 :

Coefficient of determination

RSM:

Response surface methodology

SEM:

Scanning electron microscope

TSK:

Takagi–Sugeno fuzzy

TW:

Textile wastewater

XRD:

X-ray diffraction

References

  • Adegbite AJ, Afolabi O, Ogunji JM (2017) Effect of two extractants on the chemical composition of the defatted seed of Luffa cylindrica. Int J Innov Sci Eng Technol 4(7):356–368

    Google Scholar 

  • Abitogun AS, Ashogbon AO, Polytechnic RG, State O (2010) Nutritional assessment and chemical composition of raw and defatted Luffa cylindrica seed flour. Ethnobot Leaflets 14:225–235

    Google Scholar 

  • Adesina OA, Abdulkareem F, Yusuff AS, Lala M, Okewale A (2019) Response surface methodology approach to optimization of process parameter for coagulation process of surface water using Moringa oleifera seed. S Afr J Chem Eng 28:46–51

    Google Scholar 

  • Adewuyi A, Vargas F (2017) Underutilized Luffa cylindrica sponge: a local bio-adsorbent for the removal of Pb(II) pollutant from water system. Beni-Suef Univ J Basic Appl Sci 6(2):118–126

    Google Scholar 

  • Ahmad R, Haseeb S (2015) Competitive adsorption of Cu2+ and Ni2+ on Luffa acutangula modified Tetraethoxysilane (LAP-TS) from the aqueous solution: thermodynamic and isotherm studies. Groundw Sustain Dev 1(1–2):146–154

    Google Scholar 

  • Al-sameraiy M (2017) A new approach using coagulation rate constant for evaluation of turbidity removal. Appl Water Sci 7(3):1439–1448

    CAS  Google Scholar 

  • Anadebe VC, Onukwuli OD, Abeng FE, Okafor NA, Ezeugo JO, Okoye CC (2020) Electrochemical-kinetics, MD-simulation and multi-input single-output (MISO) modeling using adaptive neuro-fuzzy inference system (ANFIS) prediction for dexamethasone drug as eco-friendly corrosion inhibitor for mild steel in 2 M HCl electrolyte. J Taiwan Inst Chem Eng 115:251–265

    CAS  PubMed  PubMed Central  Google Scholar 

  • Anastopoulos I, Pashalidis I (2020) Environmental applications of Luffa cylindrica-based adsorbents. J Mol Liq 319:114127

    CAS  Google Scholar 

  • Ani JU, Nnaji NJN, Onukwuli OD, Okoye COB (2012) Nephelometric and functional parameters response of coagulation for the purification of industrial wastewater using Detarium microcarpum. J Hazard Mater 243:59–66

    CAS  PubMed  Google Scholar 

  • Aniagor CO, Menkiti MC (2018) Kinetics and mechanistic description of adsorptive uptake of crystal violet dye by lignified elephant grass complexed isolate. J Environ Chem Eng 6(2):2105–2118

    CAS  Google Scholar 

  • AOAC (2005) Official methods of analysis, 16th edn. Association of Official Analytical Chemist, Gaitherburg

    Google Scholar 

  • AWWA, APHA, WEF (2012) Standard method for the examination of water and wastewater, 22 edn, New York

  • Badawi AK, Zaher K (2021) Hybrid treatment system for real textile wastewater remediation based on coagulation/flocculation, adsorption and filtration processes: performance and economic evaluation. J Water Process Eng 40:101963

    Google Scholar 

  • Baharlouei A, Jalilnejad E, Sirousazar M (2018) Fixed-bed column performance of methylene blue biosorption by Luffa cylindrica: statistical and mathematical modeling. Chem Eng Commun 205(11):1537–1554

    CAS  Google Scholar 

  • Betiku E, Ishola NB (2020) Optimization of sorrel oil biodiesel production by base heterogeneous catalyst from kola nut pod husk: neural intelligence-genetic algorithm versus neuro-fuzzy-genetic algorithm. Environ Prog Sustain Energy. https://doi.org/10.1002/ep.13393

    Article  Google Scholar 

  • Bruno P, Campo R, Giustra MG, De Marchis M, Di Bella G (2020) Bench scale continuous coagulation-flocculation of saline industrial wastewater contaminated by hydrocarbons. J Water Process Eng 34:101156

    Google Scholar 

  • Chamoli S (2015) ANN and RSM approach for modeling and optimization of designing parameters for a V down perforated baffle roughened rectangular channel. Alex Eng J 54(3):429–446

    Google Scholar 

  • Dal Magro Follmann HV, Souza E, Aguiar Battistelli A, Rubens Lapolli F, Lobo-Recio MÁ (2020) Determination of the optimal electrocoagulation operational conditions for pollutant removal and filterability improvement during the treatment of municipal wastewater. J Water Process Eng 36:101295

    Google Scholar 

  • Dalvand A, Gholibegloo E, Ganjali MR, Golchinpoor N, Khazaei M, Kamani H, Hosseini SS, Mahvi AH (2016) Comparison of Moringa stenopetala seed extract as a clean coagulant with Alum and Moringa stenopetala-Alum hybrid coagulant to remove direct dye from Textile Wastewater. Environ Sci Pollut Res 23(16):16396–16405

    CAS  Google Scholar 

  • de Souza MTF, de Almeida CA, Ambrosio E, Santos LB, de Souza Freitas TKF, Manholer DD, de Carvalho GM, Garcia LB (2016) Extraction and use of Cereus peruvianus cactus mucilage in the treatment of textile effluents. J Taiwan Inst Chem Eng 67:174–183

    Google Scholar 

  • Desai KM, Survase SA, Saudagar PS, Lele SS, Singhal RS (2008) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan. Biochem Eng J 41(3):266–273

    CAS  Google Scholar 

  • Elsayed K, Lacor C (2011) Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches. Powder Technol 212(1):115–133

    CAS  Google Scholar 

  • Ezemagu IG, Ejimofor MI, Menkiti MC, Nwobi-Okoye CC (2021) Modeling and optimization of turbidity removal from produced water using response surface methodology and artificial neural network. S Afr J Chem Eng 35:78–88

    Google Scholar 

  • Ezemagu IG, Menkiti MC, Ugonabo VI, Aneke MC (2016) Adsorptive approach on nepholometric study of paint effluent using Tympanotonos fuscatus extract. Bull Chem Soc Ethiop 30(3):377–390

    CAS  Google Scholar 

  • Gupta KN, Kumar R (2020) Fixed bed utilization for the isolation of xylene vapor: kinetics and optimization using response surface methodology and artificial neural network. Environ Eng Res 26(2):200105

    Google Scholar 

  • Hadi SM, Al-Mashhadani MKH, Eisa MY (2019) Optimization of dye adsorption process for Albizia lebbeck pods as a biomass using central composite rotatable design model. Chem Ind Chem Eng Q 25(1):39–46

    CAS  Google Scholar 

  • Hammoudi A, Moussaceb K, Belebchouche C, Dahmoune F (2019) Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Constr Build Mater 209:425–436

    Google Scholar 

  • Igwegbe CA, Oba SN, Aniagor CO, Adeniyi AG, Ighalo JO (2021) Adsorption of ciprofloxacin from water: a comprehensive review. J Ind Eng Chem 93:57–77

    CAS  Google Scholar 

  • Imen F, Lamia K, Asma T, Neacute ji G, Radhouane G (2013) Optimization of coagulation-flocculation process for printing ink industrial wastewater treatment using response surface methodology. Afr J Biotechnol 12(30):4819–4826

    Google Scholar 

  • Joshi S, Bajpai S, Jana S (2020) Application of ANN and RSM on fluoride removal using chemically activated D. sissoo sawdust. Environ Sci Pollut Res 27(15):17717–17729

    CAS  Google Scholar 

  • Karam A, Bakhoum ES, Zaher K (2020) Coagulation/flocculation process for textile mill effluent treatment: experimental and numerical perspectives. Int J Sustain Eng 14(5):983–995

    Google Scholar 

  • Khayet M, Zahrim AY, Hilal N (2011) Modelling and optimization of coagulation of highly concentrated industrial grade leather dye by response surface methodology. Chem Eng J 167(1):77–83

    CAS  Google Scholar 

  • Kim SC (2016) Application of response surface method as an experimental design to optimize coagulation–flocculation process for pre-treating paper wastewater. J Ind Eng Chem 38:93–102

    CAS  Google Scholar 

  • Maroneze MM, Zepka LQ, Vieira JG, Queiroz MI, Jacob-Lopes E (2014) A tecnologia de remoção de fósforo: Gerenciamento do elemento em resíduos industriais. Revista Ambiente e Agua 9(3):445–458

    Google Scholar 

  • Mdlovu NV, Lin KS, Chen ZW, Liu YJ, Mdlovu NB (2020) Treatment of simulated chromium-contaminated wastewater using polyethylenimine-modified zero-valent iron nanoparticles. J Taiwan Inst Chem Eng 108:92–101

    CAS  Google Scholar 

  • Menkiti MC, Okoani AO, Ejimofor MI (2018) Adsorptive study of coagulation treatment of paint wastewater using novel Brachystegia eurycoma extract. Appl Water Sci 8(6):1–15

    CAS  Google Scholar 

  • Menkiti M, Ezemagu I, Okolo B (2016) Perikinetics and sludge study for the decontamination of petroleum produced water (PW) using novel mucuna seed extract Collision factor for Brownian transport. Pet Sci 13(2):328–339

    CAS  Google Scholar 

  • Mohamad Yusof MS, Othman MHD, Abdul Wahab R, Abu Samah R, Kurniawan TA, Mustafa A, Rahman MA, Jaafar J, Ismail AF (2020) Effects of pre and post-ozonation on POFA hollow fibre ceramic adsorptive membrane for arsenic removal in water. J Taiwan Inst Chem Eng 110:100–111

    CAS  Google Scholar 

  • Naghibi SA, Salehi E, Khajavian M, Vatanpour V, Sillanpää M (2021) Multivariate data-based optimization of membrane adsorption process for wastewater treatment: multi-layer perceptron adaptive neural network versus adaptive neural fuzzy inference system. Chemosphere 267:129268

    CAS  PubMed  Google Scholar 

  • Nnaji P, Anadebe C, Onukwuli OD (2020a) Application of experimental design methodology to optimize dye removal by mucuna sloanei induced coagulation of dye-based wastewater. Desalin Water Treat 198:396–406

    CAS  Google Scholar 

  • Nnaji PC, Okolo BI, Onukwuli OD (2020b) Luffa cylindrica seed: Biomass for wastewater treament, sludge generation study at optimum conditions. Chem Ind Chem Eng Q 26(4):349–358

    Google Scholar 

  • Oboh I, Aluyor E, Audu T (2015) Kinetic modelling for zinc(II) ions biosorption onto Luffa cylindrica. In: AIP Conference Proceedings, 1653 (Ii).

  • Okolo BI, Nnaji PC, Onukwuli OD (2016) Nephelometric approach to study coagulation-flocculation of brewery effluent medium using Detarium microcarpum seed powder by response surface methodology. J Environ Chem Eng 4(1):992–1001

    CAS  Google Scholar 

  • Onu CE, Nwabanne JT, Ohale PC, Asadu CO (2020) Preparation and characterization of clay ANN and ANFIS models; critical comparative analysis of the three models; evaluation of mechanistic modeling of the adsorption process; optimization using genetic algorithm. S Afr J Chem Eng 36:24–42

    Google Scholar 

  • Onukwuli OD, Nnaji PC, Menkiti MC, Anadebe VC, Oke EO, Ude CN, Okafor NA (2021) Dual-purpose optimization of dye-polluted wastewater decontamination using bio-coagulants from multiple processing techniques via neural intelligence algorithm and response surface methodology. J Taiwan Inst Chem Eng 125:372–386

    CAS  Google Scholar 

  • Salem RH (2017) Functional characterization of luffa (Luffa cylindrica) seeds powder and their utilization to improve stabilized emulsions. Middle East J Appl Sci 7:613–625

    Google Scholar 

  • Samsami S, Mohamadi M, Sarrafzadeh MH, Rene ER, Firoozbahr M (2020) Recent advances in the treatment of dye-containing wastewater from textile industries: overview and perspectives. Process Saf Environ Prot 143:138–163

    CAS  Google Scholar 

  • Sangal VK, Kumar V, Mishra IM (2012) Optimization of structural and operational variables for the energy efficiency of a divided wall distillation column. Comput Chem Eng 40:33–40

    CAS  Google Scholar 

  • Sarkar S, Chowdhury R, Das R, Chakraborty S, Choi H, Bhattacharjee C (2014) Application of ANFIS model to optimise the photocatalytic degradation of chlorhexidine digluconate. RSC Adv 4(40):21141–21150

    CAS  Google Scholar 

  • Singh B, Kumar P (2020) Pre-treatment of petroleum refinery wastewater by coagulation and flocculation using mixed coagulant: optimization of process parameters using response surface methodology (RSM). J Water Process Eng 36:101317

    Google Scholar 

  • Thirunavukkarasu A, Nithya R (2020) Adsorption of acid orange 7 using green synthesized CaO/CeO2 composite: an insight into kinetics, equilibrium, thermodynamics, mass transfer and statistical models. J Taiwan Inst Chem Eng 111:44–62

    CAS  Google Scholar 

  • Wang J, Chen Y, Wang Y, Yuan S, Yu H (2011) Optimization of the coagulation-flocculation process for pulp mill wastewater treatment using a combination of uniform design and response surface methodology. Water Res 45(17):5633–5640

    CAS  PubMed  Google Scholar 

  • Youssefi S, Emam-Djomeh Z, Mousavi SM (2009) Comparison of artificial neural network (ANN) and response surface methodology (RSM) in the prediction of quality parameters of spray-dried pomegranate juice. Drying Technol 27(7):910–917

    Google Scholar 

  • Zhang W, Wei Q, Xiao J, Liu Y, Yan C, Liu J, Sand W, Chow CWK (2020) The key factors and removal mechanisms of sulfadimethoxazole and oxytetracycline by coagulation. Environ Sci Pollut Res 27(14):16167–16176

    CAS  Google Scholar 

  • Zhao B, Xiao W, Shang Y, Zhu H, Han R (2017) Adsorption of light green anionic dye using cationic surfactant-modified peanut husk in batch mode. Arab J Chem 10:S3595–S3602

    CAS  Google Scholar 

  • Zhao Z, Sun W, Ray MB, Ray AK, Huang T, Chen J (2019) Optimization and modeling of coagulation-flocculation to remove algae and organic matter from surface water by response surface methodology. Front Environ Sci Eng. https://doi.org/10.1007/s11783-019-1159-7

    Article  Google Scholar 

  • Zin KM, Effendi Halmi MI, Abd Gani SS, Zaidan UH, Samsuri AW, Abd Shukor MY (2020) Microbial decolorization of Triazo Dye, Direct Blue 71: an optimization approach using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Biomed Res Int 2020:1–16

    Google Scholar 

Download references

Acknowledgements

V.C. Anadebe is grateful to CSIR, India, and TWAS, Italy, for the Postgraduate Fellowship (Award No. 22/FF/CSIR-TWAS/2019) to purse research program in CSIR-CECRI, India. In addition, Alex Ekwueme Federal University Ndufu-Alike Ebonyi State, Nigeria, is acknowledged for the Research Leave to visit CECRI, India.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, Michael Okpara University, Umudike, Nigeria

    Patrick Chukwudi Nnaji & Chiamaka Joan Ude

  2. Department of Chemical Engineering, Alex Ekwueme Federal University Ndufu-Alike, Abakaliki, Ebonyi State, Nigeria

    Valentine Chikaodili Anadebe

  3. Corrosion and Material Protection Division, CSIR-Central Electrochemical Research Institute, Karaikudi, Tamil Nadu, 630003, India

    Valentine Chikaodili Anadebe

  4. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Valentine Chikaodili Anadebe

  5. Department of Chemical Engineering, Nnamdi Azikiwe University, Awka, Nigeria

    Okechukwu Dominic Onukwuli & Chukwunonso Chukwuzuloke Okoye

Authors
  1. Patrick Chukwudi Nnaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentine Chikaodili Anadebe
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Okechukwu Dominic Onukwuli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chukwunonso Chukwuzuloke Okoye
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chiamaka Joan Ude
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Chukwudi Nnaji.

Ethics declarations

Conflict of interest

On behalf of all other authors, I declare that there is no conflict of interest whatsoever. All authors read and agreed with the author order.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nnaji, P.C., Anadebe, V.C., Onukwuli, O.D. et al. Multifactor optimization for treatment of textile wastewater using complex salt–Luffa cylindrica seed extract (CS-LCSE) as coagulant: response surface methodology (RSM) and artificial intelligence algorithm (ANN–ANFIS). Chem. Pap. 76, 2125–2144 (2022). https://doi.org/10.1007/s11696-021-01971-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11696-021-01971-7

Keywords

Search

Navigation