Machine learning insights in predicting heavy metals interaction with biochar

Abstract

The use of  machine learning (ML) in the field of predicting heavy metals interaction with biochar is a promising field of research, mainly because of the growing understanding of how removal efficiency is affected by characteristic variables, reaction conditions and biochar properties. The practical application in biochar still faces large challenges, such as difficulties in data collection, inadequate algorithm development, and insufficient information. However, the quantity, quality, and representation of data have a large impact on the accuracy, efficiency, and generalizability of machine learning tasks. From this perspective, the present data descriptors, the efficiency of machine learning-aided property and performance prediction, the interpretation of underlying mechanisms and complicated relationships, and some potential ways to augment the data are discussed regarding the interactions of heavy metals with biochar. Finally, future perspectives and challenges are discussed, and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective.

Highlights

•	A high growth rate of studies on the application of machine learning (ML) in biochar in recent years.
•	ML interpretability of heavy metals (HMs) interaction mechanisms with biochar is explicated emphatically.
•	Challenges and perspectives of ML application in the removal of HMs by biochar.
•	Combining an advanced machine learning technique to achieve better predicted performance.

Keywords

Biochar / Heavy metals / Interaction mechanism / Machine learning

References

[1] Al-Yaari M, Aldhyani THH, Rushd S. Prediction of arsenic removal from contaminated water using artificial neural network model. Appl Sci, 2022, 12(3): 999,

[2] Almalawi A, Khan AI, Alqurashi F, Abushark YB, Alam MM, Qaiyum S. Modeling of Remora optimization with Deep Learning enabled Heavy Metal Sorption Efficiency Prediction onto Biochar. Chemosphere, 2022, 303: 135065,

[3] Ang JC, Tang JY, Chung BYH, et al.. Development of predictive model for biochar surface properties based on biomass attributes and pyrolysis conditions using rough set machine learning. Biomass Bioenergy, 2023, 174: 106820,

[4] Cao HL, Xin Y, Yuan QX. Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach. Bioresour Technol, 2016, 202: 158-164,

[5] Chen C, Liang R, Ge YD, et al.. Fast characterization of biomass pyrolysis oil via combination of ATR-FTIR and machine learning models. Renew Energy, 2022, 194: 220-231,

[6] Chen C, Wang Z, Ge YD, et al.. Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning. Bioresour Technol, 2023, 377: 128893,

[7] Da TX, Ren HK, He WK, Gong SY, Chen T. Prediction of uranium adsorption capacity on biochar by machine learning methods. J Environ Chem Eng, 2022, 10(5): 108449,

[8] Dashti A, Raji M, Harami HR, Zhou JL, Asghari M. Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: application for environmental protection. Sep Purif Technol, 2023, 312: 123399,

[9] Fang L, Huang T, Lu H, et al.. Biochar-based materials in environmental pollutant elimination, H₂ production and CO₂ capture applications. Biochar, 2023, 5: 42,

[10] Gu H, Liu X, Wang S, et al.. COF-based composites: extraordinary removal performance for heavy metals and radionuclides from aqueous solutions. Rev Environ Contam Toxicol, 2022, 260: 23,

[11] Guo GM, Lin LY, Jin FM, Mašek O, Huang Q. Application of heavy metal immobilization in soil by biochar using machine learning. Environ Res, 2023, 231: 116098,

[12] Hao M, Liu Y, Wu W, et al.. Advanced Porous adsorbents for Radionuclides Elimination. EnergyChem, 2023, 5: 100101,

[13] Huang Q, Song S, Chen Z, et al.. Biochar-based materials and their applications in removal of organic contaminants from wastewater: state-of-the-art review. Biochar, 2019, 1: 45-73,

[14] Leng LJ, Yang LH, Lei XN, et al.. Machine learning predicting and engineering the yield, N content, and specifc surface area of biochar derived from pyrolysis of biomass. Biochar, 2022, 4: 63,

[15] Li HL, Ai ZJ, Yang LH, Zhang WJ, Yang ZQ, Peng HY, Leng LJ. Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar. Bioresour Technol, 2023, 369: 128417,

[16] Li J, Pan LJ, Li ZW, Wang Y. Unveiling the migration of Cr and Cd to biochar from pyrolysis of manure and sludge using machine learning. Sci Total Environ, 2023, 885: 163895,

[17] Liu ZX, Xu ZY, Xu LF, et al.. Modified biochar: synthesis and mechanism for removal of environmental heavy metals. Carbon Res, 2022, 1: 8,

[18] Liu X, Li Y, Chen Z, et al.. Recent progress of COFs membranes: design, synthesis and application in Water Treatment. Eco-Environ Health, 2023, 2: 117-130,

[19] Liu JX, Xu ZL, Zhang WJ. Unraveling the role of Fe in as(III & V) removal by biochar via machine learning exploration. Sep Purif Technol, 2023, 311: 123245,

[20] Palansooriya KN, Li J, Dissanayake PD, et al.. Prediction of Soil Heavy Metal immobilization by Biochar using machine learning. Environ Sci Technol, 2022, 56(7): 4187-4198,

[21] Qiu MQ, Liu LJ, Ling Q, Cai YW, Yu SJ, Wang SQ, Fu D, Hu BW, Wang XK. Biochar for the removal of contaminants from soil and water: a review. Biochar, 2022, 4(1): 19,

[22] Sun Y, Zhang YY, Lu L, Wu YJ, Zhang YC, Kamran MA, Chen BL. The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil. Sci Total Environ, 2022, 829: 154668,

[23] Sun ZY, Feng L, Li YQ, Han YM, Zhou HJ, Pan JT. The role of electrochemical properties of biochar to promote methane production in anaerobic digestion. J Clean Prod, 2022, 362: 132296,

[24] Uliana AA, Bui NT, Kamcev J, Taylor MK, Urban JJ, Long JR. Ion-capture electrodialysis using multifunctional adsorptive membranes. Science, 2021, 372(6539): 296-299,

[25] Wang RP, Zhang SY, Chen HL, et al.. Enhancing Biochar-based nonradical persulfate activation using data-driven techniques. Environ Sci Technol, 2023, 57(9): 4050-4059,

[26] Wei X, Peng D, Shen L, Ai YJ, Lu ZH. Analyzing of metal organic frameworks performance in CH₄ adsorption using machine learning techniques: a GBRT model based on small training dataset. J Environ Chem Eng, 2023, 11(3): 110086,

[27] Wei X, Lu Z, Ai Y, Shen L, Wei M, Wang X. Implementing and understanding the unsupervised transfer learning in metal organic framework toward methane adsorption from hypothetical to experimental data. Sep Purif Technol, 2024, 330: 125291,

[28] Zhang HH, Li YF, Xie RY, Zhu Y, Shi S, Yang ZL, Han LJ. A particle scale micro-CT approach for 3D in-situ visualizing the pb (II) adsorption in different crop residue-derived chars. Bioresour Technol, 2022, 344: 126269,

[29] Zhang YJ, Ren M, Tang YM, et al.. Immobilization on anionic metal(loid)s in soil by biochar: a meta-analysis assisted by machine learning. J Hazard Mater, 2022, 438: 129442,

[30] 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(1): 25,

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

[32] Zheng XL, Nguyen H. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere, 2022, 287: 132251,

[33] Zhu XZ, Xu ZB, You SM, et al.. Machine learning exploration of the direct and indirect roles of Fe impregnation on cr(VI) removal by engineered biochar. Chem Eng J, 2022, 428: 131967,

Funding

National Natural Science Foundation of China(U2267222)