Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) For Optimizing Nano-Biochar Application in Soil Remediation Projects in Chas

Authors

  • Mukesh Kumar Sinha University Department of Mathematics, Binod Bihari Mahto Koyalanchal University, Dhanbad 828103, Jharkhand, India; Guru Gobind Singh Educational Society’s Technical Campus (affiliated to Jharkhand University of Technology, Ranchi), Chas, Bokaro 827013, Jharkhand, India https://orcid.org/0009-0006-2088-8547
  • Rajesh Kumar Tiwari University Department of Mathematics, Binod Bihari Mahto Koyalanchal University, Dhanbad 828103, Jharkhand, India https://orcid.org/0009-0002-2278-6887

DOI:

https://doi.org/10.26713/cma.v15i5.2903

Keywords:

ANFIS, Nano-biochar, Soil remediation, Chas, Optimization, Environmental management

Abstract

 The use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) is investigated into this work for optimizing nano-biochar application in soil remediation projects in Chas, Bokaro, Jharkhand, India. The research addresses the critical need for effective soil remediation techniques in areas affected by industrial pollution and agricultural intensification. By leveraging ANFIS, an intelligent hybrid system that blends neural networks with fuzzy logic, we aim to enhance the precision and efficiency of nano-biochar application in soil remediation efforts. The study encompasses extensive field experiments, laboratory analyses, and computational modeling to develop a well ANFIS model for forecast optimal nano-biochar dosages based on various soil parameters and contaminant levels. Results demonstrate the superior performance of ANFIS in optimizing nano-biochar application compared to conventional methods, leading to improved soil quality indicators and reduced remediation time. This research contributes to the advancement of sustainable soil management practices and provides a valuable tool for environmental practitioners and policymakers in Chas and similar regions facing soil contamination challenges.

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References

M. Ahmad, A. U. Rajapaksha, J. E. Lim, M. Zhang, N. Bolan, D. Mohan, M. Vithanage, S. S. Lee and Y. S. Ok, Biochar as a sorbent for contaminant management in soil and water: a review, Chemosphere 99 (2014), 19 – 33, DOI: 10.1016/j.chemosphere.2013.10.071.

M. Ahmadlou, M. Karimi, S. Alizadeh, A. Shirzadi, D. Parvinnejhad, H. Shahabi and M. Panahi, Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA), Geocarto International 34(11) (2018), 1252 – 1272, DOI: 10.1080/10106049.2018.1474276.

B. J. Alloway, Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability, 3rd edition, Springer, (2013), DOI: 10.1007/978-94-007-4470-7.

R. Bian, S. Joseph, L. Cui, G. Pan, L. Li, X. Liu, A Zhang, H. Rutlidge, S. Wong, C. Chia, C. Marjo, B. Gong, P. Munroe and S. Donne, A three-year experiment confirms continuous immobilization of cadmium and lead in contaminated paddy field with biochar amendment, Journal of Hazardous Materials 272 (2014), 121 – 128, DOI: 10.1016/j.jhazmat.2014.03.017.

J.-S. R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics 23(3) (1993), 665 – 685, DOI: 10.1109/21.256541.

J.-S. R. Jang, C.-T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing: a Computational Approach to Learning and Machine Intelligence, Prentice Hall, Upper Saddle River, NJ, xxvi + 614 pages (1997).

O. Kisi and M. Ay, Comparison of ANN and ANFIS techniques in modeling dissolved oxygen, in: Proceedings of the 16th International Water Technology Conference (IWTC16), Istanbul, Turkey (2014).

S. Kuppusamy, T. Palanisami, M. Megharaj, K. Venkateswarlu and R. Naidu, In-situ remediation approaches for the management of contaminated sites: a comprehensive overview, in: Reviews of Environmental Contamination and Toxicology, P. de Voogt (editor), Vol. 236, Springer, Cham., DOI: 10.1007/978-3-319-20013-2_1.

J. Lehmann, M. C. Rillig, J. Thies, C. A. Masiello, W. C. Hockaday and D. Crowley, Biochar effects on soil biota — a review, Soil Biology and Biochemistry 43(9) (2011), 1812 – 1836, DOI: 10.1016/j.soilbio.2011.04.022.

J. J. Manya, Pyrolysis for biochar purposes: a review to establish current knowledge gaps and research needs, Environmental Science & Technology 46(15) (2012), 7939 – 7954, DOI: 10.1021/es301029g.

A. Mellit and S. A. Kalogirou, ANFIS-based modelling for photovoltaic power supply system: A case study, Renewable Energy 36(1) (2011), 250 – 258, DOI: 10.1016/j.renene.2010.06.028.

P. C. Nayak, K. P. Sudheer, D. M. Rangan and K. S. Ramasastri, A neuro-fuzzy computing technique for modeling hydrological time series, Journal of Hydrology 291(1-2) (2004), 52 – 66, DOI: 10.1016/j.jhydrol.2003.12.010.

L. Ouyang, F. Wang, J. Tang, L. Yu and R. Zhang, Effects of biochar amendment on soil aggregates and hydraulic properties, Journal of Soil Science and Plant Nutrition 13(4) (2013), 991 – 1002, DOI: 10.4067/S0718-95162013005000078.

L. Qian, B. Chen and D. Hu, Effective alleviation of aluminum phytotoxicity by manure-derived biochar, Environmental Science & Technology 47(6) (2013), 2737 – 2745, DOI: 10.1021/es3047872.

R. Setia, P. Gottschalk, P. Smith, P. Marschner, J. Baldock, D. Setia and J. Smith, Soil salinity decreases global soil organic carbon stocks, Science of The Total Environment 465 (2013), 267 – 272, DOI: 10.1016/j.scitotenv.2012.08.028.

R. M. S. Sharma and N. S. Raju, Correlation of heavy metal contamination with soil properties of industrial areas of Mysore, Karnataka, India by cluster analysis, International Research Journal of Environment Sciences 2(10) (2013), 22 – 27.

X. Tan, Y. Liu, G. Zeng, X. Wang, X. Hu, Y. Gu and Z. Yang, Application of biochar for the removal of pollutants from aqueous solutions, Chemosphere 125 (2015), 70 – 85, DOI: 10.1016/j.chemosphere.2014.12.058.

O. Taylan, Modelling and analysis of ozone concentration by artificial intelligent techniques for estimating air quality, Atmospheric Environment 150 (2017), 356 – 365, DOI: 10.1016/j.atmosenv.2016.11.030.

Y. Yao, B. Gao, M. Zhang, M. Inyang and A. R. Zimmerman, Effect of biochar amendment on sorption and leaching of nitrate, ammonium, and phosphate in a sandy soil, Chemosphere 89(11) (2012), 1467 – 1471, DOI: 10.1016/j.chemosphere.2012.06.002.

M. Zhang and B. Gao, Removal of arsenic, methylene blue, and phosphate by biochar/AlOOH nanocomposite, Chemical Engineering Journal 226 (2013), 286 – 292, DOI: 10.1016/j.cej.2013.04.089.

X. Zhang, H. Wang, L. He, K. Lu, A. Sarmah, J. Li, N. S. Bolan, J. Pei and H. Huang, Using biochar for remediation of soils contaminated with heavy metals and organic pollutants, Environmental Science and Pollution Research 20 (2013), 8472 – 8483, DOI: 10.1007/s11356-013-1659-0

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Published

31-12-2024
CITATION

How to Cite

Sinha, M. K., & Tiwari, R. K. (2024). Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) For Optimizing Nano-Biochar Application in Soil Remediation Projects in Chas. Communications in Mathematics and Applications, 15(5), 1443–1457. https://doi.org/10.26713/cma.v15i5.2903

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Research Article