Zum Hauptinhalt springen

Application of machine learning in prediction of Pb <superscript>2+</superscript> adsorption of biochar prepared by tube furnace and fluidized bed.

Huang, W ; Wang, L ; et al.
In: Environmental science and pollution research international, Jg. 31 (2024-04-01), Heft 18, S. 27286-27303
Online academicJournal

Titel:
Application of machine learning in prediction of Pb <superscript>2+</superscript> adsorption of biochar prepared by tube furnace and fluidized bed.
Autor/in / Beteiligte Person: Huang, W ; Wang, L ; Zhu, J ; Dong, L ; Hu, H ; Yao, H ; Lin, Z
Link:
Zeitschrift: Environmental science and pollution research international, Jg. 31 (2024-04-01), Heft 18, S. 27286-27303
Veröffentlichung: <2013->: Berlin : Springer ; <i>Original Publication</i>: Landsberg, Germany : Ecomed, 2024
Medientyp: academicJournal
ISSN: 1614-7499 (electronic)
DOI: 10.1007/s11356-024-32951-5
Schlagwort:
  • Adsorption
  • Water Pollutants, Chemical chemistry
  • Charcoal chemistry
  • Machine Learning
  • Lead chemistry
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article
  • Language: English
  • [Environ Sci Pollut Res Int] 2024 Apr; Vol. 31 (18), pp. 27286-27303. <i>Date of Electronic Publication: </i>2024 Mar 20.
  • MeSH Terms: Charcoal* / chemistry ; Machine Learning* ; Lead* / chemistry ; Adsorption ; Water Pollutants, Chemical / chemistry
  • 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. (PMID: 10.1016/j.cej.2015.12.081) ; 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. (PMID: 10.1016/j.chemosphere.2013.10.071) ; 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. (PMID: 10.1021/acs.est.7b06261) ; Awual MR (2019) Mesoporous composite material for efficient lead(II) detection and removal from aqueous media. J Environ Chem Eng 7(3):103124. (PMID: 10.1016/j.jece.2019.103124) ; 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. (PMID: 10.1016/j.jcis.2013.07.050) ; 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. (PMID: 10.1016/j.biortech.2008.06.009) ; 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. ; 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. (PMID: 10.1039/C6NR00695G) ; 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. (PMID: 10.1016/j.biortech.2014.04.048) ; 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. (PMID: 10.1016/j.cej.2015.06.043) ; 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. (PMID: 10.1016/j.scitotenv.2016.03.248) ; 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. (PMID: 10.1016/j.jcis.2017.07.069) ; Dhyani V, Bhaskar T (2018) A comprehensive review on the pyrolysis of lignocellulosic biomass. Renew Energy 129:695–716. (PMID: 10.1016/j.renene.2017.04.035) ; 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. (PMID: 10.1016/j.renene.2023.119412) ; Drucker H (1997): Improving Regressors using Boosting Techniques, International Conference on Machine Learning (97, 107, p. e115). ; 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. (PMID: 10.1016/j.jaap.2015.12.013) ; 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. (PMID: 10.1016/j.jiec.2013.12.036) ; 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. (PMID: 10.1016/j.chemosphere.2018.05.044) ; 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. (PMID: 10.1016/j.jhazmat.2008.06.042) ; Ganaie MA, Hu M, Malik AK, Tanveer M, Suganthan PN (2022) Ensemble deep learning: A review. Eng Appl Artif Intell 115:105151. (PMID: 10.1016/j.engappai.2022.105151) ; 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. (PMID: 10.1016/j.biortech.2018.09.138) ; 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. (PMID: 10.1016/j.seppur.2005.12.022) ; 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. (PMID: 10.3390/electronics12234847) ; 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. (PMID: 10.1016/j.biortech.2017.08.025) ; 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. (PMID: 10.1080/10643389.2015.1096880) ; 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. (PMID: 10.1016/j.jenvman.2021.112808) ; 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. (PMID: 10.1016/j.wasman.2016.01.040) ; 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. (PMID: 10.1016/j.cej.2012.05.025) ; 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. (PMID: 10.1016/j.chemosphere.2019.124364) ; 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. (PMID: 10.1016/j.cej.2006.01.015) ; 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. (PMID: 10.1007/s42773-022-00183-w) ; 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. (PMID: 10.1016/j.biortech.2019.122286) ; 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. (PMID: 10.1039/C6RA07460J) ; 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. (PMID: 10.1016/j.biortech.2018.09.078) ; 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. (PMID: 10.1016/j.scitotenv.2018.01.016) ; 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. (PMID: 10.1016/j.wasman.2019.08.021) ; Liu Z, Han G (2015) Production of solid fuel biochar from waste biomass by low temperature pyrolysis. Fuel 158:159–165. (PMID: 10.1016/j.fuel.2015.05.032) ; 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. ; 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. (PMID: 10.1016/j.carbpol.2022.119240) ; 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. (PMID: 10.1016/j.watres.2011.11.058) ; 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. ; 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. (PMID: 10.1016/j.jaap.2017.07.015) ; 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. (PMID: 10.1021/am201645g) ; 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. (PMID: 10.1016/j.jcis.2007.01.020) ; 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. (PMID: 10.1049/iet-rpg.2020.0224) ; 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. (PMID: 10.1016/j.fuel.2022.123644) ; 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. (PMID: 10.1021/acs.est.1c08302) ; 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. (PMID: 10.1007/s11104-011-0948-y) ; 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. (PMID: 10.1007/978-0-387-39940-9_565) ; 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. (PMID: 10.1016/j.ijepes.2021.107712) ; Rupp M (2015) Machine learning for quantum mechanics in a nutshell. Int J Quantum Chem 115:1058–1073. (PMID: 10.1002/qua.24954) ; 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. (PMID: 10.1016/j.energy.2018.12.176) ; 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. (PMID: 10.1016/j.renene.2020.03.184) ; Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14:199–222. (PMID: 10.1023/B:STCO.0000035301.49549.88) ; 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. (PMID: 10.1016/j.jhazmat.2007.06.005) ; 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. (PMID: 10.1109/ACCESS.2021.3076410) ; 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. (PMID: 10.1016/j.cej.2013.10.081) ; 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. (PMID: 10.1021/ci034160g) ; 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. (PMID: 10.1016/j.jhazmat.2012.10.047) ; 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. (PMID: 10.1016/j.cej.2010.09.037) ; 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. (PMID: 10.1016/j.biortech.2014.08.108) ; 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. (PMID: 10.1016/j.jece.2016.05.009) ; 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. (PMID: 10.1016/j.molliq.2019.111197) ; Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7(1):1–8. (PMID: 10.1186/1471-2105-7-91) ; 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. (PMID: 10.3390/cleantechnol4030039) ; 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. (PMID: 10.1016/j.biortech.2018.04.032) ; 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. (PMID: 10.1016/j.cej.2023.144731) ; 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. (PMID: 10.1016/j.biortech.2014.11.077) ; 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. (PMID: 10.1016/j.ecolind.2014.12.028) ; 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. (PMID: 10.1002/cpe.3745) ; 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. (PMID: 10.1016/j.fuel.2015.04.033) ; 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. (PMID: 10.1016/j.biortech.2018.10.042) ; 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. (PMID: 10.1016/j.marpolbul.2019.110586) ; 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. (PMID: 10.1016/j.chemosphere.2017.10.134) ; 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. (PMID: 10.1016/j.jaap.2015.01.010) ; 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. (PMID: 10.1016/j.energy.2020.118290) ; 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. (PMID: 10.1016/j.biortech.2021.124876) ; 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. (PMID: 10.1016/j.biortech.2017.01.074) ; 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. (PMID: 10.1016/j.cej.2020.126782) ; 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. (PMID: 10.1016/j.jhazmat.2019.06.004) ; 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. (PMID: 10.1016/j.scitotenv.2023.165066)
  • Contributed Indexing: Keywords: Biochar; Pb2+ adsorption; fluidized bed; machine learning; tube furnace
  • Substance Nomenclature: 16291-96-6 (Charcoal) ; 0 (biochar) ; 2P299V784P (Lead) ; 0 (Water Pollutants, Chemical)
  • Entry Date(s): Date Created: 20240320 Date Completed: 20240426 Latest Revision: 20240502
  • Update Code: 20240503

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

oder
oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

oder
oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

xs 0 - 576
sm 576 - 768
md 768 - 992
lg 992 - 1200
xl 1200 - 1366
xxl 1366 -