Artificial Neural Network and Response Surface Design for Modeling the Competitive Biosorption of Pentachlorophenol and 2,4,6-Trichlorophenol to Canna indica L. in Aquaponia

Volume 6, Issue 01, Pages 79-99, March 2023 *** Field: Method in Environmental Chemistry

  • Enyoh Christian Ebere, Corresponding Author, Group Research in Analytical Chemistry, Environment and Climate change (GRACE&CC), Department of Chemistry, Faculty of Science, Imo State University, Owerri, Imo State
  • Prosper Ovuoraye Department of Chemical Engineering, Federal University of Petroleum Resource, P.M.B 1221 Effurun, Nigeria
  • Obinna Isiuku Department of Chemistry, Faculty of Physical Sciences, Imo State University, Imo State, Nigeria
  • Chinenye Adaobi Igwegbe Department of Chemical Engineering Nnamdi Azikiwe University, P. M. B. 5025, Awka, Nigeria
DOI: https://doi.org/10.24200/amecj.v6.i01.228
Keywords: Phytoremediation, UV-Vis spectrometry, Chlorophenol biosorption, Canna Indica L, ANN modeling, RSM optimization

Abstract

The continuous exposure of the environment to carcinogenic wastes and toxic chlorophenols such as pentachlorophenol (PCP) and 2,4,6-trichlorophenol (TCP) resulting from industrial production activities is become a great concern. The search for cost efficient and ecofriendly approach to phytoremediation of water will guarantee sustainability. The present research work is concerned with cost benefit evaluation, and the optimization modeling of the competitive biosorption of PCP and TCP from aqueous solution to Cana indica. L (CiL-plant) using response surface methodology (RSM) and artificial neural network (ANN) model. The predictive performances of the ANN model and the RSM were compared based on their statistical metrics. The antagonistic and synergetic effect of significant biosorption variables (pH, initial concentration, and exposure time) on the biosorption process were studied at p-values ≤0.005. The optimized output transcends to PCP and TCP removal rates of 90% and 87.99% efficiencies at predicted r-squared ≤0.9999, at 95% confidence interval. The cost benefit evaluation established that at the optimum conditions, the cost of operating the removal of TCP from aqueous solution will save $ 7.72 compared to PCP. The reliability of the optimization model based on design of experiment was proven to be more sustainable compared to the one-factor-at-a-time methodologies.

References

USEPA, 2,4,6 Trichlorophenol. United States Environmental Protection Agency, 2000. https://www.epa.gov/sites/default/files/2016-09/documents/2-4-6-trichlorophenol.pdf

WHO, The World Health Organization recommended classification of pesticides by hazard and guidelines to classification 2004, International Programme on Chemical Safety, pp.19-39, 2005. https://apps.who.int/iris/rest/bitstreams/1278712/retrieve

ATSDR, Pentachlorophenol, Agency for Toxic Substances and Disease Registry, 2021. https://wwwn.cdc.gov/TSP/substances/ToxSubstance.aspx?toxid=70

A.I. Olayinka, F.A. Ademola, I.A. Emmanuel, Assessment of Organochlorine and Organophosphorus pesticides residue in water and sediments from Ero River in South-Western Nigeria, J. Chem. Biol. Phys. Sci. D, 5 (2015) 4679–4690. https://www.jcbsc.org

A.W. Verla, J. E. Ejiako, E.N. Verla, I. G. Ndubuisi, C. E. Enyoh, Potential health risk index of polyaromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in fish species from Oguta Lake, Nigeria, Int. J. Environ. Anal. Chem., (2021) 1946687. https://doi.org/10.1080/03067319.2021.1946687

. B. M. Obidike, A. W. Verla, C. E. Enyoh, E. N. Verla, O. N. Onyekachi, Fate and distribution of pesticides residues in water sources in Nigeria: A review, Int. J. Agrochem., 6 (2020) 29–48. https://chemical.journalspub.info/index.php?journal=IJCPD&page=article&op=view&path%5B%5D=1015

B.O. Isiuku, C.E. Enyoh, Water pollution by heavy metal and organic pollutants: Brief review ofsources, effects and progress on remediation with aquatic plants, Anal. Methods Environ. Chem. J., 2 (3) (2019) 5-38. https://doi.org/10.24200/amecj.v2.i03.66

C. E. Enyoh, B. O. Isiuku, Competitive biosorption and phytotoxicity of chlorophenols in aqueous solution to Canna indica L, Curr. Res. Green Sustain. Chem., 4 (2021) 100094. https://10.1016/j.crgsc.2021.100094

C.E. Enyoh, B. O. Isiuku, Removal of pentachlorophenol (PCP) from aqueous solution using Canna indica L.: kinetics, isotherm and thermodynamic studies, Arab. J. Chem., 8(2) (2021) 193-213. http://www.mocedes.org/ajcer/volume8/AJCER-13-Enyoh-2021.pdf

C.E. Enyoh, B.O. Isiuku, 2,4,6-Trichlorophenol (TCP) removal from aqueous solution using Canna iindica L.: kinetic, isotherm and Thermodynamic studies, Chem. Ecol., 37 (2020) 64-82. https://doi.org/10.1080/02757540.2020.1821673

D.H. Tran, T.M.H. Vi, T.T.H. Dang, R. Narbaitz, Pollutant removal by Canna Generalis in tropicalconstructed wetlands for domestic wastewater treatment, Global J. Environ. Sci. Manage., 5 (3) (2019) 331-344. https://www.gjesm.net/article_35321_dc150b14811afe228b6bea1bcc85228f.pdf

C.E. Enyoh, B.O. Isiuku, Determination and human health risk assessment of heavy metals in flood basin soils in Owerri, southeastern Nigeria, Chem. Africa, 3 (2020) 1059–1071. https://doi.org/10.1007/s42250-020-00171-2

C.E. Enyoh, Q. Wang, P.E. Ovuoraye, Response surface methodology for modeling the adsorptive uptake of phenol from aqueous solution using adsorbent polyethylene terephthalate microplastics, Chem. Eng. J. Adv., 12 (2022) 100370. https://doi.org/10.1016/j.ceja.2022.100370

S. Saini, J. Chawla, R. Kumar, Response surface methodology (RSM) for optimization of cadmium ions adsorption using C16-6-16 incorporated mesoporous MCM-41, SN Appl. Sci., 1 (2019) 894. https://doi.org/10.1007/s42452-019-0922-5

P. E. Ovuoraye, V.I. Ugonabo, G.F. Nwokocha, Optimization studies on turbidity removal from cosmetics wastewater using Aluminum Sulfate and blends of fishbone, SN Appl. Sci., 3 (2021) 488. https://dio.org/10.1007/s42452-021-04458-y

C. A. Igwegbe, L. Mohammed, S. Ahmadi, A. Rahdar, Modeling of adsorption of methylene blue dye on Ho-CaWO4 nanoparticles using response surface methodology and artificial neural network (ANN) techniques, Methods X, 6 (2019) 1779-1797. https://doi.org/10.1016/j.mex2019.07.016

C.A. Igwegbe, O.D. Onukwuli, J.O. Ighalo, M.C. Menkiti, Bio-coagulation-flocculation (BCF) of municipal solid waste leachate using picralima nitida extract: RSM and ANN modelling, Curr. Res. Green Sustain. Chem. 4 (2021) 100078. https://doi.org/10.1016/j.crgsc.2021.100078

C.E. Enyoh, C. E. Duru, P. E. Ovuoraye, Q. Wang, Evaluation of nanoplastics toxicity to the human placenta in systems, J. Hazard. Mater., 446 (2023) 130600. https://doi.org/ 10.1016/j.jhazmat.2022.130600

R. Hasanzadeh, M. Mojaver, T. Azdast, C. B. Park, Polyethylene waste gasification syngas analysis and multi-objective optimization using central composite design for simultaneous minimization of required heat and maximization of exergy efficiency, Energy Convers. Manag., 247 (2021) 114713. https://doi.org/10.1016/j.enconman.2021.114713

L. Lujian, S. Tang, X. Wang, X. Sun, A. Yu, Adsorption of malachite green from aqueous solution by nylon microplastics: Reaction mechanism and the optimum conditions by response surface methodology, Process Saf. Environ. Prot., 140 (2020) 339-347. https://doi.org/10.1016/j.psep.2020.05.019

C. E. Enyoh, Q. Wang, P. E. Ovuoraye, T. O. Maduka, Toxicity evaluation of microplastics to aquatic organisms through molecular simulations and fractional factorial designs, Chemosphere, 308 (2022) 136342. https://doi.org/10.1016/j.chemosphere.2022.136342

C.E. Duru, C.E. Enyoh, I.A. Duru, M. Enedoh, Degradation of PET nanoplastic oligomers at the novel PHL7 target: insights from molecular docking and machine learning, J. Niger. Soc. Phys. Sci., 5 (2023) 1154. https://doi.org/10.46481/jnsps.2022.1154

G. Aicha, I. Soumaya, E. Noureddine, A. T. Mohamed, G. Djamel, H. Ahmed, M. Abdelhakim, A. Badreddine, and K. Lioua, Comparative study of chemical coagulation and electrocoagulation for the treatment of real textile wastewater: optimization and operating cost estimation, ACS Omega, 7(26) (2022) 22456–22476. https://doi.org/10.1021/acsomega.2c01652

P.E. Ovuoraye, L.C. Okpala, V.I. Ugonabo, Clarification efficacy of eggshell and aluminum base coagulant for the removal of total suspended solids (TSS) from cosmetics wastewater by coag-flocculation, Chem. Pap., 75 (2021) 4759–4777. https://doi.org/10.1007/s11696-021-01703-x

D. Wang, S. Thunell, L. Ulrika, A machine learning frame work to improve effluent quality control in wastewater treatment plants, Sci. Total Environ., 784 (2021) 147138. https://doi.org/10.1016/j.scitotenv.2021.147138

Y. Zhang, Y. Wu, Introducing machine learning models to response surface methodologies. Publisher: Intechopen, 270 pages, 2021. https://doi.org/10.5772/intechopen.9819

P. E. Ovuoraye, V. I. Ugonabo, M. A. Tahir, P. A. Balogun, Kinetics-driven coagulation treatment of petroleum refinery effluent using land snail shells: An empirical approach to environmental sustainability, Clean. Chem. Eng., 4 (2022) 100084. https://doi.org/10.1016/j.clce.2022.100084

H. Guo, Prediction of effluent concentration in a wastewater treatment plant using machine learning models, J. Environ. Sci., 32 (2015) 90-101. https://doi.org/10.1016/j.jes.2015.01.007.

M. Radhika, K. Palanivelu, Adsorptive removal of chlorophenols from aqueous solution by low-cost adsorbent—Kinetics and isotherm analysis, J. Hazard. Mater., 138(1) (2006) 116-124. https://doi.org/10.1016/j.jhazmat.2006.05.045

S.K. Nadavala, M. Asif, A. M. Poulos, M. Suguna, M. I. Al‐Hazza, Equilibrium and kinetic studies of biosorptive removal of 2,4,6‐trichlorophenol from aqueous solutions using untreated agro‐waste pine cone biomass, Processes, 7 (2019) 757. http://doi:10.3390/pr7100757

R. Bhutiani, N. Rai, P. K. Sharma, K. Rausa, and F. Ahamad, Phytoremediation efficiency of water hyacinth (E. crassipes), canna (C. indica) and duckweed (L. minor) plants in treatment of sewage water, Environ. Conserv. J., 20 (2019) 143-156. https://doi.org/10.36953/ECJ.2019.1008.1221

Published
2023-03-30
How to Cite
Ebere, E., Ovuoraye, P., Isiuku, O., & Igwegbe, C. (2023). Artificial Neural Network and Response Surface Design for Modeling the Competitive Biosorption of Pentachlorophenol and 2,4,6-Trichlorophenol to Canna indica L. in Aquaponia. Analytical Methods in Environmental Chemistry Journal, 6(01), 79-99. https://doi.org/10.24200/amecj.v6.i01.228
Issue
Vol 6 No 01 (2023): Issue No. One
Section
Original Article

 JOURNAL PUBLISHING AGREEMENT

 PLEASE PROVIDE US THE FOLLOWING INFORMATION,

 Article entitled:

Corresponding author:

To be published in the journal:

 Your Status

 I am the sole author of the manuscript

  • I am an Iranian government employee.
  • I am a European government employee
  • I am a Asian government
  • None of the above

 I am one author signing on behalf of all co-authors of the manuscript

  • I am an Iranian government employee.
  • I am a European government employee
  • I am a Asian government
  • None of the above

 Please tick the above blanks (as appropriate), review the Journal Publishing Agreement, and then sign and date the document in black ink.

Published Journal Article: the author may share a link to the formal publication through the relevant DOI. Additionally theses and dissertations which contain embedded Published Journal Articles as part of the formal submission may be hosted publicly by the awarding institution with a link to the formal publication through the relevant DOI. Any other sharing of Published Journal Articles is by agreement with the publisher only.

 Signed: ______________________________________ Name printed: ___________________________________________

 Title and Company (if employer representative): _______________________Date: __________________________________

DOWNLOAD COPYRIGHT FORM