An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach

Kumari, Sheetal; Agarwal, Smriti; Kumar, Manish; Sharma, Pinki; Kumar, Ajay; Hashem, Abeer; Alotaibi, Nouf H.; Abd-Allah, Elsayed Fathi; Garg, Manoj Chandra

doi:10.1038/s41598-025-87274-3

Cited by 79Adsorption and biosorption for pollutant removalCoagulation and Flocculation StudiesEnvironmental remediation with nanomaterials

An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach

Open access

Kumari, Sheetal, Agarwal, Smriti, Kumar, Manish, Sharma, Pinki, Kumar, Ajay, Hashem, Abeer, Alotaibi, Nouf H., Abd-Allah, Elsayed Fathi +1 more·2025·Scientific Reports

DOI 10.1038/s41598-025-87274-3PMC PMC11757716

Open this paper in CoChat — free account Open PDF

Abstract

This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent’s potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The evaluation of RSM, ANN, and ANFIS included the quantification of R2, mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The regression coefficients from the process modelling demonstrated that RSM (R2 = 0.9216), ANN (R2 = 0.8864), and ANFIS (R2 = 0.9589) all accurately predicted MB adsorptive removal. However, comparative statistical analysis revealed that the ANFIS model exhibited superior accuracy in data-based predictions compared to ANN and RSM models. The ideal pH for MB adsorption utilizing OSSB was established as 7. Additionally, favourable outcomes were obtained with 60-minute contact durations, 20 mg adsorbent quantities, and temperatures of 30 °C. The pseudo 2nd -order kinetic model for MB adsorption by OSSB was confirmed. The equilibrium data exhibited a superior fit with the Langmuir isotherm model in comparison to the Freundlich model. The thermodynamic adsorption parameters, including (∆G = -9.1489 kJ/mol), enthalpy change (∆H = -1457.2 kJ/mol), and entropy change (∆S = -19.03 J mol−1 K−1) indicated that the adsorption of MB onto the OSSB surface is exothermic and spontaneous under the experimental conditions. This research effectively showcased the potential of RSM, ANN, and ANFIS in simulating dye removal using OSSB. The generated parameter data proved valuable for the design and control of the adsorption process.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41598-025-87274-3

Cites

99 of 99 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

ANFIS is suitable for tasks that demand a balance between prediction performance and model explainability.
“These studies underscore ANFIS's suitability for tasks that demand a balance between prediction performance and model explainability.”
From GPT-based lifelong learning and ANFIS-driven reply memory ratio prediction for aspect-based sentiment analysis (2025) · cites this paper as [40]
The Results and discussion sections report strong ANFIS prediction accuracy for methylene blue adsorption and describe its fuzzy-system reasoning and IF-THEN rules (pages not provided). They do not assess model explainability or establish that ANFIS balances it against predictive performance, so that broader suitability claim is not fully supported. (LLM, conf=0.83)
Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

1 retracted

RETRACTED: Mobilization of contaminants: Potential for soil remediation and unintended consequences
Kumar, M., Bolan, N., Jasemizad, T., Padhye, L. P., Sridharan, S., Singh, L., Bolan, S., O'Connor, J., Zhao, H., Shaheen, S. M., et al. (2022) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.156373 · Cited by 104 · see citing works
Self-citation
Retracted
Dye and its removal from aqueous solution by adsorption: A review
Yagub, M. T., Sen, T. K., Afroze, S., & Ang, H. M. (2014) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2014.04.002 · Cited by 4,357 · see citing works
Mistakes and inconsistencies regarding adsorption of contaminants from aqueous solutions: A critical review
Tran, H. N., You, S., Hosseini–Bandegharaei, A., & Chao, H. (2017) · Water Research · doi:10.1016/j.watres.2017.04.014 · Cited by 2,947 · see citing works
Efficiency of various recent wastewater dye removal methods: A review
Katheresan, V., Kansedo, J., & Lau, S. Y. (2018) · Journal of Environmental Chemical Engineering · doi:10.1016/j.jece.2018.06.060 · Cited by 2,752 · see citing works
Response surface methodology
Khuri, A. I., & Mukhopadhyay, S. (2010) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.73 · Cited by 2,137 · see citing works
Cationic and anionic dye adsorption by agricultural solid wastes: A comprehensive review
Salleh, M. A. M., Mahmoud, D. K., Karim, W. A. W. A., & Idris, A. (2011) · Desalination · doi:10.1016/j.desal.2011.07.019 · Cited by 1,916 · see citing works
Removal of methylene blue by adsorption onto activated carbon developed from Ficus carica bast
Pathania, D., Sharma, S., & Singh, P. (2017) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2013.04.021 · Cited by 1,028 · see citing works
Removal of methylene blue from aqueous solution by sewage sludge-derived biochar: Adsorption kinetics, equilibrium, thermodynamics and mechanism
Fan, S., Wang, Y. N., Wang, Z., Tang, J., Tang, J., & Li, X. (2016) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2016.12.019 · Cited by 708 · see citing works
Recent advances in applications of activated carbon from biowaste for wastewater treatment: A short review
Wong, S. L., Ngadi, N., Inuwa, I. M., & Hassan, O. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.12.059 · Cited by 665 · see citing works
Multifunctional applications of biochar beyond carbon storage
Bolan, N., Hoang, S. A., Beiyuan, J., Gupta, S., Hou, D., Karakoti, A., Joseph, S., Jung, S., Kim, K.-H., Kirkham, M. B., et al. (2022) · International Materials Reviews · doi:10.1080/09506608.2021.1922047 · Cited by 638 · see citing works
Self-citation
Recent Advances in the Remediation of Textile-Dye-Containing Wastewater: Prioritizing Human Health and Sustainable Wastewater Treatment
Periyasamy, A. P. (2024) · Sustainability · doi:10.3390/su16020495 · Cited by 632 · see citing works
Recovery, regeneration and sustainable management of spent adsorbents from wastewater treatment streams: A review
Baskar, A. V., Bolan, N. S., Hoang, S. A., Sooriyakumar, P., Kumar, M., Singh, L., Jasemizad, T., Padhye, L. P., Singh, G., Vinu, A., et al. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153555 · Cited by 630 · see citing works
Self-citation
Ball milling as a mechanochemical technology for fabrication of novel biochar nanomaterials
Kumar, M., Xiong, X., Wan, Z., Sun, Y., Tsang, D. C. W., Gupta, J., Gao, B., Cao, X., Tang, J., & Ok, Y. S. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123613 · Cited by 561 · see citing works
Self-citation
Study on the adsorption of Neutral Red from aqueous solution onto halloysite nanotubes
Luo, P., Zhao, Y., Zhang, B., Liu, J., Yang, Y. L., & Liu, J. (2009) · Water Research · doi:10.1016/j.watres.2009.10.042 · Cited by 542 · see citing works
Review on recent advances of carbon based adsorbent for methylene blue removal from waste water
Santoso‬, ‪. B., Ediati, R., Kusumawati, Y., Bahruji, H., Sulistiono, D. O., & Prasetyoko, D. (2020) · Materials Today Chemistry · doi:10.1016/j.mtchem.2019.100233 · Cited by 502 · see citing works
Binary Langmuir and Freundlich isotherms for ideal adsorbed solutions
LeVan, M. D., & Vermeulen, T. (1981) · The Journal of Physical Chemistry · doi:10.1021/j150622a009 · Cited by 414 · see citing works
Performance of different coagulants in the coagulation/flocculation process of textile wastewater
Dotto, J., Fagundes‐Klen, M. R., Veit, M. T., Palácio, S. M., & Bergamasco, R. (2018) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2018.10.112 · Cited by 401 · see citing works
Current perspectives, recent advancements, and efficiencies of various dye-containing wastewater treatment technologies
Khan, M. Z., Singh, A., Khan, M. Z., Tabraiz, S., & Sheikh, J. N. (2023) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2023.103579 · Cited by 399 · see citing works
Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review
Jawad, J., Hawari, A. H., & Zaidi, S. M. J. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.129540 · Cited by 360 · see citing works
Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: A review
Ghaedi, A. M., & Vafaei, A. (2017) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2017.04.015 · Cited by 337 · see citing works
Remediation of soils and sediments polluted with polycyclic aromatic hydrocarbons: To immobilize, mobilize, or degrade?
Kumar, M., Bolan, N. S., Hoang, S. A., Sawarkar, A. D., Jasemizad, T., Gao, B., Keerthanan, S., Padhye, L. P., Singh, L., Kumar, S., et al. (2021) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2021.126534 · Cited by 304 · see citing works
Self-citation
High surface area mesoporous activated carbon-alginate beads for efficient removal of methylene blue
Nasrullah, A., Bhat, A. H., Naeem, A., Isa, M. H., & Danish, M. (2017) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2017.10.045 · Cited by 288 · see citing works
Modified biochar as a green adsorbent for removal of hexavalent chromium from various environmental matrices: Mechanisms, methods, and prospects
Ambika, S., Kumar, M., Pisharody, L., Malhotra, M., Kumar, G., Sreedharan, V., Singh, L., Nidheesh, P. V., & Bhatnagar, A. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.135716 · Cited by 266 · see citing works
Self-citation
Biochar application for greenhouse gas mitigation, contaminants immobilization and soil fertility enhancement: A state-of-the-art review
Abhishek, K., Shrivastava, A., Vimal, V., Gupta, A. K., Bhujbal, S. K., Biswas, J. K., Singh, L., Ghosh, P., Pandey, A., Sharma, P., et al. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.158562 · Cited by 265 · see citing works
Self-citation
Removing miscellaneous heavy metals by all-in-one ion exchange-nanofiltration membrane
Fu, Z., Jiang, S.-K., Chao, X., Zhang, C., Shi, Q., Wang, Z., Liu, M., & Sun, S. (2022) · Water Research · doi:10.1016/j.watres.2022.118888 · Cited by 228 · see citing works
Optimal oxidation with nitric acid of biochar derived from pyrolysis of weeds and its application in removal of hazardous dye methylene blue from aqueous solution
Güzel, F., Sayğılı, H., Sayğılı, G. A., Koyuncu, F., & Yılmaz, C. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.01.029 · Cited by 213 · see citing works
Critical Review on Biochar‐Supported Catalysts for Pollutant Degradation and Sustainable Biorefinery
Kumar, M., Xiong, X., Sun, Y., Yu, I. K. M., Tsang, D. C. W., Hou, D., Gupta, J., Bhaskar, T., & Pandey, A. (2020) · Advanced Sustainable Systems · doi:10.1002/adsu.201900149 · Cited by 209 · see citing works
Self-citation
Novel synthesis of Ni/Fe layered double hydroxides using urea and glycerol and their enhanced adsorption behavior for Cr(VI) removal
El-Reesh, G. Y. A., Farghali, A. A., Taha, M., & Mahmoud, R. (2020) · Scientific Reports · doi:10.1038/s41598-020-57519-4 · Cited by 208 · see citing works
Advanced oxidation processes and nanomaterials -a review
Kurian, M. (2021) · Cleaner Engineering and Technology · doi:10.1016/j.clet.2021.100090 · Cited by 195 · see citing works
Waste to catalyst: Role of agricultural waste in water and wastewater treatment
Kumar, M., Selvaraj, A., Hassani, A., & Nidheesh, P. V. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.159762 · Cited by 181 · see citing works
Self-citation
Terminalia catappa shell as low-cost biosorbent for the removal of methylene blue from aqueous solutions
Hevira, L., Zilfa, Z., Rahmayeni, R., Ighalo, J. O., Aziz, H., & Zein, R. (2021) · Journal of Industrial and Engineering Chemistry · doi:10.1016/j.jiec.2021.01.028 · Cited by 177 · see citing works
Adsorptive decontamination of rhodamine-B from water using banana peel powder: A biosorbent
Singh, S., Parveen, N., & Gupta, H. (2018) · Environmental Technology & Innovation · doi:10.1016/j.eti.2018.09.001 · Cited by 176 · see citing works
RSM-CCD design of malachite green adsorption onto activated carbon with multimodal pore size distribution prepared from Amygdalus scoparia: Kinetic and isotherm studies
Bagheri, R., Ghaedi, M., Asfaram, A., Dil, E. A., & Javadian, H. (2019) · Polyhedron · doi:10.1016/j.poly.2019.07.037 · Cited by 166 · see citing works
Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS
Dolatabadi, M., Mehrabpour, M., Esfandyari, M., Alidadi, H., & Davoudi, M. (2018) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2018.07.012 · Cited by 164 · see citing works
Biosorption optimization, characterization, immobilization and application of Gelidium amansii biomass for complete Pb2+ removal from aqueous solutions
El‐Naggar, N. E., Hamouda, R. A., Mousa, I., Abdel‐Hamid, M. S., & Rabei, N. H. (2018) · Scientific Reports · doi:10.1038/s41598-018-31660-7 · Cited by 163 · see citing works
Predictive capability evaluation of RSM, ANFIS and ANN: A case of reduction of high free fatty acid of palm kernel oil via esterification process
Betiku, E., Odude, V. O., Ishola, N. B., Bamimore, A., Osunleke, A. S., & Okeleye, A. A. (2016) · Energy Conversion and Management · doi:10.1016/j.enconman.2016.07.030 · Cited by 163 · see citing works
Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators
You, H., Ma, Z., Tang, Y., Wang, Y., Yan, J., Ni, M., Cen, K., & Huang, Q. (2017) · Waste Management · doi:10.1016/j.wasman.2017.03.044 · Cited by 149 · see citing works
Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance
Sada, S. O., & Ikpeseni, S. C. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06136 · Cited by 146 · see citing works
Zeolites in wastewater treatment: A comprehensive review on scientometric analysis, adsorption mechanisms, and future prospects
Kumari, S., Chowdhry, J., Kumar, M., & Garg, M. C. (2024) · Environmental Research · doi:10.1016/j.envres.2024.119782 · Cited by 144 · see citing works
Self-citation
Adsorption of low-cost absorption materials based on biomass ( Cortaderia selloana flower spikes) for dye removal: Kinetics, isotherms and thermodynamic studies
Jia, Z., Li, Z., Ni, T., & Li, S. (2016) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2016.12.059 · Cited by 140 · see citing works
Chemically modified sugarcane bagasse-based biocomposites for efficient removal of acid red 1 dye: Kinetics, isotherms, thermodynamics, and desorption studies
Kamran, U., Bhatti, H. N., Noreen, S., Tahir, M. A., & Park, S. (2021) · Chemosphere · doi:10.1016/j.chemosphere.2021.132796 · Cited by 133 · see citing works
Ultrasound assisted mixed azo dye adsorption by chitosan–graphene oxide nanocomposite
Banerjee, P., Barman, S. R., Mukhopadhayay, A., & Das, P. (2016) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2016.10.009 · Cited by 128 · see citing works
Generation of high-efficient biochar for dye adsorption using frass of yellow mealworms (larvae of Tenebrio molitor Linnaeus) fed with wheat straw for insect biomass production
Yang, S.-S., Chen, Y.-D., Kang, J.-H., Xie, T.-R., He, L., Xing, D.-F., Ren, N.-Q., Ho, S.-H., & Wu, W.-M. (2019) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2019.04.005 · Cited by 126 · see citing works
Wastewater treatment containing methylene blue dye as pollutant using adsorption by chitosan lignin membrane: Development of membrane, characterization and kinetics of adsorption
Vedula, S. S., & Yadav, G. D. (2021) · Journal of the Indian Chemical Society · doi:10.1016/j.jics.2021.100263 · Cited by 123 · see citing works
Applicability of dragon fruit (Hylocereus polyrhizus) peels as low-cost biosorbent for adsorption of methylene blue from aqueous solution: kinetics, equilibrium and thermodynamics studies
Jawad, A. H., Kadhum, A. M., & Ngoh, Y. S. (2018) · Desalination and Water Treatment · doi:10.5004/dwt.2018.21976 · Cited by 120 · see citing works
Optimization and Experimental Design of the Pb2+ Adsorption Process on a Nano-Fe3O4‑Based Adsorbent Using the Response Surface Methodology
Singh, R., & Bhateria, R. (2020) · ACS Omega · doi:10.1021/acsomega.0c04284 · Cited by 118 · see citing works
Graphene oxide nanoplatelets synthesized with carbonized agro-waste biomass as green precursor and its application for the treatment of dye rich wastewater
Goswami, S., Banerjee, P., Datta, S., Mukhopadhayay, A., & Das, P. (2017) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2017.01.003 · Cited by 116 · see citing works
Utilization of watermelon (Citrullus lanatus) rinds as a natural low-cost biosorbent for adsorption of methylene blue: kinetic, equilibrium and thermodynamic studies
Jawad, A. H., Ngoh, Y. S., & Radzun, K. A. (2018) · Journal of Taibah University for Science · doi:10.1080/16583655.2018.1476206 · Cited by 116 · see citing works
Soybean hulls as a low‐cost biosorbent for removal of methylene blue contaminant
Cusioli, L. F., Quesada, H. B., Baptista, A. T. A., Gomes, R. G., & Bergamasco, R. (2019) · Environmental Progress & Sustainable Energy · doi:10.1002/ep.13328 · Cited by 105 · see citing works
Artificial intelligence modeling of cadmium(II) biosorption using rice straw
Nasr, M., Mahmoud, A. E. D., Fawzy, M., & Radwan, A. A. (2015) · Applied Water Science · doi:10.1007/s13201-015-0295-x · Cited by 101 · see citing works
Treatment of textile wastewater containing acid dye using novel polymeric graphene oxide nanocomposites (GO/PAN,GO/PPy, GO/PSty)
Noreen, S., Tahira, M., Ghamkhar, M., Hafiz, I., Bhatti, H. N., Nadeem, R., Murtaza, M. A., Yaseen, M. S., Sheikh, A. A., Naseem, Z., et al. (2021) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2021.06.007 · Cited by 94 · see citing works
Non-linear adsorption characteristics of modified pine wood sawdust optimised for adsorption of Cd(II) from aqueous systems
Hashem, A., Badawy, S. M., Farag, S., Mohamed, O. A., Fletcher, A. J., & Taha, G. M. (2020) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2020.103966 · Cited by 90 · see citing works
Self-citation
Application of Activated Carbon Banana Peel Coated with Al2O3-Chitosan for the Adsorptive Removal of Lead and Cadmium from Wastewater
Ramutshatsha-Makhwedzha, D., Mbaya, R., & Mavhungu, M. L. (2022) · Materials · doi:10.3390/ma15030860 · Cited by 87 · see citing works
Chemically reduced tea waste biochar and its application in treatment of fluoride containing wastewater: Batch and optimization using response surface methodology
Roy, S., Sengupta, S., Manna, S., & Das, P. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.03.009 · Cited by 86 · see citing works
Prediction of dioxin emission from municipal solid waste incineration based on expansion, interpolation, and selection for small samples
Tang, J., Xia, H., Aljerf, L., Wang, D., & Ukaogo, P. O. (2022) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2022.108314 · Cited by 79 · see citing works
A novel hetero-exopolysaccharide for the adsorption of methylene blue from aqueous solutions: Isotherm, kinetic, and mechanism studies
Li, C., Chen, D., Ding, J., & Shi, Z. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.121800 · Cited by 74 · see citing works
Optimization of xylanase production by Streptomyces sp. P12-137 using response surface methodology and central composite design
Coman, G., & Bahrim, G. E. (2011) · Annals of Microbiology · doi:10.1007/s13213-010-0195-0 · Cited by 73 · see citing works
Congo red dye adsorption onto cationic amino-modified walnut shell: Characterization, RSM optimization, isotherms, kinetics, and mechanism studies
Rose, P. K., Kumar, R., Kumar, R., Kumar, M., & Sharma, P. (2023) · Groundwater for Sustainable Development · doi:10.1016/j.gsd.2023.100931 · Cited by 73 · see citing works
Self-citation
AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis
Kumari, S., Chowdhry, J., & Garg, M. C. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.119968 · Cited by 71 · see citing works
Self-citation
Equilibrium isotherms, kinetics, and thermodynamics studies of methylene blue adsorption on pomegranate (Punica granatum) peels as a natural low-cost biosorbent
Jawad, A. H., Waheeb, A. S., Rashid, R. A., Nawawi, W. I., & Yousif, E. A. (2018) · Desalination and Water Treatment · doi:10.5004/dwt.2018.22021 · Cited by 70 · see citing works
Machine learning and modelling approach for removing methylene blue from aqueous solutions: Optimization, kinetics and thermodynamics studies
Kumari, S., Singh, S., Lo, S., Sharma, P., Agarwal, S., & Garg, M. C. (2024) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2024.105361 · Cited by 68 · see citing works
Self-citation
Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass
Kumari, S., Chowdhry, J., Sharma, P., Agarwal, S., & Garg, M. C. (2023) · Chemosphere · doi:10.1016/j.chemosphere.2023.140262 · Cited by 64 · see citing works
Self-citation
Modeling of thermo-chemical pretreatment of yam peel substrate for biogas energy production: RSM, ANN, and ANFIS comparative approach
Onu, C. E., Nweke, C. N., & Nwabanne, J. T. (2022) · Applied Surface Science Advances · doi:10.1016/j.apsadv.2022.100299 · Cited by 63 · see citing works
Ultrasonic assisted graphene oxide nanosheet for the removal of phenol containing solution
Mukherjee, M., Goswami, S., Banerjee, P., Sengupta, S., Das, P., Banerjee, P. K., & Datta, S. (2017) · Environmental Technology & Innovation · doi:10.1016/j.eti.2016.11.006 · Cited by 61 · see citing works
Innovative Strategies for Dye Removal from Textile Wastewater: A Comprehensive Review of Treatment Approaches and Challenges
Kumari, S., Singh, R. K., Jahangeer, J., & Garg, M. C. (2024) · Water Air & Soil Pollution · doi:10.1007/s11270-024-07532-4 · Cited by 57 · see citing works
Self-citation
Artificial neural network simulation of combined humic substance coagulation and membrane filtration
Al-Abri, M. Z., & Hilal, N. (2007) · Chemical Engineering Journal · doi:10.1016/j.cej.2007.10.005 · Cited by 56 · see citing works
Process Optimization and Modeling of Methylene Blue Adsorption Using Zero-Valent Iron Nanoparticles Synthesized from Sweet Lime Pulp
Sahu, N., Rawat, S., Singh, J., Karri, R. R., Lee, S., Choi, J.-S., & Koduru, J. R. (2019) · Applied Sciences · doi:10.3390/app9235112 · Cited by 54 · see citing works
Treatment of malachite green dye containing solution using bio-degradable Sodium alginate/NaOH treated activated sugarcane baggsse charcoal beads: Batch, optimization using response surface methodology and continuous fixed bed column study
Das, L., Das, P., Bhowal, A., & Bhattachariee, C. (2020) · Journal of Environmental Management · doi:10.1016/j.jenvman.2020.111272 · Cited by 54 · see citing works
Machine learning (ML): An emerging tool to access the production and application of biochar in the treatment of contaminated water and wastewater
Kumari, S., Chowdhry, J., Kumar, M., & Garg, M. C. (2024) · Groundwater for Sustainable Development · doi:10.1016/j.gsd.2024.101243 · Cited by 52 · see citing works
Self-citation
Response surface modeling and optimization of major medium variables for glycolipopeptide production
Ekpenyong, M., Antai, S. P., Asitok, A., & Ekpo, B. O. (2017) · Biocatalysis and Agricultural Biotechnology · doi:10.1016/j.bcab.2017.02.015 · Cited by 51 · see citing works
Machine learning approaches for the treatment of textile wastewater using sugarcane bagasse (Saccharum officinarum) biochar
Kumari, S., Chowdhry, J., Choudhury, A., Agarwal, S., Narad, P., & Garg, M. C. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-31826-z · Cited by 48 · see citing works
Self-citation
A Biodegradable Magnetic Nanocomposite as a Superabsorbent for the Simultaneous Removal of Selected Fluoroquinolones from Environmental Water Matrices: Isotherm, Kinetics, Thermodynamic Studies and Cost Analysis
Mashile, G. P., Dimpe, K. M., & Nomngongo, P. N. (2020) · Polymers · doi:10.3390/polym12051102 · Cited by 46 · see citing works
Adsorption of methylene blue from textile industrial wastewater using activated carbon developed from H3PO4-activated khat stem waste
Takele, T., Angassa, K., Abewaa, M., Mengistu, A., & Tessema, I. (2023) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-023-05245-y · Cited by 44 · see citing works
Conversion of locally available materials to biochar and activated carbon for drinking water treatment
Nidheesh, P. V., Kumar, M., Venkateshwaran, G., Ambika, S., Bhaskar, S., Vinay, V., & Ghosh, P. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.141566 · Cited by 43 · see citing works
Self-citation
Application of RSM for Bioremoval of Methylene Blue Dye from Industrial Wastewater onto Sustainable Walnut Shell (Juglans regia) Biomass
Kumari, S., Rajput, V. D., Minkina, T., Rajput, P., Sharma, P., Verma, A. K., Agarwal, S., & Garg, M. C. (2022) · Water · doi:10.3390/w14223651 · Cited by 42 · see citing works
Self-citation
Adsorptive removal of crystal violet from aqueous solution by bioadsorbent
Radoor, S., Jayakumar, A., Shivanna, J. M., Karayil, J., Kim, J. T., & Siengchin, S. (2023) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-023-05079-8 · Cited by 36 · see citing works
Adaptive MLP neural network controller for consensus tracking of Multi-Agent systems with application to synchronous generators
Sharifi, A., Sharafian, A., & Ai, Q. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115460 · Cited by 36 · see citing works
Adsorptive Removal of Cd, Cu, Ni and Mn from Environmental Samples Using Fe3O4-Zro2@APS Nanocomposite: Kinetic and Equilibrium Isotherm Studies
Gugushe, A. S., Mpupa, A., Munonde, T. S., Nyaba, L., & Nomngongo, P. N. (2021) · Molecules · doi:10.3390/molecules26113209 · Cited by 31 · see citing works
Enhancing rice straw compost with an amino acid-derived ionic liquid as additive
Ma, H., Beadham, I., Ruan, W., Zhang, C., & Deng, Y. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.126387 · Cited by 31 · see citing works
Magnetic Mesoporous Carbon/β-Cyclodextrin–Chitosan Nanocomposite for Extraction and Preconcentration of Multi-Class Emerging Contaminant Residues in Environmental Samples
Mashile, G. P., Mpupa, A., & Nomngongo, P. N. (2021) · Nanomaterials · doi:10.3390/nano11020540 · Cited by 30 · see citing works
Comparison of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Models in Simulating Polygalacturonase Production
Uzuner, S., & Çekmecelioğlu, D. (2016) · BioResources · doi:10.15376/biores.11.4.8676-8685 · Cited by 30 · see citing works
Adsorption of methylene blue dye on feather keratin/silk fibroin porous aerogels
Xing, X., Zhang, X., Feng, Y., & Yang, X. (2023) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2023.105298 · Cited by 29 · see citing works
Integral approach of treatment of phenolic wastewater using nano-metal coated graphene oxide in combination with advanced oxidation
Ganguly, P., Sarkhel, R., Bhattacharya, S., Das, P., Saha, A., & Bhowal, A. (2020) · Surfaces and Interfaces · doi:10.1016/j.surfin.2020.100660 · Cited by 28 · see citing works
Artificial Neuro-Fuzzy Inference System (ANFIS) based validation of laccase production using RSM model
Kumar, M., Singh, G., Arya, S. K., Bhatti, J. S., & Sharma, P. (2018) · Biocatalysis and Agricultural Biotechnology · doi:10.1016/j.bcab.2018.03.016 · Cited by 28 · see citing works
Self-citation
Research hotspot and trend analysis of heavy metals decontamination of wastewater in past two decades: A bibliometric analysis
Kumari, S., Kumar, M., Agrawal, S., Kaushik, A., Hashem, A., Allah, E. F. A., Kumar, A., & Garg, M. C. (2024) · Journal of Hazardous Materials Advances · doi:10.1016/j.hazadv.2024.100532 · Cited by 27 · see citing works
Self-citation
Physical Treatment for Biochar Modification: Opportunities, Limitations and Advantages
Anerao, P., Salwatkar, G., Kumar, M., Pandey, A., & Singh, L. (2022) · Springer eBooks · doi:10.1007/978-981-19-2488-0_4 · Cited by 24 · see citing works
Self-citation
Modelling and optimization of crude oil removal from surface water via organic acid functionalized biomass using machine learning approach
Asadu, C. O., Ekwueme, B. N., Onu, C. E., Onah, T. O., Ike, I. S., & Ezema, C. A. (2022) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2022.104025 · Cited by 22 · see citing works
Kinetic Model for Simultaneous Adsorption/Photodegradation Process of Alizarin Red S in Water Solution by Nano-TiO2 under Visible Light
Giovannetti, R., Rommozzi, E., D’Amato, C. A., & Zannotti, M. (2016) · Catalysts · doi:10.3390/catal6060084 · Cited by 21 · see citing works
Paddy straw as a biomass feedstock for the manufacturing of bioethanol using acid hydrolysis and parametric optimization through response surface methodology and an artificial neural network
Jayakumar, M., Thiyagar, T., Abo, L. D., Arumugasamy, S. K., & Jabesa, A. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-024-05371-1 · Cited by 21 · see citing works
Performance prediction of ion-engineered water injection (EWI) in chalk reservoirs using Response Surface Methodology (RSM)
Mosallanezhad, A., & Kalantariasl, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.05.012 · Cited by 20 · see citing works
Urban wood waste as precursor of activated carbon and its subsequent application for adsorption of polyaromatic hydrocarbons
Barman, S. R., Banerjee, P., Das, P., & Mukhopadhayay, A. (2018) · International Journal of Energy and Water Resources · doi:10.1007/s42108-018-0001-4 · Cited by 17 · see citing works
Artificial intelligence techniques
Shafiullah, M., Abido, M. A., & Al-Mohammed, A. H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-88429-7.00007-2 · Cited by 16 · see citing works
Predicting the adsorption capacity of organic dye using zirconium-based metal–organic framework: a comparative analysis of RSM and ANN-based models
Dahlan, I., Obi, C. C., & Poopathi, V. (2023) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-023-05012-z · Cited by 15 · see citing works
Enhanced adsorptive removal of malachite green in environmental samples using Java plum leaves: from equilibrium to mechanism studies
Gul, S., Gul, S., Gul, H., Raza, N., Azzouz, A., Elamin, M. R., & Khezami, L. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-024-05567-5 · Cited by 13 · see citing works
Enhanced adsorptive removal of methylene blue using ternary nanometal oxides in an aqueous solution
Pourramezan, E., Omidvar, M., Motavalizadehkakhky, A., Zhiani, R., & Darzi, H. H. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-023-05225-2 · Cited by 8 · see citing works
Artificial neural network and mathematical approach for estimation of surface water quality parameters (case study: California, USA)
Shahid, E. S., Salari, M., Rastegar, M., Sheibani, S. N., & Ehteshami, M. (2021) · Desalination and Water Treatment · doi:10.5004/dwt.2021.26709 · Cited by 8 · see citing works
Removal of Emerging Contaminants and Micropollutants from the Aquatic Environment Using Biochar
Nidheesh, P. V., Kumar, M., Karim, A. V., & Venu, D. (2024) · CRC Press eBooks · doi:10.1201/9781003344803-19 · Cited by 7 · see citing works
Self-citation
Modelling and optimization of phlorotannin extraction from Stoechospermum marginatum using response surface methodology and evaluating its activity against marine microfouling bacteria
Ashokan, A., Sanath Kumar, H., Kumar, K., Deshmukhe, G., Deshmukh, R. R., Kumar, M. S., & Shukla, S. P. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-024-05596-0 · Cited by 5 · see citing works
Self-citation
Methods of Biochar Production, Its Modifications, and Characterization
Kumar, M., Sawarkar, A. D., & Anerao, P. (2024) · CRC Press eBooks · doi:10.1201/9781003344803-2 · Cited by 3 · see citing works
Self-citation

Flagged first, then by citations

Research works printed but not linked: 3

[13]Kuang, Y., Zhang, X. & Zhou, S. Adsorption of methylene blue in water onto activated carbon by surfactant modification. Water (Basel). 12, 587 (2020).
[14]Alam, M. Z., Bari, M. N. & Kawsari, S. Statistical optimization of Methylene Blue dye removal from a synthetic textile wastewater using indigenous adsorbents. Environ. Sustain. Indic. 14, 100176 (2022).
[57]Ndilimeke Akawa, M. & Mogolodi Dimpe, K. Nosizo Nomngongo, P. An adsorbent composed of alginate, polyvinylpyrrolidone and activated carbon (AC@PVP@alginate) for ultrasound-assisted dispersive micro-solid phase extraction of nevirapine and zidovudine in environmental water samples. Environ. Nanotechnol. Monit. Manag. 16, 100559 (2021).

Sorted by citations

Sustainable wastewater treatment: Mechanistic, environmental, and economic insights into biochar for synthetic dye removal
Mnyango, J. I., Nyoni, B., Phiri, C., Fouda-Mbanga, B. G., Amusat, S. O., Maringa, A., Yalala-Ndlovu, B., Hlabano-Moyo, B. M., Tywabi-Ngeva, Z., & Hlangothi, S. P. (2025) · Next Materials · doi:10.1016/j.nxmate.2025.100974 · Cited by 42 · see citing works
Optimization and comparative modelling of RSM and ANN for the adsorptive removal of Remazol Brilliant Blue R dye using spent coffee ground biochar
Hidayat, E., Hamid, N. A. H., Sarbani, N. M. M., Samitsu, S., Aoyagi, M., Harada, H., & Safari, M. A. M. (2025) · Chemosphere · doi:10.1016/j.chemosphere.2025.144709 · Cited by 17 · see citing works
Charge-selective magnetic bionanosorbents for multi-dye wastewater treatment: performance, regeneration and ecotoxicity assessment
Soares, S. F., Pereira, B., Santos, J., Trindade, T., Barreto, Â., Maria, V. L., & Daniel‐da‐Silva, A. L. (2025) · Separation and Purification Technology · doi:10.1016/j.seppur.2025.134636 · Cited by 14 · see citing works
Sustainable optimization of mechanical properties in fiber-reinforced polymer composites using RSM and ANN: A systematic review
Shettar, M., Bhat, A., Katagi, N. N., Gowrishankar, M. C., & Somdee, P. (2026) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2026.02.087 · Cited by 12 · see citing works
Leveraging machine learning for microplastic detection, global distribution analysis, and management
Kumari, S., Patel, P. K., Kumar, M., & Cheng, H. Y. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.141200 · Cited by 10 · see citing works
Self-citation
Transforming Agro-Waste to Sustainable Biochar for Removal of Nitrate from Wastewater Using Response Surface Methodology
Chowdhry, J., Kumari, S., & Nandal, M. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-025-00870-z · Cited by 10 · see citing works
Self-citation
Machine learning modelling and mechanistic insights into malachite green adsorption on sustainable magnetic activated carbon
Sudarsan, S., Murugesan, G., Varadavenkatesan, T., Vinayagam, R., & Selvaraj, R. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.109327 · Cited by 9 · see citing works
Prediction of methylene blue dye sorption to sulfonated date palm kernel biochar using statistical regression and machine learning methods and DFT studies
Aigbe, U. O., Ukhurebor, K. E., Onyancha, R. B., Osibote, O. A., Hassaan, M. A., Elkatory, M. R., & Nemr, A. E. (2025) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2025.127547 · Cited by 8 · see citing works
Development of low-cost adsorbents from coconut shell for energy-efficient dye removal from laboratory effluent discharge
Abubakar, A. M., Nazar, N., Yusuf, A. A., Idama, E. A., Arowo, M. N., Ma’aji, A. M., & Zakarya, I. A. (2025) · Measurement Energy · doi:10.1016/j.meaene.2025.100057 · Cited by 8 · see citing works
Enhanced removal of low-concentrations of PFOA from water and wastewater with a novel luffa sponge-derived adsorbent: one-step synthesis and optimization using design of experiments and artificial neural network
Coutinho, R., Hoshima, H. Y., & Marques, M. (2025) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2025.108208 · Cited by 7 · see citing works
Nonlinear impact of artificial intelligence on clean energy transformation: The asymmetric role of heterogeneous environment regulations
Huang, X., Tang, J., Chen, Y., & Qiao, S. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.127457 · Cited by 6 · see citing works
Face centered central composite design-Response surface methodology (FCC-RSM) a multivariate optimization of crystal violet dye adsorption by Cajanus cajan residues with thermodynamic, kinetic and mechanistic insights
Brahma, D., Brahma, S., & Kumar, Y. (2025) · Next Materials · doi:10.1016/j.nxmate.2025.101476 · Cited by 6 · see citing works
A comprehensive review on green materials as adsorbents for the remediation of heavy metals, dyes, antibiotics, pesticides, and microplastics from water and wastewater: an overview
Kumar, A. (2026) · Chemical Product and Process Modeling · doi:10.1515/cppm-2025-0217 · Cited by 6 · see citing works
Self-citation
Machine learning in industrial wastewater treatment: Critical insights into modelling approaches and digital futures
Suzaimi, N. D., Abuhabib, A. A., Darwish, M. S. J., Alhadi, A. C., Mohammad, A. W., & Hamzah, S. (2026) · Journal of Industrial and Engineering Chemistry · doi:10.1016/j.jiec.2025.12.035 · Cited by 6 · see citing works
The role of green chemistry in the transformation of agro-industrial wastes for health and environment: a review
Garg, M. C., Kumari, S., Nguyen, M.-K., Daudu, B. O., Thapa, S., Gupta, J., Kumar, M., Altuner, E. E., Yadav, A. K., Anaiye, E. B., et al. (2026) · Biodegradation · doi:10.1007/s10532-026-10304-y · Cited by 6 · see citing works
Self-citation
From conventional treatment to light-driven remediation: a review of strategies for treatment of dye-contaminated wastewater
Aayushi, & Pandey, S. (2026) · Frontiers in Environmental Engineering · doi:10.3389/fenve.2026.1847347 · Cited by 6 · see citing works
Mechanistic insights into methylene blue biodegradation by Tetradesmus obliquus: a multimodal approach using absorption, fluorescence, and square wave voltammetry
Talha, M. A., Agwa, H. E., Beltagi, A. M., El-Mohsnawy, E., & Ali, A. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-19675-3 · Cited by 5 · see citing works
Isotherm, kinetic, and thermodynamic insights into textile effluent remediation using acid-treated sugarcane peel
Abouzied, A. S., Kola, O. E., Al‐Ahmary, K. M., AlMohamadi, H., AL-Mhyawi, S. R., Alqahtani, Z., Alsharif, A., & Al-Otaibi, J. S. (2026) · Scientific Reports · doi:10.1038/s41598-026-39059-5 · Cited by 5 · see citing works
Dye removal from synthetic wastewater by Eichhornia crassipes : a phytoremediation approach for basic fuchsin and coomassie brilliant blue R-250
Abbas, Z. R., & Kareem, S. L. (2025) · Bioremediation Journal · doi:10.1080/10889868.2025.2585871 · Cited by 5 · see citing works
Valorization of Caryocar brasiliense Byproducts: Microwave-Assisted Extraction of Phenolics and Material Characterization for Environmental and Bioenergy Applications
da Silva, M. D., Martins, E. A., Moreira, R. P. L., Rosa, A. P., & Borges, A. C. (2025) · ACS Omega · doi:10.1021/acsomega.5c09005 · Cited by 4 · see citing works
Modeling studies of the adsorption of Methyl Red and Acid Yellow 36 dyes by sulphonated Ulva lactuca carbon
Hassaan, M. A., Yılmaz, M., Sikaily, A. E., Shoaib, A. G. M., El-Nemr, M. A., & Nemr, A. E. (2025) · Scientific Reports · doi:10.1038/s41598-025-29983-3 · Cited by 4 · see citing works
Analysis of Response Surface and Artificial Neural Network for Cr(Ⅵ) Removal Column Experiment
Ren, Z., Li, Z., Tang, H., Yang, L., Zhu, J., & Jing, Q. (2025) · Water · doi:10.3390/w17081211 · Cited by 4 · see citing works
Xyloglucan films from tamarind kernels reinforced with chemically modified cellulose nanospheres
Sravanthi, G. B., Thakur, R. S., Giri, S., Dissanayake, T. U., Janaswamy, S., Mekonnen, T. H., Bandara, N., Pal, K., & Sarkar, P. (2025) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2025.148411 · Cited by 4 · see citing works
Design of porous activated carbon beads: Controlling size and structure for optimal dye adsorption
Waisi, B. I., Al‐Musawy, W. K., Ahmed, S. B., & Al-Furaiji, M. H. (2025) · Desalination and Water Treatment · doi:10.1016/j.dwt.2025.101533 · Cited by 4 · see citing works
A drift-aware RS2FS pipeline with confidence gating for IDS
Sakthivel, G., & kumarasamy, A. K. (2026) · Scientific Reports · doi:10.1038/s41598-025-34124-x · Cited by 4 · see citing works
Integrative chemometric and intelligent modeling approaches for pollutant adsorption: Synergistic insights from experimental design, artificial intelligence (AI), and DFT applied to bisphenol A, β-naphthol, and eriochrome black
Bouzid, T., Grich, A., Naboulsi, A., Yazid, H., Elbasraoui, A., Regti, A., Himri, M. E., & Haddad, M. E. (2026) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2026.105639 · Cited by 4 · see citing works
Comparative analysis of ANFIS, ANN, and BBD for enhanced prediction of methyl orange adsorption in water treatment
Bbumba, S., & Karume, I. (2026) · Scientific Reports · doi:10.1038/s41598-026-43445-4 · Cited by 4 · see citing works
Insights into iron-doped keratin derived via alkali hydrolysis for nitrate adsorption: Experimental and computational modeling
Sen, R., Sen, K., & Mondal, N. K. (2025) · Biochemical Engineering Journal · doi:10.1016/j.bej.2025.109997 · Cited by 3 · see citing works
Advanced machine-learning frameworks for predicting malachite green adsorption onto superparamagnetic-activated carbon derived from Spathodea campanulata flowers
Sudarsan, S., Vinayagam, R., & Selvaraj, R. (2025) · South African Journal of Chemical Engineering · doi:10.1016/j.sajce.2025.11.017 · Cited by 3 · see citing works
AI-optimized arc-discharge synthesis: Tailoring silver nanoparticle size and shape for superior dye elimination
Aljohani, F. S., elhaj, A., Hany, M. A., El-Arwash, H. M., & El-Khatib, M. (2026) · Materials Chemistry and Physics · doi:10.1016/j.matchemphys.2026.132293 · Cited by 3 · see citing works
Plum pit waste biochar as a sustainable and efficient adsorbent targeting water contamination: an in-depth analysis of the impact of pyrolysis parameter changes on the adsorption mechanism
Zažímal, F., Lukáč, T., Bajnok, A., Vrána, L., Stýskalík, A., Tulipánová, A., Drdanová, A. P., Staňová, A. V., Mackuľak, T., Homola, T., et al. (2026) · Waste Disposal & Sustainable Energy · doi:10.1007/s42768-026-00286-1 · Cited by 3 · see citing works
Optimization and characterization of biochar yields from pyrolysis of coconut shell for sustainable waste-driven bioenergy transformation
Onokwai, A. O., Akuru, U. B., & Desai, D. A. (2025) · Matériaux & Techniques · doi:10.1051/mattech/2025010 · Cited by 3 · see citing works
Modeling of methylene blue adsorption on a superhydrophilic activated carbon-coated melamine sponge using response surface methodology
Ghavipanjeh, F., Sheikholeslami, Z., Yavari, M., Beygzadeh, M., & Ehteshami, M. (2025) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-025-06925-0 · Cited by 2 · see citing works
Response surface methodology optimization of COD removal from wastewater using peanut shell-derived activated carbons: a sustainable biosorption approach
Sunaina, Kumari, S., Khandurie, A., Sharma, C., Devi, B. S., & Garg, M. C. (2026) · Environmental Science Water Research & Technology · doi:10.1039/d5ew01291k · Cited by 2 · see citing works
Self-citation
Agricultural and food wastes derived cellulose hydrogels for water reservoirs and dye sorption
Gayen, T. K., Ali, M. A., & Warkar, S. G. (2026) · Polymer Bulletin · doi:10.1007/s00289-026-06386-6 · Cited by 2 · see citing works
Sustainable methylene blue dye removal via bio-derived micro/micron-sized porous particles Zygophyllum coccineum and Calotropis procera: A machine learning-assisted study
Fakry, H., Salama, E., Taha, A., Ossman, M., Bonanomi, G., Abd‐ElGawad, A. M., & El-Amier, Y. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-42218-3 · Cited by 2 · see citing works
Digestibility, microbiome dynamics, and biogas generation in anaerobic digestion with integrated additives and artificial intelligence
Ullah, W., Alsaiari, M., Jalalah, M., Harraz, F. A., Thakur, N., Zidan, N. S., Sakran, M., Eraky, M. T., & Salama, E. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122640 · Cited by 2 · see citing works
Sustainable Approach for Eradicating Methylene Blue from Simulated Wastewater by Cost‐Effective and Eco‐Friendly Agricultural Waste
Naskar, A., Chakraborty, S., Dutta, S., Koley, A., Roy, R., Chabri, S., Mondal, S., Das, R., Jana, S., Adhikari, A., et al. (2025) · ChemistrySelect · doi:10.1002/slct.202501628 · Cited by 1 · see citing works
Green Remediation of Heavy Metals Using Chitosan-Carbon Aerogels Derived from Agricultural By-Products
Le, K. A., Nguyen, H. T. A., Nga, H. N., & Le, P. K. (2026) · Water Air & Soil Pollution · doi:10.1007/s11270-025-09057-w · Cited by 1 · see citing works
Optimized valorization of green SiO2-rich sugarcane bagasse white ash for effective pollutant adsorptive removal in sustainable wastewater remediation
Aziz, N. A., Sheng, C. K., & Aziz, M. F. A. (2026) · South African Journal of Chemical Engineering · doi:10.1016/j.sajce.2026.100859 · Cited by 1 · see citing works
Response surface optimization of hexavalent chromium removal from real tannery wastewater using carbonized eggshells
Gebretsadkan, B. G., Hagos, D. W., Weldehans, M. G., Teweldebrhan, B. E., & Zegeye, M. G. (2026) · Discover Sustainability · doi:10.1007/s43621-026-03042-x · Cited by 1 · see citing works
Melamine-Derived MgO/AC Nanocomposite for Efficient Dye Removal: Integrating RSM and Machine Learning Modeling
Nazari, H., Salimi, F., & Karami, C. (2026) · Langmuir · doi:10.1021/acs.langmuir.5c05181 · Cited by 1 · see citing works
Impact of Climate Change on Potable Water Quality and its Remediation Using Artificial Intelligence: A Review
Singh, M., Pathak, S., & Hussain, A. (2026) · Water Resources Management · doi:10.1007/s11269-026-04672-6 · Cited by 1 · see citing works
Comparative assessment of artificial neural network and response surface methodology for modeling and optimizing phosphate adsorption onto rice straw biochar
Kumari, S., Agarwal, S., Chowdhry, J., Sharma, P., Kumar, M., Kumar, A. T. R., Nayak, M., & Garg, M. C. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.130484 · Cited by 1 · see citing works
Self-citation
Evaluation of Erodium moschatum (L.) L’Hér biomass as an eco-friendly adsorbent for methylene blue removal: Isotherm, kinetic, and thermodynamic investigations
Adiguzel, A., Ersin, T., & Işık, B. (2026) · Process Biochemistry · doi:10.1016/j.procbio.2026.07.013 · Cited by 1 · see citing works
Surface engineering of chitosan nanoparticles via polyamidoxime grafting and Response Surface Methodology optimization for hexavalent chromium remediation
Taimur, S., Shahid, A., Anwar, S., Leghari, S. A. K., Nasir, A., Anjum, F., Noreen, F., & Yasin, T. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.153825 · Cited by 1 · see citing works
Fe-modified rice straw biochar for efficient nitrate removal: advancing sustainable water treatment technologies
Chowdhry, J., Kumari, S., Patel, P. K., Garg, M. C., & Nandal, M. (2026) · Chemical Papers · doi:10.1007/s11696-026-05427-8 · Cited by 1 · see citing works
Self-citation
Sustainable sequestration of Pb²⁺ ions from electroplating wastewater using OSB@ZnO nanocomposites: Statistical modelling and phytotoxicity evaluation
Kumari, A., Mittal, R., Yadav, M., & Bhateria, R. (2026) · Next Sustainability · doi:10.1016/j.nxsust.2026.100551 · Cited by 1 · see citing works
Rhodamine B Extraction by Emulsified Liquid Membrane: Innovative Approach Utilizing Gaussian Process Regression for Enhanced Efficiency and Sustainability
Mechati, S., Zamouche, M., Tahraoui, H., Laggoun, Z., Chemchmi, R., Boudadi, Y., Bourzerzour, A. E. K., Assadi, A. A., Elfellah, W., Khezami, L., et al. (2026) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-026-11209-x · Cited by 1 · see citing works
Sustainable Approaches to Dye Degradation: A Review of Eco-Sustainable Techniques, Recent Strategies, and Emerging Solutions
Kumar, P. S., Singh, K., Singh, J., Talla, M. K., Ali, S., Chauhan, R., & Ranjan, A. (2026) · Journal of Environmental Engineering · doi:10.1061/joeedu.eeeng-8462 · Cited by 1 · see citing works
AI-Guided Biosorption of Lead Using Prosopis cineraria Leaf Powder in an Omani Industrial Wastewater Matrix: Kinetics, Thermodynamic Trends, and Predictive Modeling
Namdeti, R., Rao, G. B., Lakkimsetty, N. R., Babu, N. R., Doddamani, D., Qahoor, N. M. S., Amri, L. A. A., Al-Kathiri, D. S. M. S., Meka, U. R., & Prema., P. M. (2026) · Environmental Engineering Science · doi:10.1177/15579018261486699 · Cited by 1 · see citing works
GPT-based lifelong learning and ANFIS-driven reply memory ratio prediction for aspect-based sentiment analysis
Huang, H., Ye, X., Mustafa, M. B., Dong, Q., Li, Y., Asemi, A., & Asemi, A. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02086-2
Fabrication of a Parali (Rice Straws) Biomass & Herbal Based Filtration System for Sustainable Rural Water PurificationPreprint
Singh, K. K., Prakash, D., & Saini, M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.13.682254
Deep Fuzzy-NN modeling for the prediction of Zn(II) adsorption in columns using alkaline modified biochar: Integrated experimental and computational insights
Kamala, B., & Latha, B. (2025) · Environmental Research · doi:10.1016/j.envres.2025.123329
Optimization of adsorption capacity for CO2/N2 mixture via RSM and machine learning
Khan, M. F. A. M., & Ghaemi, A. (2025) · Chemical Engineering Journal Advances · doi:10.1016/j.ceja.2025.100957
Upcycling Citrus limetta peel into a high‑efficiency adsorbent for methylene blue removal: a waste-to-worth approach
Khan, S., Alishba, & Nasar, A. (2025) · International Journal of Phytoremediation · doi:10.1080/15226514.2025.2599422
Treatment of Surface Water Using Chrysopogon zizanioides With Water Quality Monitoring and Classification by Machine Learning Models
Latha, A., Kavitha, P., Ganesan, R., Revathee, T., & Ahamed, R. M. (2026) · Environmental Quality Management · doi:10.1002/tqem.70284
Application of machine learning tools for prediction of fertilizers/pesticides/dye adsorption onto agricultural waste-derived biochar/modified biochar for clean environment
Catalina, C., Sirbu, E. E., Popa, C. R., Bondarev, A., & Tănase, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40447-4.00004-9
Integrating experimental and predictive modeling approaches for phenolic pollutants removal using bisphenol-based hyper-cross-linked resin
Firmansyah, M. L., Abdel‐Azeim, S., Ashraf, M., & Ullah, N. (2026) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2026.129422
Biosorption of Procion Magenta and Black Azabache textile dyes using banana and carrot peel waste: a comparative analysis of pure and blended systems
Patil, S. B., & Singh, R. (2026) · International Journal of Phytoremediation · doi:10.1080/15226514.2026.2660151
Recent Advances in Nutrient Removal from Wastewater Using Constructed Wetlands with Biochar
Patel, P. K., & Kumari, S. (2026) · Springer eBooks · doi:10.1007/978-981-95-6966-3_10
Self-citation
Response surface optimization of fluoride sequestration using L-lysine stabilized mesoporous silica xerogel
Srivastava, A., & Srivastava, A. (2026) · Nano-Structures & Nano-Objects · doi:10.1016/j.nanoso.2026.101685
Biomass-Derived Activated Carbon for Congo Red Dye Adsorption: Machine-Learning-Based Prediction and Comparative Evaluation
Sudarsan, S., Vinayagam, R., & Selvaraj, R. (2026) · ACS Omega · doi:10.1021/acsomega.6c02488
Recent insights into chitosan-derived adsorbents for efficient treatment of dye-polluted wastewater: a review, bibliometric analysis, challenges and future prospects
Anuar, S. Z. K., Nordin, A. H., Husna, S. M. N., Azami, M. S. M., Nordin, M. L., Mohamed, M., Ahmad, Z., & Ismail, W. Z. W. (2026) · International Journal of Phytoremediation · doi:10.1080/15226514.2026.2680209
Sustainable Bioremediation of Benzo[a]pyrene Using a Trichoderma Graphene Oxide Bio-Composite in a Hybrid Packed Bed System
Hamad, M. T. M. H. (2026) · Journal of Polymers and the Environment · doi:10.1007/s10924-026-03878-4
Removal of phosphate by green zeolite - peanut shell biochar: Performance, mechanism
Ling, X., Li, F., Qi, X., Guo, H., Liu, L., Xin, Y., & Zhang, S. (2026) · Arabian Journal of Chemistry · doi:10.25259/ajc_1487_2025
Microwave-activated spent biomass, Saccharomyces cerevisiae for efficient methylene blue removal: mechanistic insights and regeneration potential
Kamaraju, R., Venkatesan, B., Pasupathi, S., Asaithambi, K., Karuppiah, S., & Mathivanan, M. (2026) · Chemistry and Ecology · doi:10.1080/02757540.2026.2691153
Hyper-branched dendrimer-modified magnetic nanoparticles for removal of an anionic dye from water samples: Response surface methodology and artificial neural network modeling
Shandaki, A. M., Miralinaghi, M., Moniri, E., Shahsavari, S., & Shabani, M. (2026) · Journal of Hazardous Materials Advances · doi:10.1016/j.hazadv.2026.101379
Enhanced removal of Cu(II) and Ni(II) from binary systems by Ca-modified low-temperature pine bark biochar
Im, E.-C., Lee, J.-I., Park, S.-J., & Lee, C. (2026) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2026.109741
Optimization of microwave-aided biodiesel production from waste cooking oil using functionalized catalyst derived from bio-waste: experimental investigations, taguchi modelling, and machine learning
Edhor, P. O., Akhabue, C. E., Adama, K. K., Esiekpe, L. E., Ukhurebor, K. E., Onyancha, R. B., Aigbe, U. O., Shola, N. T., & Anakpoha, O. C. (2026) · Next Materials · doi:10.1016/j.nxmate.2026.102887
Advanced ternary core–shell g-C3N4@PANi/PPy for high adsorption of diclofenac sodium from wastewater: mechanism study and ANN modelling
Omari, S. E., Maitouf, S., Carlier, L., Lefebvre, M., Montrelay, N., Chkir, M., Albourine, A., Laabd, M., & Benhabib, K. (2026) · Chemical Engineering Science · doi:10.1016/j.ces.2026.124894
Automated machine learning approach for accurate prediction of adsorptive removal of dye in a coconut fiber packed column system
Jha, S., Shukla, V., Shahabuddin, S., Nayak, A., Bisht, S., Tyagi, I., Suhas, S., Mohsin, M. E. A., Mousa, S., & Gaur, R. (2026) · Scientific Reports · doi:10.1038/s41598-026-68308-w
BioMCM-41 mesoporous silica from agricultural waste for methylene blue removal and ANN-based prediction
Khelif, M., Bouchenafa-Saib, N., Ibrir, A., Chennouf, Z., Boumessaidia, S., & Gaigneaux, É. M. (2026) · Next Materials · doi:10.1016/j.nxmate.2026.103346
Optimization of methylene blue adsorption using agricultural biomass waste: a comparative study of batch performance via response surface methodology and artificial neural network
Alardhi, S. M., Abdul-Azeez, H. M., Breig, S. J. M., & Majdi, H. S. (2026) · Water Practice & Technology · doi:10.2166/wpt.2026.377
Integrated Machine Learning and Statistical Interpretation of Malachite Green Removal by SDBS-Modified Ostrich Bone Waste: A Comparative Random Forest, Bayesian Regression, and LMG Analysis
Bahrami, M., Amiri, M. J., Chargi, Z. R., Lednicka, B., & Gheibi, M. (2026) · Molecules · doi:10.3390/molecules31193484
Optimization and Machine Learning-Assisted Photocatalytic Degradation of Methylene Blue Using Fe–Zn Nanoparticles
Banisheikholeslami, A., Qaderi, F., Moghadam, M. T., & Kebria, M. R. S. (2026) · Iranian Journal of Science and Technology Transactions of Civil Engineering · doi:10.1007/s40996-026-02276-z
Comparative analysis of RSM and ANFIS in predicting combustion and vibration characteristics of a diesel engine fuelled with WCO biodiesel-methanol-diesel ternary blends
Murugan, M. A. (2026) · Next Energy · doi:10.1016/j.nxener.2026.101081
Valorization of Rice Husk into Efficient Adsorbents for Dye Removal: A Comparative RSM–CCD Optimization Approach
Pal, P., Lynch, M., Harris, P. W., Nahar, K., & Burey, P. P. (2026) · Clean Technologies · doi:10.3390/cleantechnol8050171
A hybrid RSM-ANN-NSGA-II framework for optimizing plant-mediated green synthesis of silver nanoparticles from coriander stem extract
Sonia, & Yadav, M. (2026) · Chemical Product and Process Modeling · doi:10.1515/cppm-2026-0180

Machine learning and modelling approach for removing methylene blue from aqueous solutions: Optimization, kinetics and thermodynamics studies
Kumari, S., Singh, S., Lo, S., Sharma, P., Agarwal, S., & Garg, M. C. (2024) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2024.105361 · Cited by 68 · see citing works
Self-citation
Adsorptive Removal of Methylene Blue (MB) from Wastewater: A Comparative Modeling Study Utilizing RSM, ANN, and ANFIS Model
Igberase, E., & Mkhize, I. G. (2025) · Engineering chemistry · doi:10.4028/p-z9rtbb · Cited by 1 · see citing works
Removal of aqueous methylene blue dye over Vallisneria Natans biosorbent using artificial neural network and statistical response surface methodology analysis
Musa, M. A., Chowdhury, S., Biswas, S., Alam, S., Parvin, S., & Sattar, M. A. (2023) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2023.123624 · Cited by 29 · see citing works
Insights into predictive modeling, isotherms and kinetic studies in the removal of methylene blue from water using pineapple Peel activated carbon
Bbumba, S., Karume, I., Talibawo, J., Ntale, M., Kaddu, G., Yikii, C. L., & Kigozi, M. (2026) · Discover Water · doi:10.1007/s43832-026-00357-8 · Cited by 3 · see citing works
Novel use of artificial neural networks in predicting kinetic and equilibrium parameters for methylene blue adsorption on tomato stem
Koyuncu, H., Kul, A. R., & Akyavaşoğlu, Ö. (2026) · Separation Science and Technology · doi:10.1080/01496395.2026.2664648

Authors: Kumari, S., Agarwal, S., Kumar, M., Sharma, P., Kumar, A., Hashem, A., Alotaibi, N. H., Abd-Allah, E. F., & Garg, M. C.
Affiliations: Amity University (IN)
Motilal Nehru National Institute of Technology (IN)
Indian Institute of Technology Roorkee (IN)
King Saud University (SA)
Corresponding author: Kumar, Manish
Garg, Manoj Chandra
ORCID: Kumari, Sheetal
Agarwal, Smriti
Kumar, Manish
Sharma, Pinki
Kumar, Ajay
Hashem, Abeer
Alotaibi, Nouf H.
Garg, Manoj Chandra
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2025-01-23
Volume: 15
Issue: 1
Pages: 2979-2979
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-025-87274-3
PMC: PMC11757716
OpenAlex: W4406758065
Cited by: 78
References: 99
Citations by year: 2026: 50 · 2025: 28
FWCI: 19.82
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0 (tdm)
creativecommons.org/licenses/by-nc-nd/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Amity University
King Saud University (RSP2025R356)
Topics: Physical Sciences › Environmental Science › Water Science and Technology › Adsorption and biosorption for pollutant removal
Physical Sciences › Environmental Science › Water Science and Technology › Coagulation and Flocculation Studies
Physical Sciences › Engineering › Biomedical Engineering › Environmental remediation with nanomaterials
Keywords: methylene blue adsorption, dye removal, artificial neural networks, adaptive neuro-fuzzy inference system, response surface methodology, adsorption kinetics, pseudo-second-order kinetics, Langmuir isotherm, adsorption thermodynamics, adsorption optimization
MeSH terms: Adsorption, Coloring Agents, Hydrogen-Ion Concentration, Kinetics, Methylene Blue, Oryza, Water Pollutants, Chemical, Neural Networks, Computer, Fuzzy Logic, Spectroscopy, Fourier Transform Infrared, Water Purification, Biomass

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks