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Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed

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Abstract

Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Ridge Regression (KRR), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were employed to predict lead adsorption based on a dataset of 1012 adsorption experiments, comprising 422 TF-C groups from our experiments and 590 FB-C groups from literatures. The XGB model showed superior accuracy and predictive performance for adsorption, achieving R2 values for TF-C (0.992) and FB-C (0.981), respectively. Contrasting inferior results were observed in other models, including RF (0.962 and 0.961), GBR (0.987 and 0.975), SVR (0.839 and 0.763), KRR (0.817 and 0.881), and LGBM (0.975 and 0.868). Additionally, a hybrid dataset combining both biochars in Pb adsorption also indicated high accuracy (0.972) as obtained from XGB model. The investigation revealed that the influence of char characteristics and adsorption conditions on Pb adsorption differs between the two biochar. Specific char characteristics, particularly nitrogen content, significantly influence lead adsorption in both biochar. Interestingly, the influence of pyrolysis temperature (PT) on lead adsorption is found to be greater for TF-C than for FB-C. Consequently, careful consideration of PT is crucial when preparing TF-C biochar. These findings offer practical guidance for optimizing biochar preparation conditions during heavy metal removal from wastewater.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  • Abdullah N, Gohari RJ, Yusof N, Ismail AF, Juhana J, Lau WJ, Matsuura T (2016) Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution. Chem Eng J 289:28–37

    Article  CAS  Google Scholar 

  • Ahmad M, Rajapaksha AU, Lim JE, Zhang M, Bolan N, Mohan D, Vithanage M, Lee SS, Ok YS (2014) Biochar as a sorbent for contaminant management in soil and water: A review. Chemosphere 99:19–33

    Article  CAS  Google Scholar 

  • Alam MS, Gorman-Lewis D, Chen N, Flynn SL, Ok YS, Konhauser KO, Alessi DS (2018) Thermodynamic Analysis of Nickel(II) and Zinc(II) Adsorption to Biochar. Environ Sci Technol 52:6246–6255

    Article  CAS  Google Scholar 

  • Awual MR (2019) Mesoporous composite material for efficient lead(II) detection and removal from aqueous media. J Environ Chem Eng 7(3):103124

    Article  CAS  Google Scholar 

  • Baptista M L, Goebel K, Henriques EMP (2022) Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artif Intell https://doi.org/10.1016/j.artint.2022.103667

  • Bernardo M, Mendes S, Lapa N, Goncalves M, Mendes B, Pinto F, Lopes H, Fonseca I (2013) Removal of lead (Pb2+) from aqueous medium by using chars from co-pyrolysis. J Colloid Interface Sci 409:158–165

    Article  CAS  Google Scholar 

  • Cagnon B, Py X, Guillot A, Stoeckli F, Chambat G (2009) Contributions of hemicellulose, cellulose and lignin to the mass and the porous properties of chars and steam activated carbons from various lignocellulosic precursors. Bioresour Technol 100:292–298

    Article  CAS  Google Scholar 

  • Cawley GC, Talbot NL (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11:2079–2107

    Google Scholar 

  • Chen DZ, Shen WS, Wu SL, Chen CQ, Luo XB, Guo L (2016) Ion exchange induced removal of Pb(II) by MOF-derived magnetic inorganic sorbents. Nanoscale 8:7172–7179

    Article  CAS  Google Scholar 

  • Chen T, Zhang YX, Wang HT, Lu WJ, Zhou ZY, Zhang YC, Ren LL (2014) Influence of pyrolysis temperature on characteristics and heavy metal adsorptive performance of biochar derived from municipal sewage sludge. Bioresour Technol 164:47–54

    Article  CAS  Google Scholar 

  • Cui LM, Wang YG, Gao L, Hu LH, Yan LG, Wei Q, Du B (2015) EDTA functionalized magnetic graphene oxide for removal of Pb(II), Hg(II) and Cu(II) in water treatment: Adsorption mechanism and separation property. Chem Eng J 281:1–10

    Article  CAS  Google Scholar 

  • Cui XQ, Fang SY, Yao YQ, Li TQ, Ni QJ, Yang XE, He ZL (2016) Potential mechanisms of cadmium removal from aqueous solution by Canna indica derived biochar. Sci Total Environ 562:517–525

    Article  CAS  Google Scholar 

  • Deng JQ, Liu YQ, Liu SB, Zeng GM, Tan XF, Huang BY, Tang XJ, Wang SF, Hua Q, Yan ZL (2017) Competitive adsorption of Pb(II), Cd(II) and Cu(II) onto chitosan-pyromellitic dianhydride modified biochar. J Colloid Interface Sci 506:355–364

    Article  CAS  Google Scholar 

  • Dhyani V, Bhaskar T (2018) A comprehensive review on the pyrolysis of lignocellulosic biomass. Renew Energy 129:695–716

    Article  CAS  Google Scholar 

  • Dong L, Liu YH, Wen HZ, Zou C, Dai QQ, Zhang HJ, Xu LJ, Hu HY, Yao H (2023) The deoxygenation mechanism of biomass thermal conversion with molten salts: Experimental and theoretical analysis. Renew Energy 219:119412

    Article  CAS  Google Scholar 

  • Drucker H (1997): Improving Regressors using Boosting Techniques, International Conference on Machine Learning (97, 107, p. e115).

    Google Scholar 

  • Elaigwu SE, Greenway GM (2016) Microwave-assisted and conventional hydrothermal carbonization of lignocellulosic waste material: Comparison of the chemical and structural properties of the hydrochars. J Anal Appl Pyrolysis 118:1–8

    Article  CAS  Google Scholar 

  • Elaigwu SE, Rocher V, Kyriakou G, Greenway GM (2014) Removal of Pb2+ and Cd2+ from aqueous solution using chars from pyrolysis and microwave-assisted hydrothermal carbonization of Prosopis africana shell. J Ind Eng Chem 20:3467–3473

    Article  CAS  Google Scholar 

  • Fan QY, Sun JX, Chu L, Cui LQ, Quan GX, Yan JL, Hussain Q, Iqbal M (2018) Effects of chemical oxidation on surface oxygen-containing functional groups and adsorption behavior of biochar. Chemosphere 207:33–40

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Ganaie MA, Hu M, Malik AK, Tanveer M, Suganthan PN (2022) Ensemble deep learning: A review. Eng Appl Artif Intell 115:105151

    Article  Google Scholar 

  • Gao L-Y, Deng J-H, Huang G-F, Li K, Cai K-Z, Liu Y, Huang F (2019) Relative distribution of Cd2+ adsorption mechanisms on biochars derived from rice straw and sewage sludge. Bioresour Technol 272:114–122

    Article  CAS  Google Scholar 

  • Granados P, Mireles S, Pereira E, Cheng CL, Kang JJ (2022) Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution. Appl Sci 12(10):5040

  • Hankins NP, Lu N, Hilal N (2006) Enhanced removal of heavy metal ions bound to humic acid by polyelectrolyte flocculation. Sep Purif Technol 51:48–56

    Article  CAS  Google Scholar 

  • He Q, Wang H, Li C, Zhou W, Ye Z, Hong L, Yu X, Yu S, Peng L (2023) A Clone Selection Algorithm Optimized Support Vector Machine for AETA Geoacoustic Anomaly Detection. Electronics 12(23):4847

    Article  Google Scholar 

  • Ho SH, Chen YD, Yang ZK, Nagarajan D, Chang JS, Ren NQ (2017) High-efficiency removal of lead from wastewater by biochar derived from anaerobic digestion sludge. Bioresour Technol 246:142–149

    Article  CAS  Google Scholar 

  • Hong W (2010) On Multiple Kernel Learning Methods. Acta Automat Sinica n. pag

  • Inyang MI, Gao B, Yao Y, Xue Y, Zimmerman A, Mosa A, Pullammanappallil P, Ok YS, Cao X (2015) A review of biochar as a low-cost adsorbent for aqueous heavy metal removal. Crit Rev Environ Sci Technol 46:406–433

    Article  Google Scholar 

  • Ke B, Nguyen H, Bui XN, Bui HB, Nguyen-Thoi T (2021) Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network. J Environ Manag 293:112808

    Article  CAS  Google Scholar 

  • Kim M, Lee Y, Park J, Ryu C, Ohm T-I (2016) Partial oxidation of sewage sludge briquettes in a updraft fixed bed. Waste Manag 49:204–211

    Article  CAS  Google Scholar 

  • Kołodyńska D, Wnętrzak R, Leahy JJ, Hayes MHB, Kwapiński W, Hubicki Z (2012) Kinetic and adsorptive characterization of biochar in metal ions removal. Chem Eng J 197:295–305

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Kurniawan TA, Chan GYS, Lo W-H, Babel S (2006) Physico–chemical treatment techniques for wastewater laden with heavy metals. Chem Eng J 118:83–98

    Article  CAS  Google Scholar 

  • Leng L, Yang L, Lei X, Zhang W, Ai Z, Yang Z, Zhan H, Yang J, Yuan X, Peng H, Li H (2022) Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass. Biochar 4:63

    Article  CAS  Google Scholar 

  • Leng LJ, Xu SY, Liu RF, Yu T, Zhuo XM, Leng SQ, Xiong Q, Huang HJ (2020) Nitrogen containing functional groups of biochar: An overview. Bioresour Technol 298:122286

    Article  CAS  Google Scholar 

  • Li YC, Liu J, Yuan QH, Tang H, Yu F, Lv X (2016) A green adsorbent derived from banana peel for highly effective removal of heavy metal ions from water. RSC Adv 6:45041–45048

    Article  CAS  Google Scholar 

  • Li YF, Liu X, Zhang PZ, Wang XL, Cao YY, Han LJ (2018) Qualitative and quantitative correlation of physicochemical characteristics and lead sorption behaviors of crop residue-derived chars. Bioresour Technol 270:545–553

    Article  CAS  Google Scholar 

  • Liu L, Huang Y, Cao J, Liu C, Dong L, Xu L, Zha J (2018) Experimental study of biomass gasification with oxygen-enriched air in fluidized bed gasifier. Sci Total Environ 626:423–433

    Article  CAS  Google Scholar 

  • Liu LQ, Huang Y, Zhang S, Gong Y, Su Y, Cao J, Hu H (2019) Adsorption characteristics and mechanism of Pb(II) by agricultural waste-derived biochars produced from a pilot-scale pyrolysis system. Waste Manag 100:287–295

    Article  CAS  Google Scholar 

  • Liu Z, Han G (2015) Production of solid fuel biochar from waste biomass by low temperature pyrolysis. Fuel 158:159–165

    Article  CAS  Google Scholar 

  • Liu Z, Zhang FS (2009) Removal of lead from water using biochars prepared from hydrothermal liquefaction of biomass. J Hazard Mater 167:933–939

  • Louppe G, Wehenkel L, Sutera A, Geurts P (2013) Understanding variable importances in forests of randomized trees. Adv Neural Inf Proces Syst 26:431–439

    Google Scholar 

  • Lu C, Xu ZX, Dong B, Zhang YH, Wang M, Zeng YF, Zhang C (2022) Machine learning for the prediction of heavy metal removal by chitosan-based flocculants. Carbohydr Polym 285:119240

    Article  CAS  Google Scholar 

  • Lu HL, Zhang WH, Yang YX, Huang XF, Wang SZ, Qiu RL (2012) Relative distribution of Pb2+ sorption mechanisms by sludge-derived biochar. Water Res 46:854–862

    Article  CAS  Google Scholar 

  • Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp 4768–4777

    Google Scholar 

  • Ma ZQ, Yang YY, Ma QQ, Zhou HZ, Luo XP, Liu XH, Wang SR (2017) Evolution of the chemical composition, functional group, pore structure and crystallographic structure of bio-char from palm kernel shell pyrolysis under different temperatures. J Anal Appl Pyrolysis 127:350–359

    Article  CAS  Google Scholar 

  • Madadrang CJ, Kim HY, Gao GH, Wang N, Zhu J, Feng H, Gorring M, Kasner ML, Hou SF (2012) Adsorption Behavior of EDTA-Graphene Oxide for Pb (II) Removal. ACS Appl Mater Interfaces 4:1186–1193

    Article  CAS  Google Scholar 

  • Mohan D, Pittman CU, Bricka M, Smith F, Yancey B, Mohammad J, Steele PH, Alexandre-Franco MF, Gomez-Serrano V, Gong H (2007) Sorption of arsenic, cadmium, and lead by chars produced from fast pyrolysis of wood and bark during bio-oil production. J Colloid Interface Sci 310:57–73

    Article  CAS  Google Scholar 

  • Moreno SR, Coelho LD, Ayala HVH, Mariani VC (2021) Wind turbines anomaly detection based on power curves and ensemble learning. IET Renew Power Gener 14:4086–4093

    Article  Google Scholar 

  • Mu L, Wang Z, Wu D, Zhao L, Yin HC (2022) Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO-NN model. Fuel 318:123644

    Article  CAS  Google Scholar 

  • Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot https://doi.org/10.3389/fnbot.2013.00021

  • Palansooriya KN, Li J, Dissanayake PD, Suvarna M, Li L, Yuan X, Sarkar B, Tsang DCW, Rinklebe J, Wang X, Ok YS (2022) Prediction of soil heavy metal immobilization by biochar using machine learning. Environ Sci Technol 56(7):4187–4198

    Article  Google Scholar 

  • Park JH, Choppala GK, Bolan NS, Chung JW, Chuasavathi T (2011) Biochar reduces the bioavailability and phytotoxicity of heavy metals. Plant Soil 348:439–451

    Article  CAS  Google Scholar 

  • Pekala K, Woznica K, Biecek P (2021) Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure. https://doi.org/10.48550/arXiv.2104.03403

  • Refaeilzadeh P, Tang L, Liu H (2009) Cross-Validation. In: Liu L, MT ÖZ (eds) Encyclopedia of Database Systems. Springer US, Boston, MA, pp 532–538

    Chapter  Google Scholar 

  • Ribeiro MHD, da Silva RG, Moreno SR, Mariani VC, Coelho LD (2022) Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting. Int J Electr Power Energy Syst 136:107712

    Article  Google Scholar 

  • Rupp M (2015) Machine learning for quantum mechanics in a nutshell. Int J Quantum Chem 115:1058–1073

    Article  CAS  Google Scholar 

  • Shen Y, Li X, Yao ZY, Cui XQ, Wang CH (2019) CO2 gasification of woody biomass: Experimental study from a lab-scale reactor to a small-scale autothermal gasifier. Energy 170:497–506

    Article  CAS  Google Scholar 

  • Singh RK, Chakraborty JP, Sarkar A (2020) Optimizing the torrefaction of pigeon pea stalk (cajanus cajan) using response surface methodology (RSM) and characterization of solid, liquid and gaseous products. Renew Energy 155:677–690

    Article  CAS  Google Scholar 

  • Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222

    Article  Google Scholar 

  • Stavropoulos GG, Samaras P, Sakellaropoulos GP (2008) Effect of activated carbons modification on porosity, surface structure and phenol adsorption. J Hazard Mater 151:414–421

    Article  CAS  Google Scholar 

  • Stéfenon SF, Ribeiro MHD, Nied A, Mariani VC, Coelho LD, Leithardt VRQ, Silva LA, Seman LO (2021) Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting. IEEE Access 9:66387–66397

    Article  Google Scholar 

  • Sun Y, Gao B, Yao Y, Fang J, Zhang M, Zhou Y, Chen H, Yang L (2014) Effects of feedstock type, production method, and pyrolysis temperature on biochar and hydrochar properties. Chem Eng J 240:574–578

    Article  CAS  Google Scholar 

  • Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling. J Chem Inf Comput Sci 43:1947–1958

    Article  CAS  Google Scholar 

  • Tang YK, Chen L, Wei XR, Yao QY, Li T (2013) Removal of lead ions from aqueous solution by the dried aquatic plant, Lemna perpusilla Torr. J Hazard Mater 244:603–612

    Article  Google Scholar 

  • Tian Y, Ji CY, Zhao MJ, Xu M, Zhang YS, Wang RG (2010) Preparation and characterization of baker's yeast modified by nano-Fe3O4: Application of biosorption of methyl violet in aqueous solution. Chem Eng J 165:474–481

    Article  CAS  Google Scholar 

  • Trakal L, Bingol D, Pohorely M, Hruska M, Komarek M (2014) Geochemical and spectroscopic investigations of Cd and Pb sorption mechanisms on contrasting biochars: Engineering implications. Bioresour Technol 171:442–451

    Article  CAS  Google Scholar 

  • Tran HN, You S-J, Chao H-P (2016) Thermodynamic parameters of cadmium adsorption onto orange peel calculated from various methods: A comparison study. J Environ Chem Eng 4:2671–2682

    Article  CAS  Google Scholar 

  • Vardhan KH, Kumar PS, Panda RC (2019) A review on heavy metal pollution, toxicity and remedial measures: Current trends and future perspectives. J Mol Liq 290:111197

    Article  CAS  Google Scholar 

  • Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7(1):1–8

    Article  Google Scholar 

  • Wang C, Wang X, Li N, Tao J, Yan B, Cui X, Chen G (2022) Adsorption of lead from aqueous solution by biochar: A review. Clean Technol 4(3):629–652

    Article  Google Scholar 

  • Wang RZ, Huang DL, Liu YG, Zhang C, Lai C, Zeng GM, Cheng M, Gong XM, Wan J, Luo H (2018) Investigating the adsorption behavior and the relative distribution of Cd2+ sorption mechanisms on biochars by different feedstock. Bioresour Technol 261:265–271

    Article  CAS  Google Scholar 

  • Wang Y, Dong L, Hu H, Yan DH, Xu SH, Zou C, Huang YD, Guo GZ, Yao H (2023) The migration and transformation mechanisms of heavy metals during molten salt cyclic thermal treatment of MSWI fly ash. Chem Eng J 471:144731

    Article  CAS  Google Scholar 

  • Wang Z, Liu G, Zheng H, Li F, Ngo HH, Guo W, Liu C, Chen L, Xing B (2015) Investigating the mechanisms of biochar’s removal of lead from solution. Bioresour Technol 177:308–317

    Article  CAS  Google Scholar 

  • Were K, Bui DT, Dick ØB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Indic 52:394–403

    Article  CAS  Google Scholar 

  • Xiao C, Ye J, Esteves RM, Rong C (2016b) Using Spearman's correlation coefficients for exploratory data analysis on big dataset. Concurr Comp: Pract Exp 28(14):3866–3878

    Article  Google Scholar 

  • Xiao X, Chen ZM, Chen BL (2016a) H/C atomic ratio as a smart linkage between pyrolytic temperatures, aromatic clusters and sorption properties of biochars derived from diverse precursory materials. Sci Rep 6:22644

  • Xue Y, Zhou S, Brown RC, Kelkar A, Bai X (2015) Fast pyrolysis of biomass and waste plastic in a fluidized bed reactor. Fuel 156:40–46

    Article  CAS  Google Scholar 

  • Yang F, Zhang SS, Sun YQ, Du Q, Song JP, Tsang DCW (2019a) A novel electrochemical modification combined with one-step pyrolysis for preparation of sustainable thorn-like iron-based biochar composites. Bioresour Technol 274:379–385

    Article  CAS  Google Scholar 

  • Yang WC, Wang ZW, Song S, Han JB, Chen H, Wang XM, Sun RJ, Cheng JY (2019b) Adsorption of copper(II) and lead(II) from seawater using hydrothermal biochar derived from Enteromorpha. Mar Pollut Bull 149:110586

    Article  CAS  Google Scholar 

  • Yu WC, Lian F, Cui GN, Liu ZQ (2018) N-doping effectively enhances the adsorption capacity of biochar for heavy metal ions from aqueous solution. Chemosphere 193:8–16

    Article  CAS  Google Scholar 

  • Yuan H, Lu T, Huang H, Zhao D, Kobayashi N, Chen Y (2015) Influence of pyrolysis temperature on physical and chemical properties of biochar made from sewage sludge. J Anal Appl Pyrolysis 112:284–289

    Article  CAS  Google Scholar 

  • Zhang ZQ, Duan HQ, Zhang YJ, Guo XJ, Yu X, Zhang XG, Rahman MM, Cai JM (2020) Investigation of kinetic compensation effect in lignocellulosic biomass torrefaction: Kinetic and thermodynamic analyses. Energy 207:118290

    Article  CAS  Google Scholar 

  • Zhao Y, Li YL, Fan D, Song JP, Yang F (2021) Application of kernel extreme learning machine and Kriging model in prediction of heavy metals removal by biochar. Bioresour Technol 329:124876

    Article  CAS  Google Scholar 

  • Zhou N, Chen H, Xi J, Yao D, Zhou Z, Tian Y, Lu X (2017) Biochars with excellent Pb(II) adsorption property produced from fresh and dehydrated banana peels via hydrothermal carbonization. Bioresour Technol 232:204–210

    Article  CAS  Google Scholar 

  • Zhu X, Wan Z, Tsang DC, He M, Hou D, Su Z, Shang J (2021) Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption. Chem Eng J 406:126782

    Article  CAS  Google Scholar 

  • Zhu X, Wang X, Ok Y (2019) The application of machine learning methods for prediction of metal sorption onto biochars. J Hazard Mater 378:120727

    Article  CAS  Google Scholar 

  • Zou C, Li S, Huan XZ, Hu HY, Dong L, Zhang HJ, Dai QQ, Yao H (2023) The adsorption mechanism of arsenic in flue gas over the P-doped carbonaceous adsorbent: Experimental and theoretical study. Sci Total Environ 895:165066

    Article  CAS  Google Scholar 

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Funding

This work was supported by National Natural Science Foundation of China (52206141), Key Projects of Shenzhen Technology Research (JSGG20220831101202005), Key Research and Development Program in Hubei Province (2022BCA071, 2022BCA085), and the Project funded by China Postdoctoral Science Foundation (2023T160245, 2022M711238).

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Authors and Affiliations

  1. State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Wei Huang, JingJing Zhu, Lu Dong, Hongyun Hu & Hong Yao

  2. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, China

    Wei Huang

  3. China Power Hua Chuang (Suzhou) Electricity Technology Research Company Co., Ltd., Suzhou, 215125, China

    Liang Wang

  4. Research Institute, Huazhong University of Science and Technology in Shenzhen, Wuhan, 430074, China

    Lu Dong & Hongyun Hu

  5. School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    LinLing Wang

  6. Faculty of Chemistry and Environmental Science, Guangdong Ocean University, Zhanjiang, 524088, PR China

    Zhong Lin

  7. Shenzhen Research Institute of Guangdong Ocean University, Shenzhen, 518108, PR China

    Zhong Lin

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  1. Wei Huang
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Contributions

Wei Huang: Conceptualization, Investigation, Methodology, Writing-Original Draft, Writing-Review & Editing. Liang Wang: Resources, Methodology. JingJing Zhu: Data Curation, Resources. Lu Dong: Conceptualization, Methodology, Resources, Supervision. Hongyun Hu: Writing - Review & Editing, Validation. Hong Yao: Methodology, Supervision. Linling Wang: Methodology, Supervision. Zhong Lin: Resources, Methodology.

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Huang, W., Wang, L., Zhu, J. et al. Application of machine learning in prediction of Pb2+ adsorption of biochar prepared by tube furnace and fluidized bed. Environ Sci Pollut Res 31, 27286–27303 (2024). https://doi.org/10.1007/s11356-024-32951-5

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