Machine learning–aided design of La-based composite modified biochar: Efficient materials and cost optimization for low-phosphorus water treatment

Abstract

Phosphates are key contributors to eutrophication in water bodies. Lanthanum (La)-modified biochar (LaBC) offers notable advantages in achieving ultralow residual phosphate concentrations in water. However, the high cost of La limits its economic feasibility for practical use. This study applied machine learning (ML) models to optimize the design of La-based composite modified biochar, aiming to reduce application costs while maintaining effective phosphate removal to low residual levels. Eight ML models, namely random forest, gradient boosting regression (GBR), extreme gradient boosting (XGB), light gradient boosting, support vector machine, ridge regression, Bayesian ridge regression, and artificial neural network, were employed to predict the phosphate removal performance of La-based composite modified biochar. Results revealed that tree-based ensemble learning models (GBR and XGB: R²=0.98 and 0.99, respectively) outperformed other models. Feature importance analysis indicated that adsorption reaction conditions and metal loading were the primary factors influencing residual phosphate concentrations. Experimental validation demonstrated strong agreement between actual removal efficiencies and model predictions. Based on actual phosphate concentrations in various lakes, economic costs, and treatment effectiveness, targeted material remediation strategies were proposed. The phosphate removal costs for La–Fe-modified biochar and two types of La–Ca-modified biochar were reduced by 59.25%, 55.10%, and 76.54%, respectively, compared with that of LaBC, achieving dual optimization of treatment effectiveness and economic cost. Overall, this study provides insights into developing low-cost, high-efficiency biochar materials and offers robust technical support for controlling water eutrophication.

Keywords

Machine learning / La-based composite modified biochar / Phosphate / Low concentration / Cost-effectiveness

Highlight

•	Tree-based ensemble learning models are best suited for phosphate removal studies.
•	Adsorption conditions and metal loading are key factors affecting phosphate removal.
•	Targeted remediation strategies were proposed for lakes across various countries.
•	La–Fe and La–Ca biochars deliver high-efficiency treatment and cost optimization.

References

[1] Akram M, Xu X, Gao BY, Wang S, Khan R, Yue QY, Duan PJ, Dan HB, Pan JW. Highly efficient removal of phosphate from aqueous media by pomegranate peel co-doping with ferric chloride and lanthanum hydroxide nanoparticles. J Cleaner Prod, 2021, 292 125311

[2] Asaoka S, Kawakami K, Saito H, Ichinari T, Nohara H, Oikawa T. Adsorption of phosphate onto lanthanum-doped coal fly ash—blast furnace cement composite. J Hazard Mater, 2021, 406 124780

[3] Banu JR, Merrylin J, Kavitha S, Kannah RY, Selvakumar P, Gopikumar S, Sivashanmugam P, Do KU, Kumar G. Trends in biological nutrient removal for the treatment of low strength organic wastewaters. Curr Pollut Rep, 2020, 7: 1-30

[4] Belloni C, Korving L, Witkamp GJ, Brück E, Dugulan AI. Effect of goethite doping using elements with different preferential oxidation states for improved reversible phosphate adsorption. J Environ Chem Eng, 2023, 11 110505

[5] Cabral JBP, de Barcelos AA, Ramalho FL, da Silva Gomes P, Nogueira PF, Paulino AT. Eutrophication levels of hydropower plant water reservoirs via trophic state index with evaluation of the fate of pollutants affected by the land use model. Water Air Soil Pollut, 2024, 235: 625

[6] Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, Marquéz JRG, Gruber B, Lafourcade B, Leitão PJ, Münkemüller T, McClean C, Osborne PE, Reineking B, Schröder B, Skidmore AK, Zurell D, Lautenbach S. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 2012, 36: 27-46

[7] Egle L, Rechberger H, Krampe J, Zessner M. Phosphorus recovery from municipal wastewater: an integrated comparative technological, environmental and economic assessment of P recovery technologies. Sci Total Environ, 2016, 571: 522-542

[8] Fan SQ, Liu GX, Tu Y, Zhu JD, Zhang PF, Tian ZY. Improved multi-criteria decision making method integrating machine learning for patent competitive potential evaluation: a case study in water pollution abatement technology. J Cleaner Prod, 2023, 403 136896

[9] Faria FF, Ariyan TN, Mia MdY. Trophic status analysis and nutrient source allocation in urban lakes of Dhaka, Bangladesh: a comprehensive approach to eutrophication monitoring. Environ Monit Assess, 2024, 196: 1252

[10] Feng MH, Li MM, Guo CB, Yuan MY, Zhang LS, Qiu SK, Fu WL, Zhang KQ, Guo HX, Wang F. Green synthesis of Ca_xLa_1-xMnO₃ with modulation of mesoporous and vacancies for efficient low concentration phosphate adsorption. J Environ Manage, 2024, 351 119837

[11] Fu W, Feng M, Guo C, Zhou J, Zhang X, Lv S, Huo Y, Wang F. Machine learning-driven prediction of phosphorus removal performance of metal-modified biochar and optimization of preparation processes considering water quality management objectives. Bioresour Technol, 2024, 403 130861

[12] Fu W, Yao X, Zhang L, Zhou J, Zhang X, Yuan T, Lv S, Yang P, Fu K, Huo Y, Wang F. Design optimization of bimetal-modified biochar for enhanced phosphate removal performance in livestock wastewater using machine learning. Bioresour Technol, 2025, 418 131898

[13] Fulazzaky MA, Nuid M, Aris A, Fulazzaky M, Sumeru K, Muda K. Mass transfer kinetics of phosphorus biosorption by aerobic granules. J Water Process Eng, 2019, 31 100889

[14] Gao N, Qiu CH, Gu XY, Xu YT, Chen YW, Liu JN. Enhanced selective adsorption of low-concentration phosphorus via layered La₂O₂CO₃ with high environmental adaptability. Chem Eng J, 2023, 477 146998

[15] Gebler D, Wiegleb G, Szoszkiewicz K. Integrating river hydromorphology and water quality into ecological status modelling by artificial neural networks. Water Res, 2018, 139: 395-405

[16] Ghiasi R, Torkzadeh P, Noori M. A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function. Struct Health Monit, 2016, 15: 302-316

[17] Gonzalez V, Vignati DAL, Leyval C, Giamberini L. Environmental fate and ecotoxicity of lanthanides: are they a uniform group beyond chemistry?. Environ Int, 2014, 71: 148-157

[18] He QQ, Zhao HJ, Teng ZD, Wang Y, Li M, Hoffmann MR. Phosphate removal and recovery by lanthanum-based adsorbents: a review for current advances. Chemosphere, 2022, 303 134987

[19] He FL, Cao YH, Wu YH, Chen H, Huang F, Chang M, Wei CH, Qiu GL. Selective and efficient phosphate removal using Ca–La layered double hydroxide-functionalized sludge biochar. ACS EST Water, 2024, 4: 5575-5586

[20] Himeur Y, Rimal B, Tiwary A, Amira A. Using artificial intelligence and data fusion for environmental monitoring: a review and future perspectives. Inf Fusion, 2022, 86–87: 44-75

[21] Hong S, Lynn HS. Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction. BMC Med Res Methodol, 2020, 20: 199

[22] Jamwal P, Carvalho L, Bhattacharyya S, Muttepawar P. The benefits of restoring urban lakes in the tropics. Environ Monit Assess, 2023, 195: 1294

[23] Janiesch C, Zschech P, Heinrich K. Machine learning and deep learning. Electron Mark, 2021, 31: 685-695

[24] Jebli I, Belouadha FZ, Kabbaj MI, Tilioua A. Prediction of solar energy guided by pearson correlation using machine learning. Energy, 2021, 224 120109

[25] Koh KY, Chen Z, Zhang S, Chen JP. Cost-effective phosphorus removal from aqueous solution by a chitosan/lanthanum hydrogel bead: material development, characterization of uptake process and investigation of mechanisms. Chemosphere, 2022, 286 131458

[26] Kong LC, Tian Y, Pang Z, Huang XH, Li M, Li N, Zhang J, Zuo W, Li JJ. Needle-like Mg-La bimetal oxide nanocomposites derived from periclase and lanthanum for cost-effective phosphate and fluoride removal: characterization, performance and mechanism. Chem Eng J, 2020, 382 122963

[27] Kumar PS, Korving L, van Loosdrecht MCM, Witkamp GJ. Adsorption as a technology to achieve ultra-low concentrations of phosphate: research gaps and economic analysis. Water Research X, 2019, 4 100029

[28] Le C, Zha Y, Li Y, Sun D, Lu H, Yin B. Eutrophication of lake waters in China: cost, causes, and control. Environ Manage, 2010, 45: 662-668

[29] Leng LJ, Lei XN, Abdullah AlDhabi N, Wu ZB, Yang ZQ, Li TH, Zhang WJ, Liu W, Zhan H, Peng HY, Tang WW, Xiong T, Li HL. Machine-learning-aided prediction and engineering of nitrogen-containing functional groups of biochar derived from biomass pyrolysis. Chem Eng J, 2024, 485 149862

[30] Li MM, Luo Y, Zhao D, Qiu SK, Zhang LS, Zhang KQ, Song C, Wang F. Different La/Fe oxide composites for efficient phosphate removal from wastewater: properties and mechanisms. J Environ Chem Eng, 2022, 10 107329

[31] Li J, Huang HM, Yu W, Young B. A novel lanthanum carbonate for low-level phosphorus removal: adsorption performance and mechanism. Chem Eng J, 2023, 473 145225

[32] Li WL, Cai GG, Luo K, Zhang JJ, Li HB, Li GL, Zhang JM, Chen X, Xie FZ. Synthesis of magnesium-modified ceramsite from iron tailings as efficient adsorbent for phosphorus removal. Sep Purif Technol, 2023, 326 124817

[33] Liao H, Chen H, Mi Y, Luo C, Horng S-J, Li T. Sparse orthogonal supervised feature selection with global redundancy minimization, label scaling, and robustness. Inform Sci, 2024, 666 120454

[34] Lin Z, Chen J. Magnetic Fe₃O₄@MgAl-LDH@La(OH)₃ composites with a hierarchical core-shell structure for phosphate removal from wastewater and inhibition of labile sedimentary phosphorus release. Chemosphere, 2021, 264 128551

[35] Liu H, Chen M, Wei D, Zhang H, Peng J, Liu G. A novel visible light controllable adsorption-desorption system with a magnetic recyclable adsorbent. Sci Total Environ, 2020, 707 136025

[36] Liu DD, Bai L, Qiao Q, Zhang Y, Li XY, Zhao RN, Liu JY. Anthropogenic total phosphorus emissions to the Tuojiang River basin, China. J Clean Prod, 2021, 294 126325

[37] Liu P, Xu H, Jin P, Zhu X, Zheng J, Liu Y, Yang J, Xu D, Liang H. DFT-assisted machine learning for polyester membrane design in textile wastewater recovery applications. Water Res, 2025, 279 123438

[38] Luo Y, Xie K, Feng YY, He QP, Zhang KQ, Shen SZ, Wang F. Synthesis of a La(OH)₃ nanorod/walnut shell biochar composite for reclaiming phosphate from aqueous solutions. Colloid Surface A, 2021, 610 125736

[39] Luo ZN, He H, Zhang TY, Wei XL, Dong ZY, Xu MY, Zhao HX, Zheng ZX, Pan RJ, Hu CY, Zeng C, ElDin MG, Xu B. Enhanced iodinated disinfection byproducts formation in iodide/iodate-containing water undergoing UV-chloramine sequential disinfection: Machine learning-aided identification of reaction mechanisms. Water Res, 2025, 272 122975

[40] McDowell RW, Haygarth PM. Reducing phosphorus losses from agricultural land to surface water. Curr Opin Biotechnol, 2024, 89 103181

[41] Mohr F, van Rijn JN. Learning curves for decision making in supervised machine learning: a survey. Mach Learn, 2024, 113: 8371-8425

[42] Morales-Hernández A, Nieuwenhuyse IV, Gonzalez SR. A survey on multi-objective hyperparameter optimization algorithms for machine learning. Artif Intell Rev, 2022, 56: 8043-8093

[43] Saracanlao RJ, Saelens T, Voegelin A, Smolders E, Everaert M. Recycled iron phosphates: a new phosphorus fertilizer for paddy rice. Environ Sci Technol, 2024, 58: 9250-9260

[44] Shao M, Wang X, Bu Z, Chen X, Wang Y. Prediction of energy consumption in hotel buildings via support vector machines. Sustain Cities Soc, 2020, 57 102128

[45] Siders ZA, Havens KE. Revisiting the total maximum daily load total phosphorus goal in Lake Okeechobee. Hydrobiologia, 2020, 847: 4221-4232

[46] Sravandas P, Alexander LK. Dual-functional MnS nanomaterials: efficient adsorbent for phosphate removal and sonocatalyst for textile dye degradation. J Phys Chem Solids, 2025, 196 112409

[47] Suba VO, Khan MS, Miruka J, Mwalati L, Njoroge B, Oluoch E, Nyongesa J, Otieno D, Li Y. Seasonal phytoplankton ecosystem dynamics in response to environmental variables in Winam gulf, Lake Victoria, Kenya. Aquat Sci, 2024, 87 4

[48] Sun EH, Zhang YY, Xiao QB, Li HY, Qu P, Yong C, Wang BY, Feng YF, Huang HY, Yang LZ, Hunter C. Formable porous biochar loaded with La-Fe(hydr)oxides/montmorillonite for efficient removal of phosphorus in wastewater: process and mechanisms. Biochar, 2022, 4: 53

[49] Thebelt A, Kronqvist J, Mistry M, Lee RM, Sudermann-Merx N, Misener R. Entmoot: a framework for optimization over ensemble tree models. Comput Chem Eng, 2021, 151 107343

[50] Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc B, 2011, 73: 273-282

[51] Ullah H, Khan S, Zhu XY, Chen BL, Rao ZP, Wu NC, Idris AM. Machine learning-aided biochar design for the adsorptive removal of emerging inorganic pollutants in water. Sep Purif Technol, 2025, 362 131421

[52] Wang M, Wang T, Cai PQ, Chen XD. Nanomaterials discovery and design through machine learning. Small Methods, 2019

[53] Wang GY, Yue XZ, Zhang SR, Geng Q, Zheng JJ, Xu XX, Li T, Pu YL, Li Y, Jia YX, Lv GC, Xu CL, Wu GX, Yang ZB, Cheng Z. La(III) loaded Fe(III) cross–linked chitosan composites for efficient removal of phosphate from wastewater: performance and mechanisms. J Cleaner Prod, 2022, 379 134833

[54] Wang DS, Gan XY, Wang ZQ, Jiang SF, Zheng XY, Zhao M, Zhang YH, Fan CZ, Wu SQ, Du LN. Research status on remediation of eutrophic water by submerged macrophytes: a review. Process Saf Environ, 2023, 169: 671-684

[55] Wang S, Wang Y, Dong S, Li X, Liu C. Synchronously construction of hierarchical porous channels and cationic surface charge on lanthanum-hydrogel for rapid phosphorus removal. Environ Res, 2023, 236 116730

[56] Wang ZX, Anand D, He Z. Phosphorus recovery from whole digestate through electrochemical leaching and precipitation. Environ Sci Technol, 2023, 57: 10107-10116

[57] Wang XH, Li Y, Wen X, Liu LY, Zhang LZ, Long MC. Cooperation of ferrous ions and hydrated ferric oxide for advanced phosphate removal over a wide pH range: mechanism and kinetics. Water Res, 2024, 249 120969

[58] Wang ZY, Chen QW, Zhang JY, Yan HL. Water diversion is not to blame for phosphorus enrichment in Taihu Lake. Engineering, 2024, 35: 10-14

[59] Wen S, Zhong J, Li X, Liu C, Yin H, Li D, Ding S, Fan C. Does external phosphorus loading diminish the effect of sediment dredging on internal phosphorus loading? An in-situ simulation study. J Hazard Mater, 2020, 394 122548

[60] Wu Y-R, Chen P-J. Toxicity assessments with medaka (oryzias latipes) reveal elevated ecological risks of lanthanum and gadolinium in aquatic ecosystems. Environ Pollut, 2025, 383 126802

[61] Wu BL, Fang LP, Fortner JD, Guan XH, Lo IMC. Highly efficient and selective phosphate removal from wastewater by magnetically recoverable La(OH)₃/Fe₃O₄ nanocomposites. Water Res, 2017, 126: 179-188

[62] Yao Y, Gao B, Chen J, Yang L. Engineered biochar reclaiming phosphate from aqueous solutions: mechanisms and potential application as a slow-release fertilizer. Environ Sci Technol, 2013, 47: 8700-8708

[63] Yeo I-K. A new family of power transformations to improve normality or symmetry. Biometrika, 2000, 87: 954-959

[64] Yu SH, Wang Y, Hua L, Fan MY, Ren XH, Zhang L. Mg-Fe bimetallic oxide nanocomposite for superior phosphate removal and recovery from aquatic environment: synergistic effects of sorption and struvite crystallization. J Environ Chem Eng, 2024, 12 112132

[65] Zhang Q, Sang Z, Li Q, Gong J, Peng X, Li L, Zhang Z, Zhang B, Li S, Yang X. Facile fabrication of la/ca bimetal-organic frameworks for economical and efficient remove phosphorus from water. J Mol Liq, 2022, 356 119024

[66] Zhang WT, Chen RH, Li J, Huang TY, Wu BD, Ma J, Wen QQ, Tan J, Huang WG. Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning. Biochar, 2023, 5: 25

[67] Zhang Y, Xie Q, Fu C, Qiu Z, Zhuang Z, Wang T, Fang Y, Li Y, Cai Y, Feng H, Yu B. Lanthanum-based hydrogels for simultaneous removal of trace phosphorus and algae. Chem Eng J, 2025, 519 164822

[68] Zheng YL, Zimmerman AR, Gao B. Comparative investigation of characteristics and phosphate removal by engineered biochars with different loadings of magnesium, aluminum, or iron. Sci Total Environ, 2020, 747 141277

Funding

Yunnan Fundamental Research Projects (202101AT070002)

Intellectual Aid Xinjiang Innovation and Expansion Talent Program(Research)

Tianjin Central Government Funds for Guiding Local Scientific and Technological Development(24ZYCGYS00910)

Central-level Basic Research and Business Expenses Project for Talent Cultivation at Research Institute (1102021500170072528)

RIGHTS & PERMISSIONS

The Author(s)