机器学习在生物炭的制备及应用中的运用研究进展

doi:10.3969/j.issn.0253-2417.2024.02.017

Abstract

Abstract:

In recent years, machine learning had made great progress in predicting and optimizing the preparation and application of biochar. This paper provided a comprehensive review of the role of machine learning in facilitating the preparation and application of biochar. The machine learning models applied in the preparation and application of biochar were mainly introduced, as well as some suggestions for the latest research.

Key words: biochar, machine learning, prediction, preparation

CLC Number:

Bufei WANG, Yuanchong YUE, Mei WANG, Kang SUN, Shule WANG, Quan BU. Research Progress in the Application of Machine Learning in the Preparation and Application of Biochar[J]. Chemistry and Industry of Forest Products, 2024, 44(2): 127-137.

Figures/Tables 6

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References 75

1	计海洋, 汪玉瑛, 刘玉学, 等.生物炭及改性生物炭的制备与应用研究进展[J].核农学报,2018,32(11):2281-2287. doi: 10.11869/j.issn.100-8551.2018.11.2281
	JI H Y , WANG Y Y , LIU Y X , et al.Advance in preparation and application of biochar and modified biochar research[J].Journal of Nuclear Agricultural Sciences,2018,32(11):2281-2287. doi: 10.11869/j.issn.100-8551.2018.11.2281
2	YU S J, WANG L Z, LI Q H, et al. Sustainable carbon materials from the pyrolysis of lignocellulosic biomass[J/OL]. Mater Today Sustainability, 2022, 19: 100209[2023-09-10]. https://doi.org/10.1016/j.mtsust.2022.100209.
3	张学民, 贺冠宇, 尹绍奇, 等.多孔生物质碳材料的制备及其在超级电容器中的应用研究进展[J].过程工程学报,2024,24(2):127-138.
	ZHANG X M , HE G Y , YIN S Q , et al.Research progress in the preparation of porous biomass carbon materials and their applications in supercapacitors[J].The Chinese Journal of Process Engineering,2024,24(2):127-138.
4	任学勇, 祁项超, 曹畅, 等."双碳"战略下生物质活性炭材料生产与应用发展探析[J].林产工业,2022,59(4):48-51, 63.
	REN X Y , QI X C , CAO C , et al.Research on production and application of biomass activated carbon under "Double Carbon" strategy[J].China Forest Products Industry,2022,59(4):48-51, 63.
5	QIAN S X, ZHOU X R, FU Y K, et al. Biochar-compost as a new option for soil improvement: Application in various problem soils[J/OL]. The Science of The Total Environment, 2023, 870: 162024[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2023.162024.
6	DU L Q, AHMAD S, LIU L N, et al. A review of antibiotics and antibiotic resistance genes(ARGs) adsorption by biochar and modified biochar in water[J/OL]. Science of the Total Environment, 2023, 858: 159815[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2022.159815.
7	KHEDULKAR A P, PANDIT B, DANG V D, et al. Agricultural waste to real worth biochar as a sustainable material for supercapacitor[J/OL]. Science of the Total Environment, 2023, 869: 161441[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2023.161441.
8	DA T X, REN H K, HE W K, et al. Prediction of uranium adsorption capacity on biochar by machine learning methods[J/OL]. Journal of Environmental Chemical Engineering, 2022, 10(5): 108449[2023-09-10]. https://doi.org/10.1016/j.jece.2022.108449.
9	PATHY A, MEHER S, BALASUBRAMANIAN P. Predicting algal biochar yield using eXtreme Gradient Boosting(XGB) algorithm of machine learning methods[J/OL]. Algal Research, 2020, 50: 102006[2023-09-10]. https://doi.org/10.1016/j.algal.2020.102006.
10	YANG X P, YUAN C, HE S R, et al. Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics[J/OL]. Fuel, 2023, 331: 125718[2023-09-10]. https://doi.org/10.1016/j.fuel.2022.125718.
11	ARTRITH N , BUTLER K T , COUDERT F X , et al.Best practices in machine learning for chemistry[J].Nature Chemistry,2021,13(6):505-508. doi: 10.1038/s41557-021-00716-z
12	ZHANG W J, CHEN Q Y, CHEN J F, et al. Machine learning for hydrothermal treatment of biomass: A review[J/OL]. Bioresource Technology, 2023, 370: 128547[2023-09-10]. https://doi.org/10.1016/j.biortech.2022.128547.
13	SU D, BATZELIS E, PAL B. Machine learning algorithms in forecasting of photovoltaic power generation[C]//2019 International Conference on Smart Energy Systems and Technologies(SEST). NewYork: IEEE, 2019.
14	LIU Z Y , LIU Y , MENG Q , et al.A tailored machine learning approach for urban transport network flow estimation[J].Transportation Research Part C: Emerging Technologies,2019,108,130-150. doi: 10.1016/j.trc.2019.09.006
15	GOYAL D , DHAMI S S , PABLA B .Non-contact fault diagnosis of bearings in machine learning environment[J].IEEE Sensors Journal,2020,20(9):4816-4823. doi: 10.1109/JSEN.2020.2964633
16	SAKHETA A, NAYAK R, O'HARA I, et al. A review on modelling of thermochemical processing of biomass for biofuels and prospects of artificial intelligence-enhanced approaches[J/OL]. Bioresource Technology, 2023, 386: 129490[2023-09-10]. https://doi.org/10.1016/j.biortech.2023.129490.
17	JEYASUBRAMANIAN K, THANGAGIRI B, SAKTHIVEL A, et al. A complete review on biochar: Production, property, multifaceted applications, interaction mechanism and computational approach[J/OL]. Fuel, 2021, 292: 120243[2023-09-10]. https://doi.org/10.1016/j.fuel.2021.120243.
18	XU P C, JI X B, LI M J, et al. Small data machine learning in materials science[J/OL]. npj Computational Materials, 2023, 9(1): 42[2023-09-10]. https://doi.org/10.1038/s41524-023-01000-z
19	AMINI M , RAHMANI A .Agricultural databases evaluation with machine learning procedure[J].Journal of Engineering and Applied Science,2023,8,39-50.
20	陈一飞, 张晓晴, 谭康豪, 等. 基于机器学习的多孔生物炭吸附CO₂性能预测[J/OL]. 土木与环境工程学报(中英文), 2023: 1-9[2023-09-10]. https://doi.org/10.11835/j.issn.2096-6717.2023.060.
	CHEN Y F, ZHANG X Q, TAN K H, et al. Prediction of CO₂ adsorption performance in porous biochar based on machine learning[J/OL]. Journal of Civil and Environmental Engineering, 2023: 1-9[2023-09-10]. https://doi.org/10.11835/j.issn.2096-6717.2023.060.
21	YAO Z , LUM Y , JOHNSTON A , et al.Machine learning for a sustainable energy future[J].Nature Reviews Materials,2023,8(3):202-215.
22	WEI J , CHU X , SUN X Y , et al.Machine learning in materials science[J].InfoMat,2019,1(3):338-358. doi: 10.1002/inf2.12028
23	TANG J Y, CHUNG B Y H, ANG J C, et al. Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning[J/OL]. Environmental Technology, 2023: 1-15[2023-09-10]. https://doi.org/10.1080/09593330.2023.2192877.
24	LIU X, SHAO Z Y, WANG Y X, et al. New use for Lentinus edodes bran biochar for tetracycline removal[J/OL]. Environmental Research, 2023, 216: 114651[2023-09-10]. https://doi.org/10.1016/j.envres.2022.114651.
25	WANG R P , ZHANG S Y , CHEN H L , et al.Enhancing biochar-based nonradical persulfate activation using data-driven techniques[J].Environmental Science & Technology,2023,57(9):4050-4059.
26	ZHOU B Q, YANG R C, LI H P, et al. Numeric and nonnumeric information input to predict adsorption amount, capacity and kinetics of tetracyclines by biochar via machine learning[J/OL]. Chemical Engineering Journal, 2023, 471: 144636[2023-09-10]. https://doi.org/10.1016/j.cej.2023.144636.
27	TANG Q H, CHEN Y Q, YANG H P, et al. Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: Effects of pyrolysis conditions and biomass characteristics[J/OL]. Bioresource Technology, 2021, 339: 125581[2023-09-10]. https://doi.org/10.1016/j.biortech.2021.125581.
28	ALABDRABALNABI A, GAUTAM R, MANI S S. Machine learning to predict biochar and bio-oil yields from co-pyrolysis of biomass and plastics[J/OL]. Fuel, 2022, 328: 125303[2023-09-10]. https://doi.org/10.1016/j.fuel.2022.125303.
29	ZHU X Z, XU Z B, YOU S M, et al. Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr(VI) removal by engineered biochar[J/OL]. Chemical Engineering Journal, 2022, 428: 131967[2023-09-10]. https://doi.org/10.1016/j.cej.2021.131967.
30	PALANSOORIYA K N , LI J , DISSANAYAKE P D , et al.Prediction of soil heavy metal immobilization by biochar using machine learning[J].Environmental Science & Technology,2022,56(7):4187-4198.
31	NOVAKOVI J D , VELJOVI A , ILI S S , et al.Evaluation of classification models in machine learning[J].Theory and Applications of Mathematics & Computer Science,2017,7(1):39-46.
32	FLACH P. Performance evaluation in machine learning: The good, the bad, the ugly, and the way forward[C]//Proceedings of the AAAI conference on artificial intelligence. Hawaii: Association for the Advancement of Artificial Intelligence, 2019: 9808-9814.
33	LIPTON Z C .The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery[J].Queue,2018,16(3):31-57. doi: 10.1145/3236386.3241340
34	VELLIDO A, MARTÍN-GUERRERO J D, LISBOA P J. Making machine learning models interpretable[C]//ESANN 2012 proceedings. Bruges: European Symposium on Artificial Neural Networks, 2012: 163-172.
35	ZHU X Z, HE M J, SUN Y Q, et al. Insights into the adsorption of pharmaceuticals and personal care products(PPCPs) on biochar and activated carbon with the aid of machine learning[J/OL]. Journal of Hazardous Materials, 2022, 423: 127060[2023-09-10]. https://doi.org/10.1016/j.jhazmat.2021.127060.
36	LENG L J, YANG L H, LEI X N, et al. Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass[J/OL]. Biochar, 2022, 4(1): 63[2023-09-10]. https://doi.org/10.1007/s42773-022-00183-w.
37	LI Y Z, GUPTA R, YOU S M. Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass[J/OL]. Bioresource Technology, 2022, 359: 127511[2023-09-10]. https://doi.org/10.1016/j.biortech.2022.127511.
38	GREENWELL B M, BOEHMKE B C, MCCARTHY A J. A simple and effective model-based variable importance measure[J/OL]. arXiv preprint arXiv, 2018: 1805.04755[2023-09-10]. https://doi.org/10.48550/arXiv.1805.04755.
39	ALTMANN A , TOLOŞI L , SANDER O , et al.Permutation importance: A corrected feature importance measure[J].Bioinformatics,2010,26(10):1340-1347.
40	GOLDSTEIN A , KAPELNER A , BLEICH J , et al.Peeking inside the black box: Visualizing statistical learning with plots of individual conditional expectation[J].Journal of Computational and Graphical Statistics,2015,24(1):44-65.
41	APLEY D W , ZHU J .Visualizing the effects of predictor variables in black box supervised learning models[J].Journal of the Royal Statistical Society Series B: Statistical Methodology,2020,82(4):1059-1086.
42	PALANSOORIYA K N , LI J , DISSANAYAKE P D , et al.Prediction of soil heavy metal immobilization by biochar using machine learning[J].Environmental Science & Technology,2022,56(7):4187-4198.
43	WANG Z X, PENG X G, XIA A, et al. The role of machine learning to boost the bioenergy and biofuels conversion[J/OL]. Bioresource Technology, 2022, 343: 126099[2023-09-10]. https://doi.org/10.1016/j.biortech.2021.126099.
44	ZHU X Z, LI Y N, WANG X N. Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions[J/OL]. Bioresource Technology, 2019, 288: 121527[2023-09-10]. https://doi.org/10.1016/j.biortech.2019.121527.
45	HAI A, BHARATH G, PATAH M F A, et al. Machine learning models for the prediction of total yield and specific surface area of biochar derived from agricultural biomass by pyrolysis[J/OL]. Environmental Technology & Innovation, 2023, 30: 103071[2023-09-10]. https://doi.org/10.1016/j.eti.2023.103071
46	ANG J C, TANG J Y, CHUNG B Y H, et al. Development of predictive model for biochar surface properties based on biomass attributes and pyrolysis conditions using rough set machine learning[J/OL]. Biomass Bioenergy, 2023, 174: 106820[2023-09-10]. https://doi.org/10.1016/j.biombioe.2023.106820.
47	KHAN M, ULLAH Z, MAŠEK O, et al. Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms[J/OL]. Bioresource Technology, 2022, 355: 127215[2023-09-10]. https://doi.org/10.1016/j.biortech.2022.127215.
48	HAQ Z U, ULLAH H, KHAN M N A, et al. Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction[J/OL]. Bioresource Technology, 2022, 363: 128008[2023-09-10]. https://doi.org/10.1016/j.biortech.2022.128008.
49	CHEN C, WANG Z, GE Y D, et al. Characteristics prediction of hydrothermal biochar using data enhanced interpretable machine learning[J/OL]. Bioresource Technology, 2023, 377: 128893[2023-09-10]. https://doi.org/10.1016/j.biortech.2023.128893.
50	QASIM B, RAZZAK A A, RASHEED R T. Effect of biochar amendment on mobility and plant uptake of Zn, Pb and Cd in contaminated soil[J/OL]. IOP Conference Series: Earth and Environmental Science, 2021, 779(1): 012082[2023-09-10]. https://dx.doi.org/10.1088/1755-1315/779/1/012082.
51	IBRAHIM E A, EL-SHERBINI M A A, SELIM E-M M. Effects of biochar on soil properties, heavy metal availability and uptake, and growth of summer squash grown in metal-contaminated soil[J/OL]. Scientia Horticulturae, 2022, 301: 111097[2023-09-10]. https://doi.org/10.1016/j.scienta.2022.111097.
52	FENG Q W, WANG B, CHEN M, et al. Invasive plants as potential sustainable feedstocks for biochar production and multiple applications: A review[J/OL]. Resources, Conservation and Recycling, 2021, 164: 105204[2023-09-10]. https://doi.org/10.1016/j.resconrec.2020.105204.
53	LIU M Y, ZHU J, YANG X, et al. Biochar produced from the straw of common crops simultaneously stabilizes soil organic matter and heavy metals[J/OL]. Science of the Total Environment, 2022, 828: 154494[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2022.154494.
54	ZHANG R-H, XIE Y L, ZHOU G L, et al. The effects of short-term, long-term, and reapplication of biochar on the remediation of heavy metal-contaminated soil[J/OL]. Ecotoxicology and Environmental Safety, 2022, 248: 114316[2023-09-10]. https://doi.org/10.1016/j.ecoenv.2022.114316.
55	SHENTU J, LI X X, HAN R F, et al. Effect of site hydrological conditions and soil aggregate sizes on the stabilization of heavy metals(Cu, Ni, Pb, Zn) by biochar[J/OL]. Science of the Total Environment, 2022, 802: 149949[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2021.149949.
56	QI X, YIN H, ZHU M H, et al. MgO-loaded nitrogen and phosphorus self-doped biochar: High-efficient adsorption of aquatic Cu²⁺, Cd²⁺, and Pb²⁺ and its remediation efficiency on heavy metal contaminated soil[J/OL]. Chemosphere, 2022, 294: 133733[2023-09-10]. https://doi.org/10.1016/j.chemosphere.2022.133733.
57	PESCATORE A, GRASSI C, RIZZO A M, et al. Effects of biochar on berseem clover(Trifolium alexandrinum, L.) growth and heavy metal(Cd, Cr, Cu, Ni, Pb, and Zn) accumulation[J/OL]. Chemosphere, 2022, 287: 131986[2023-09-10]. https://doi.org/10.1016/j.chemosphere.2021.131986.
58	AHMAD W , KHAN A , ZEESHAN M , et al.Relative efficiency of biochar particles of different sizes for immobilising heavy metals and improving soil properties[J].Crop & Pasture Science,2023,74(2):112-120.
59	JI M Y, WANG X X, USMAN M, et al. Effects of different feedstocks-based biochar on soil remediation: A review[J/OL]. Environmental Pollution, 2022, 294: 118655[2023-09-10]. https://doi.org/10.1016/j.envpol.2021.118655.
60	LIAO W, ZHANG X, KE S J, et al. Effect of different biomass species and pyrolysis temperatures on heavy metal adsorption, stability and economy of biochar[J/OL]. Industrial Crops and Products, 2022, 186: 115238[2023-09-10]. https://doi.org/10.1016/j.indcrop.2022.115238.
61	RAHI A A , YOUNIS U , AHMED N , et al.Toxicity of Cadmium and nickel in the context of applied activated carbon biochar for improvement in soil fertility[J].Saudi Journal of Biological Sciences,2022,29(2):743-750.
62	XU M, BARBOSA DA SILVA E, GAO P, et al. Biochar impact on chromium accumulation by rice through Fe microbial-induced redox transformation[J/OL]. Journal of Hazardous Materials, 2020, 388: 121807[2023-09-10]. https://doi.org/10.1016/j.jhazmat.2019.121807.
63	GUO G M, LIN L Y, JIN F M, et al. Application of heavy metal immobilization in soil by biochar using machine learning[J/OL]. Environmental Research, 2023, 231: 116098[2023-09-10]. https://doi.org/10.1016/j.envres.2023.116098.
64	SUN Y, ZHANG Y Y, LU L, et al. The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil[J/OL]. Science of the Total Environment, 2022, 829: 154668[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2022.154668.
65	GODVIN SHARMILA V, KUMAR TYAGI V, VARJANI S, et al. A review on the lignocellulosic derived biochar-based catalyst in wastewater remediation: Advanced treatment technologies and machine learning tools[J/OL]. Bioresource Technology, 2023, 387: 129587[2023-09-10]. https://doi.org/10.1016/j.biortech.2023.129587.
66	ZHU X Z, HE M J, SUN Y Q, et al. Insights into the adsorption of pharmaceuticals and personal care products(PPCPs) on biochar and activated carbon with the aid of machine learning[J/OL]. Journal of Hazardous Materials, 2022, 423: 127060[2023-09-10]. https://doi.org/10.1016/j.jhazmat.2021.127060.
67	ALMUNTASHIRI A, HOSSEINZADEH A, BADETI U, et al. Removal of pharmaceutical compounds from synthetic hydrolysed urine using granular activated carbon: Column study and predictive modelling[J/OL]. Journal of Water Process Engineering, 2022, 45: 102480[2023-09-10]. https://doi.org/10.1016/j.jwpe.2021.102480.
68	NIGHOJKAR A, NAGPAL J, SOBOYEJO W, et al. Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms[J/OL]. Environmental Monitoring and Assessment, 2023, 195(8): 984[2023-09-10]. https://doi.org/10.1007/s10661-023-11599-7.
69	ZHANG P Y, LIU C, LAO D Q, et al. Unveiling the drives behind tetracycline adsorption capacity with biochar through machine learning[J/OL]. Scientific Reports, 2023, 13(1): 11512[2023-09-10]. https://doi.org/10.1038/s41598-023-38579-8.
70	DASHTI A, RAJI M, RIASAT HARAMI H, et al. Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection[J/OL]. Separation and Purification Technology, 2023, 312: 123399[2023-09-10]. https://doi.org/10.1016/j.seppur.2023.123399.
71	NGUYEN X C, LY Q V, NGUYEN T T H, et al. Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars[J/OL]. Chemosphere, 2022, 287: 132203[2023-09-10]. https://doi.org/10.1016/j.chemosphere.2021.132203.
72	CAO H L , MILAN Y J , MOOD S H , et al.A novel elemental composition based prediction model for biochar aromaticity derived from machine learning[J].Artificial Intelligence in Agriculture,2021,5,133-141.
73	CHEN J, ZHANG M Q, XU Z J, et al. Machine-learning analysis to predict the fluorescence quantum yield of carbon quantum dots in biochar[J/OL]. Science of the Total Environment, 2023, 896: 165136[2023-09-10]. https://doi.org/10.1016/j.scitotenv.2023.165136.
74	DERINGER V L , MERLET C , HU Y C , et al.Towards an atomistic understanding of disordered carbon electrode materials[J].Chemical Communications,2018,54(47):5988-5991.
75	BI K , WANG Y , ZHOU G Y .Hierarchical porous N/S-doped carbon with machine learning to predict advanced potassium-ion batteries[J].Journal of Materials Chemistry A,2023,11(22):11696-11703.

研究内容 research content	方法¹⁾ methods	数据集数量 datasets number	模型准确性²⁾ model accuracy	参考文献 reference
预测生物炭产量和碳含量 predicting biochar yield and carbon content	RF	245(炭产量biochar yield)， 128(碳含量C content)	-	[44]
预测藻类生物质热解的生物炭产量 predicting biochar yield from pyrolysis of algal biomass	XGB	91(炭产量biochar yield)	-	[9]
预测生物炭产量与生物炭成分 predicting biochar yield and composition	MLP-NN	226(炭产量biochar yield)， 226(炭成分biochar composition)	+	[37]
预测生物炭产量和生物炭比表面积 predicting biochar yield and specific surface area	MLR，DT，SVM，RF，KNN	292(炭产量biochar yield), 292(比表面积specific surface area)	-	[45]
预测生物炭比表面积、孔隙体积和孔径 predicting of biochar specific surface area, pore volume and pore size	RSML	104(比表面积specific surface area)，66(孔容pore volume)，57(孔径pore diamete)	-	[46]
预测和优化生物炭比表面积、氮含量和生物炭产量 predicting and optimising biochar specific surface area, nitrogen content and yield	RF，GBR	400	+	[36]
预测和优化生物炭产量 predicting and optimising biochar yield	ELT-PSO，GPR-PSO，GPR-GA，SVM-PSO，ELT-GA，DT-GA，SVM-GA，DT-PSO	443(炭产量biochar yield)	++	[47]

吸附的污染物 adsorbed contaminants	数据集大小¹⁾ datasets number	方法²⁾ methods	模型准确性 model accuracy	参考文献 reference
水体中的个人护理品 personal care products in the water	113, 101, 158	RF	-	[66]
人体尿液中的药物污染物 pharmaceutical contaminants in human urine	83	ANN	+	[67]
放射性水体中的铀 uranium in radioactive water	777	LR, SVR, MLP, RF	+	[8]
污染水中的二嗪农 diazinon in contaminated water	\	XGBoost	++	[68]
水中的四环素tetracycline in water	245	RF, GBDT, XGBoost, ANN	++	[69]
工业废水中的重金属污染物 heavy metal contaminants in industrial wastewater	353	ANFIS, CSA-LSSVM, PSO-ANFIS, GP	++	[70]
废水中的药物污染物 harmaceutical contaminants in wastewater	1 033	DNN, Cubist, KNN, RF	+	[71]

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Research Progress in the Application of Machine Learning in the Preparation and Application of Biochar

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