Tối ưu hóa tổng hợp và mô hình hóa hấp phụ của biochar cho việc loại bỏ chất ô nhiễm thông qua học máy

Abd El Aziz M, Hemdan AM, Ewees AA et al (2017) Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. IEEE PES-IAS PowerAfrica Conference 2017 Jun 27–30. Accra, GHANA, pp. 115–120 Abdulsalam J, Lawal AI, Setsepu RL, Onifade M, Bada S (2020) Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties. Bioresour Bioprocess. https://doi.org/10.1186/s40643-020-00350-6 Adedeji PA, Akinlabi SA, Madushele N, Olatunji OO (2020) Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review. J Cleaner Prod. https://doi.org/10.1016/j.jclepro.2020.122104 Ahmed MM, Isa NAM (2017) Knowledge base to fuzzy information granule: a review from the interpretability-accuracy perspective. Appl Soft Comput 54:121–140. https://doi.org/10.1016/j.asoc.2016.12.055 Alabdrabalnabi A, Gautam R, Mani Sarathy S (2022) Machine learning to predict biochar and bio-oil yields from co-pyrolysis of biomass and plastics. Fuel. https://doi.org/10.1016/j.fuel.2022.125303 Albalasmeh A, Gharaibeh MA, Mohawesh O et al (2020) Characterization and artificial neural networks modelling of methylene blue adsorption of biochar derived from agricultural residues: effect of biomass type, pyrolysis temperature, particle size. J Saudi Chem Soc 24:811–823. https://doi.org/10.1016/j.jscs.2020.07.005 Alhothali A, Khurshid H, Mustafa MRU et al (2022) Evaluation of contemporary computational techniques to optimize adsorption process for simultaneous removal of COD and TOC in wastewater. Adsorpt Sci Technol 2022:1–16. https://doi.org/10.1155/2022/7874826 Alsawy T, Rashad E, El-Qelish M, Mohammed RH (2022) A comprehensive review on the chemical regeneration of biochar adsorbent for sustainable wastewater treatment. Npj Clean Water. https://doi.org/10.1038/s41545-022-00172-3 Altikat A, Alma MH (2022a) Application of new hybrid models based on artificial neural networks for modeling pyrolysis yields of Atriplex nitens S. Int J Energy Res 46:4445–4461. https://doi.org/10.1002/er.7441 Altikat A, Alma MH (2022b) Prediction carbonization yields and the sensitivity analyses using deep learning neural networks and support vector machines. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-022-04407-1 An Q, Jiang YQ, Nan HY, Yu Y, Jiang JN (2019) Unraveling sorption of nickel from aqueous solution by KMnO4 and KOH-modified peanut shell biochar: implicit mechanism. Chemosphere 214:846–854. https://doi.org/10.1016/j.chemosphere.2018.10.007 An XF, Wu ZS, Shi W et al (2021) Biochar for simultaneously enhancing the slow-release performance of fertilizers and minimizing the pollution of pesticides. J Hazard Mater. https://doi.org/10.1016/j.jhazmat.2020.124865 Ascher S, Watson I, You SM (2022) Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renewable Sustain Energy Rev. https://doi.org/10.1016/j.rser.2021.111902 Avakyan L, Tolchina D, Barkovski V et al (2022) Ultimate sensitivity of radial distribution functions to architecture of PtCu bimetallic nanoparticles. Comput Mater Sci 208:111326. https://doi.org/10.1016/j.commatsci.2022.111326 Avrutskiy VI (2020) Preventing overfitting by training derivatives. Proceedings of the Future Technologies Conference (Ftc) 2019, Vol 1 1069:144–163. https://doi.org/10.1007/978-3-030-32520-6_12 Azzi ES, Karltun E, Sundberg C (2022) Life cycle assessment of urban uses of biochar and case study in Uppsala, Sweden. Biochar. https://doi.org/10.1007/s42773-022-00144-3 Beigzadeh B, Bahrami M, Amiri MJ, Mahmoudi MR (2020) A new approach in adsorption modeling using random forest regression, Bayesian multiple linear regression, and multiple linear regression: 2,4-D adsorption by a green adsorbent. Water Sci Technol 82:1586–1602. https://doi.org/10.2166/wst.2020.440 Bhagat SK, Tiyasha T, Awadh SM, Tung TM, Jawad AH, Yaseen ZM (2021) Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models. Environ Pollut 268:115663. https://doi.org/10.1016/j.envpol.2020.115663 Bisaria K, Singh R, Gupta M, Mathur A, Dixit A (2022) Novel acoustic-activated alkali-functionalized Trapa bispinosa peel biochar for green immobilization of chlorpyrifos from wastewater: artificial intelligence modelling and experimental validation. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-022-02898-z Bischl B, Binder M, Lang M et al (2023) Hyperparameter optimization: foundations, algorithms, best practices, and open challenges. Wiley Int Rev Data Mining Knowl Discovery Bong HK, Selvarajoo A, Arumugasamy SK (2022) Stability of biochar derived from banana peel through pyrolysis as alternative source of nutrient in soil: feedforward neural network modelling study. Environ Monit Assess. https://doi.org/10.1007/s10661-021-09691-x Boraah N, Chakma S, Kaushal P (2022) Attributes of wood biochar as an efficient adsorbent for remediating heavy metals and emerging contaminants from water: a critical review and bibliometric analysis. J Environ Chem Eng. https://doi.org/10.1016/j.jece.2022.107825 Cao L (2022) Recent advances in the application of machine-learning algorithms to predict adsorption energies. Trends Chem 4:347–360. https://doi.org/10.1016/j.trechm.2022.01.012 Cao H, Xin Y, Yuan Q (2016) Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach. Bioresour Technol 202:158–164. https://doi.org/10.1016/j.biortech.2015.12.024 Cashman S, Korostynska O, Shaw A, Lisboa P, Conroy L (2017) Detecting the presence and concentration of nitrate in water using microwave spectroscopy. IEEE Sens J 17:4092–4099. https://doi.org/10.1109/Jsen.2017.2705281 Cha JS, Jang SH, Lam SS et al (2021) Performance of CO2 and Fe-modified lignin char on arsenic (V) removal from water. Chemosphere. https://doi.org/10.1016/j.chemosphere.2021.130521 Chang CY, Medford AJ (2021) Application of density functional tight binding and machine learning to evaluate the stability of biomass intermediates on the Rh(111) surface. J Phys Chem C 125:18210–18216. https://doi.org/10.1021/acs.jpcc.1c05715 Cheng F, Luo H, Colosi LM (2020) Slow pyrolysis as a platform for negative emissions technology: an integration of machine learning models, life cycle assessment, and economic analysis. Energy Convers Manage. https://doi.org/10.1016/j.enconman.2020.113258 Cheng CY, Pourhejazy P, Ying KC, Lin CF (2021) Unsupervised learning-based artificial bee colony for minimizing non-value-adding operations. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2021.107280 Choudhary M, Kumar R, Neogi S (2020) Activated biochar derived from Opuntia ficus-indica for the efficient adsorption of malachite green dye, Cu2+ and Ni2+ from water. J Hazard Mater. https://doi.org/10.1016/j.jhazmat.2020.122441 Chowdhury S, Sikder J, Mandal T, Halder G (2019) Comprehensive analysis on sorptive uptake of enrofloxacin by activated carbon derived from industrial paper sludge. Sci Total Environ 665:438–452. https://doi.org/10.1016/j.scitotenv.2019.02.081 Da T-X, Ren H-K, He W-K, Gong S-Y, Chen T (2022) Prediction of uranium adsorption capacity on biochar by machine learning methods. J Environ Chem Eng. https://doi.org/10.1016/j.jece.2022.108449 Dahan E, Keller Y (2021) A unified approach to Kinship verification. IEEE Trans Pattern Anal Mach Int 43:2851–2857. https://doi.org/10.1109/Tpami.2020.3036993 Dalhat MA, Muazu ND, Essa MH (2021) Generalized decay and artificial neural network models for fixed-Bed phenolic compounds adsorption onto activated date palm biochar. J Environ Chem Eng. https://doi.org/10.1016/j.jece.2020.104711 Deng C, Ji X, Rainey C et al (2020) Integrating machine learning with human knowledge. Iscience 23:101656 Din SU, Awan JM, Imran M et al (2021) Novel nanocomposite of biochar-zerovalent copper for lead adsorption. Microsc Res Tech 84:2598–2606. https://doi.org/10.1002/jemt.23810 Ding Y, Jiang XQ, Kim YJ (2022) Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules. Bioinformatics 38:2826–2831. https://doi.org/10.1093/bioinformatics/btac211 Dong Z, Bai X, Xu D, Li W (2022) Machine learning prediction of pyrolytic products of lignocellulosic biomass based on physicochemical characteristics and pyrolysis conditions. Bioresour Technol 367:128182. https://doi.org/10.1016/j.biortech.2022.128182 Egbosiuba TC (2022) Biochar and bio-oil fuel properties from nickel nanoparticles assisted pyrolysis of cassava peel. Heliyon. https://doi.org/10.1016/j.heliyon.2022.e10114 El Hanandeh A, Mahdi Z, Imtiaz MS (2021) Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: comparison of six machine learning approaches. Environ Res 192:110338. https://doi.org/10.1016/j.envres.2020.110338 Ertugrul OF (2020) A novel randomized machine learning approach: reservoir computing extreme learning machine. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106433 Ewees AA, Abd El Aziz M, Elhoseny M (2017) Social-spider optimization algorithm for improving ANFIS to predict biochar yield. 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2017 Jul 03–05. Delhi, INDIA Ewees AA, Elaziz MA (2018) Improved adaptive neuro-fuzzy inference system using gray wolf optimization: a case study in predicting biochar yield. J Intell Syst 29:924–940. https://doi.org/10.1515/jisys-2017-0641 Faulon JL, Faure L (2021) In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering. Curr Opin Chem Biol 65:85–92 Findlay MR, Freitas DN, Mobed-Miremadi M, Wheeler KE (2018) Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties. Environ Sci: Nano 5:64–71. https://doi.org/10.1039/c7en00466d Goswami L, Kushwaha A, Kafle SR, Kim B-S (2022) Surface modification of biochar for dye removal from wastewater. Catalysts. https://doi.org/10.3390/catal12080817 Graham S, Epstein D, Rajpoot N (2020) Dense steerable filter CNNs for exploiting rotational symmetry in histology images. IEEE Trans Med Imaging 39:4124–4136. https://doi.org/10.1109/Tmi.2020.3013246 Guo MX, Li H, Baldwin B, Morrison J (2019) Thermochemical processing of animal manure for bioenergy and biochar. Int J Green Engry 13:963–968. https://doi.org/10.1080/15435075.2015.1087851 Gupta S, Patel P, Mondal P (2022) Biofuels production from pine needles via pyrolysis: Process parameters modeling and optimization through combined RSM and ANN based approach. Fuel. https://doi.org/10.1016/j.fuel.2021.122230 Hai A, Bharath G, Daud M et al (2021) Valorization of groundnut shell via pyrolysis: Product distribution, thermodynamic analysis, kinetic estimation, and artificial neural network modeling. Chemosphere 283:131162. https://doi.org/10.1016/j.chemosphere.2021.131162 Han M, Zhao Q, Li W et al (2021) Global soil organic carbon changes and economic revenues with biochar application. GCB Bioenergy 14:364–377. https://doi.org/10.1111/gcbb.12915 Handelman GS, Kok HK, Chandra RV et al (2019) Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods (vol 212, pg 38, 2019). Am J Roentgenol 212:479–479. https://doi.org/10.2214/Ajr.18.20994 Haq ZU, Ullah H, Khan MNA, Raza Naqvi S, Ahad A, Amin NAS (2022) Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction. Bioresour Technol 363:128008. https://doi.org/10.1016/j.biortech.2022.128008 Harrington GH, Kelly C, Attari V, Arroyave R, Kalidindi SR (2022) Application of a chained-ANN for learning the process-structure mapping in Mg2SixSn1-x spinodal decomposition. Integr Mater Manuf I 11:433–449. https://doi.org/10.1007/s40192-022-00274-3 Isaeva VI, Vedenyapina MD, Kurmysheva AY et al (2021) Modern carbon-based materials for adsorptive removal of organic and inorganic pollutants from water and wastewater. Molecules. https://doi.org/10.3390/molecules26216628 Jabar JM, Odusote YA (2021) Utilization of prepared activated biochar from water lily (Nymphaea lotus) stem for adsorption of malachite green dye from aqueous solution. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-021-01399-9 Jeyasubramanian K, Thangagiri B, Sakthivel A et al (2021) A complete review on biochar: production, property, multifaceted applications, interaction mechanism and computational approach. Fuel. https://doi.org/10.1016/j.fuel.2021.120243 Jung C, Boateng LK, Flora JRV et al (2015) Competitive adsorption of selected non-steroidal anti-inflammatory drugs on activated biochars: experimental and molecular modeling study. Chem Eng J 264:1–9. https://doi.org/10.1016/j.cej.2014.11.076 Kabir MM, Akter MM, Khandaker S et al (2022) Highly effective agro-waste based functional green adsorbents for toxic chromium(VI) ion removal from wastewater. J Mol Liq. https://doi.org/10.1016/j.molliq.2021.118327 Kang JK, Seo EJ, Lee CG, Jeong S, Park SJ (2022) Application of response surface methodology and artificial neural network for the preparation of Fe-loaded biochar for enhanced Cr(VI) adsorption and its physicochemical properties and Cr(VI) adsorption characteristics. Environ Sci Pollut Res Int 29:60852–60866. https://doi.org/10.1007/s11356-022-20009-3 Karaboga D, Kaya E (2018) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 52:2263–2293. https://doi.org/10.1007/s10462-017-9610-2 Katiyar R, Patel AK, Nguyen TB, Singhania RR, Chen CW, Dong CD (2021) Adsorption of copper(II) in aqueous solution using biochars derived from Ascophyllum nodosum seaweed. Bioresour Technol. https://doi.org/10.1016/j.biortech.2021.124829 Ke B, Nguyen H, Bui XN et al (2021a) Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models. Chemosphere 276:130204. https://doi.org/10.1016/j.chemosphere.2021.130204 Ke B, Nguyen H, Bui XN, Bui HB, Nguyen-Thoi T (2021b) 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 Manage 293:112808. https://doi.org/10.1016/j.jenvman.2021.112808 Khan M, Ullah Z, Masek O, Raza Naqvi S, Nouman Aslam Khan M (2022) Artificial neural networks for the prediction of biochar yield: a comparative study of metaheuristic algorithms. Bioresour Technol 355:127215. https://doi.org/10.1016/j.biortech.2022.127215 Kim JE, Bhatia SK, Song HJ et al (2020) Adsorptive removal of tetracycline from aqueous solution by maple leaf-derived biochar. Bioresour Technol. https://doi.org/10.1016/j.biortech.2020.123092 Kim S, Quiroz-Arita C, Monroe EA et al (2021) Application of attached algae flow-ways for coupling biomass production with the utilization of dilute non-point source nutrients in the Upper Laguna Madre. TX. Water Res 191:116816. https://doi.org/10.1016/j.watres.2021.116816 Komnitsas K, Zaharaki D, Bartzas G, Alevizos G (2017) Adsorption of scandium and neodymium on biochar derived after low-temperature pyrolysis of sawdust. Minerals. https://doi.org/10.3390/min7100200 Krueger BC, Fowler GD, Templeton MR, Moya B (2020) Resource recovery and biochar characteristics from full-scale faecal sludge treatment and co-treatment with agricultural waste. Water Res. https://doi.org/10.1016/j.watres.2019.115253 Kwiatkowski M, Kalderis D (2020) A complementary analysis of the porous structure of biochars obtained from biomass. Carbon Lett 30:325–329. https://doi.org/10.1007/s42823-019-00101-4 Lakshmi D, Akhil D, Kartik A et al (2021) Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.149623 Lamoureux PS, Choksi TS, Streibel V, Abild-Pedersen F (2021) Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces. Phys Chem Chem Phys 23:22022–22034. https://doi.org/10.1039/d1cp02198b Lee ME, Park JH, Chung JW (2019) Comparison of the lead and copper adsorption capacities of plant source materials and their biochars. J Environ Manage 236:118–124. https://doi.org/10.1016/j.jenvman.2019.01.100 Li YF, Liang DM (2019) Safe semi-supervised learning: a brief introduction. Front Comput Sci 13:669–676. https://doi.org/10.1007/s11704-019-8452-2 Li M, Wei D, Liu T et al (2019a) EDTA functionalized magnetic biochar for Pb(II) removal: adsorption performance, mechanism and SVM model prediction. Sep Purif Technol. https://doi.org/10.1016/j.seppur.2019.115696 Li S, Harris S, Anandhi A, Chen G (2019b) Predicting biochar properties and functions based on feedstock and pyrolysis temperature: a review and data syntheses. J Cleaner Prod 215:890–902. https://doi.org/10.1016/j.jclepro.2019.01.106 Li D, Liu SL, Gao FR, Sun X (2020a) Continual learning classification method with new labeled data based on the artificial immune system. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106423 Li N, Shepperd M, Guo YC (2020b) A systematic review of unsupervised learning techniques for software defect prediction. Inf Softw Technol. https://doi.org/10.1016/j.infsof.2020.106287 Li YG, Li QQ, Wu CX, Luo X, Yu X, Chen MJ (2020c) The inappropriate application of the regression Langmuir Q(m) for adsorption capacity comparison. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134222 Li LY, Cao G, Zhu RS (2021a) Adsorption of Cr(VI) from aqueous solution by a litchi shell-based adsorbent. Environ Res. https://doi.org/10.1016/j.envres.2020.110356 Li TY, Xiang H, Yang Y, Wang J, Yildiz G (2021b) Prediction of char production from slow pyrolysis of lignocellulosic biomass using multiple nonlinear regression and artificial neural network. J Anal Appl Pyrolysis. https://doi.org/10.1016/j.jaap.2021.105286 Li XD, Peng B, Liu QY, Zhang HY (2022a) Microwave pyrolysis coupled with conventional pre-pyrolysis of the stalk for syngas and biochar. Bioresour Technol. https://doi.org/10.1016/j.biortech.2022.126745 Li Y, Gupta R, You S (2022b) Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass. Bioresour Technol 359:127511. https://doi.org/10.1016/j.biortech.2022.127511 Lian SM, Liu JW, Lu RK, Luo XL (2019) Captured multi-label relations via joint deep supervised autoencoder. Appl Soft Comput 74:709–728. https://doi.org/10.1016/j.asoc.2018.10.035 Liao M, Kelley SS, Yao Y (2019) Artificial neural network based modeling for the prediction of yield and surface area of activated carbon from biomass. Biofuels Bioprod Biorefin 13:1015–1027. https://doi.org/10.1002/bbb.1991 Liew YW, Arumugasamy SK, Selvarajoo A (2022) Potential of biochar as soil amendment: prediction of elemental ratios from pyrolysis of agriculture biomass using artificial neural network. Water Air Soil Pollut. https://doi.org/10.1007/s11270-022-05510-2 Ligthart A, Catal C, Tekinerdogan B (2021) Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.107023 Liu YY, Chen JW (2022) Effect of ageing on biochar properties and pollutant management. Chemosphere. https://doi.org/10.1016/j.chemosphere.2021.133427 Liu HD, Xu GR, Li GB (2021a) Autocatalytic sludge pyrolysis by biochar derived from pharmaceutical sludge for biogas upgrading. Energy. https://doi.org/10.1016/j.energy.2021.120802 Liu L, Li C, Liu XI, Gao Y, Liu G (2021b) Study on the regulation mechanism of cadmium adsorption system mediated by extraneous dissolved organic matter. Ecotox Environ Saf. https://doi.org/10.1016/j.ecoenv.2021.112930 Liu X, Shao Z, Wang Y et al (2022a) New use for Lentinus edodes bran biochar for tetracycline removal. Environ Res. https://doi.org/10.1016/j.envres.2022.114651 Liu XW, Hu YK, Chen JG, Li KQ (2022b) Shape and boundary-aware multi-branch model for semi-supervised medical image segmentation. Comput Biol Med 143:105252. https://doi.org/10.1016/j.compbiomed.2022.105252 Liu YB, Yang XL, Zhang JQ, Zhu ZY (2022c) Process simulation of preparing biochar by biomass pyrolysis via aspen plus and its economic evaluation. Waste Biomass Valorization 13:2609–2622. https://doi.org/10.1007/s12649-021-01671-z Liu ZR, Shurin GV, Bian L, White DL, Shurin MR, Star A (2022d) A carbon nanotube sensor array for the label-free discrimination of live and dead cells with machine learning. Anal Chem 94:3565–3573. https://doi.org/10.1021/acs.analchem.1c04661 Luo ZR, Yao B, Yang X et al (2022) Novel insights into the adsorption of organic contaminants by biochar: a review. Chemosphere. https://doi.org/10.1016/j.chemosphere.2021.132113 Masek O, Budarin V, Gronnow M et al (2013) Microwave and slow pyrolysis biochar-comparison of physical and functional properties. J Anal Appl Pyrolysis 100:41–48. https://doi.org/10.1016/j.jaap.2012.11.015 Masood A, Ahmad K (2021) A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance. J Cleaner Prod 322:129072. https://doi.org/10.1016/j.jclepro.2021.129072 Masrura SU, Jones-Lepp TL, Kajitvichyanukul P, Ok YS, Tsang DCW, Khan E (2022) Unintentional release of antibiotics associated with nutrients recovery from source-separated human urine by biochar. Chemosphere. https://doi.org/10.1016/j.chemosphere.2022.134426 Mayol AP, Maningo JMZ, Chua-Unsu AGAY et al. (2018) Application of artificial neural networks in prediction of pyrolysis behavior for algal mat (LABLAB) biomass. IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management. https://doi.org/10.1109/hnicem.2018.8666376 Medeiros D, Nzediegwu C, Benally C et al (2022) Pristine and engineered biochar for the removal of contaminants co-existing in several types of industrial wastewaters: a critical review. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.151120 Merdun H, Sezgin IV (2018) Modelling of pyrolysis product yields by artificial neural networks. Int J Renew Energy Res 8:1178–1188 Mir AA, Celebi FV, Alsolai H et al (2022) Anomalies prediction in radon time series for earthquake likelihood using machine learning-based ensemble model. IEEE Access 10:37984–37999. https://doi.org/10.1109/Access.2022.3163291 Mizikovsky D, Sanchez MN, Nefzger CM, Partida GC, Palpant NJ (2022) Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations. Nucleic Acids Res. https://doi.org/10.1093/nar/gkac413 Mondal S, Aikat K, Halder G (2016) Biosorptive uptake of ibuprofen by chemically modified Parthenium hysterophorus derived biochar: equilibrium, kinetics, thermodynamics and modeling. Ecol Eng 92:158–172. https://doi.org/10.1016/j.ecoleng.2016.03.022 Mondal S, Aikat K, Siddharth K et al (2017) Optimizing ranitidine hydrochloride uptake of Parthenium hysterophorus derived N-biochar through response surface methodology and artificial neural network. Process Saf Environ Prot 107:388–401. https://doi.org/10.1016/j.psep.2017.03.011 Mukherjee S, Kamila B, Paul S et al (2022) Insight into biosorptive uptake of fluoride by chemically activated biochar: experimental modeling and parametric optimization. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-021-02200-7 Nair RR, Mondal MM, Weichgrebe D (2020) Biochar from co-pyrolysis of urban organic wastes—investigation of carbon sink potential using ATR-FTIR and TGA. Biomass Convers Biorefin 12:4729–4743. https://doi.org/10.1007/s13399-020-01000-9 Nandy A, Duan CR, Taylor MG, Liu F, Steeves AH, Kulik HJ (2021) Computational discovery of transition-metal complexes: from high-throughput screening to machine learning. Chem Rev 121:9927–10000. https://doi.org/10.1021/acs.chemrev.1c00347 Narde SR, Remya N (2022) Biochar production from agricultural biomass through microwave-assisted pyrolysis: predictive modelling and experimental validation of biochar yield. Environ Dev Sustain 24:11089–11102. https://doi.org/10.1007/s10668-021-01898-9 Nguyen XC, Ly QV, Peng W et al (2021) Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: a comparative study and prediction of effluents using machine learning. J Hazard Mater 413:125426. https://doi.org/10.1016/j.jhazmat.2021.125426 Nguyen XC, Ly QV, Nguyen TTH, Ngo HTT, Hu Y, Zhang Z (2022) Potential application of machine learning for exploring adsorption mechanisms of pharmaceuticals onto biochars. Chemosphere 287:132203. https://doi.org/10.1016/j.chemosphere.2021.132203 Ohale PE, Onu CE, Nwabanne JT, Aniagor CO, Okey-Onyesolu CF, Ohale NJ (2022) A comparative optimization and modeling of ammonia–nitrogen adsorption from abattoir wastewater using a novel iron-functionalized crab shell. Appl Water Sci. https://doi.org/10.1007/s13201-022-01713-4 Onyekwena CC, Xue Q, Li Q et al (2022) Support vector machine regression to predict gas diffusion coefficient of biochar-amended soil. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2022.109345 Ortiz-Monsalve S, Gutterres M, Valente P et al (2020) Degradation of a leather-dye by the combination of depolymerised wood-chip biochar adsorption and solid-state fermentation with Trametes villosa SCS-10. Bioresour Bioprocess. https://doi.org/10.1186/s40643-020-00349-z Padakandla S (2021) A Survey of reinforcement learning algorithms for dynamically varying environments. Acm Comput Surv 54:127. https://doi.org/10.1145/3459991 Palansooriya KN, Li J, Dissanayake PD et al (2022) Prediction of soil heavy metal immobilization by biochar using machine learning. Environ Sci Technol 56:4187–4198. https://doi.org/10.1021/acs.est.1c08302 Pan XQ, Gu ZP, Chen WM, Li QB (2021) Preparation of biochar and biochar composites and their application in a Fenton-like process for wastewater decontamination: a review. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142104 Pardakhti M, Moharreri E, Wanik D, Suib SL, Srivastava R (2017) Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs). ACS Comb Sci 19:640–645. https://doi.org/10.1021/acscombsci.7b00056 Pathy A, Meher S, Balasubramanian P (2020) Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods. Algal Res. https://doi.org/10.1016/j.algal.2020.102006 Paula AJ, Ferreira OP, Souza AG, Nepomuceno F, Andrade CE, Faria AF (2022) Machine learning and natural language processing enable a data-oriented experimental design approach for producing biochar and hydrochar from biomass. Chem Mater 34:979–990. https://doi.org/10.1021/acs.chemmater.1c02961 Potnuri R, Suriapparao DV, Sankar Rao C, Sridevi V, Kumar A (2022) Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach. Renewable Energy 197:798–809. https://doi.org/10.1016/j.renene.2022.08.006 Praveen S, Jegan J, Pushpa TB, Gokulan R (2021) Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics. Chemosphere 276:130191. https://doi.org/10.1016/j.chemosphere.2021.130191 Rathnayake N, Patel S, Halder P et al (2022) Co-pyrolysis of biosolids with alum sludge: effect of temperature and mixing ratio on product properties. J Anal Appl Pyrolysis. https://doi.org/10.1016/j.jaap.2022.105488 Ravindiran G, Saravanan P, Alagumalai A, Subbarayan S (2022) Soft computing-based models and decolorization of Reactive Yellow 81 using Ulva Prolifera biochar. Chemosphere 287:132368. https://doi.org/10.1016/j.chemosphere.2021.132368 Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM (2021) Spatial modeling of asthma-prone areas using remote sensing and ensemble machine learning algorithms. Remote Sens-Basel 13:3222. https://doi.org/10.3390/rs13163222 Reddy BS, Maurya AK, Sathishkumar V et al (2021) Prediction of batch sorption of barium and strontium from saline water. Environ Res. https://doi.org/10.1016/j.envres.2021.111107 Ro KS (2016) Kinetics and energetics of producing animal manure-based biochar. Bioenergy Res 9:447–453. https://doi.org/10.1007/s12155-016-9724-4 Rovira M, Engvall K, Duwig C (2022) Identifying key features in reactive flows: a tutorial on combining dimensionality reduction, unsupervised clustering, and feature correlation. Chem Eng J 438:135250. https://doi.org/10.1016/j.cej.2022.135250 Salawu OA, Han Z, Adeleye AS (2022) Shrimp waste-derived porous carbon adsorbent: performance, mechanism, and application of machine learning. J Hazard Mater 437:129266. https://doi.org/10.1016/j.jhazmat.2022.129266 Sayin B, Krivosheev E, Yang J et al (2021) A review and experimental analysis of active learning over crowdsourced data. Artif Intell Rev 54:5283–5305 Schleder GR, Padilha ACM, Acosta CM, Costa M, Fazzio A (2019) From DFT to machine learning: recent approaches to materials science–a review. J Phys Mater. https://doi.org/10.1088/2515-7639/ab084b Selvam SM, Balasubramanian P (2022) Influence of biomass composition and microwave pyrolysis conditions on biochar yield and its properties: a machine learning approach. Bioenergy Res. https://doi.org/10.1007/s12155-022-10447-9 Shahbaz M, AlNouss A, Ghiat I et al (2021) A comprehensive review of biomass based thermochemical conversion technologies integrated with CO2 capture and utilisation within BECCS networks. Conserv Recycl. https://doi.org/10.1016/j.resconrec.2021.105734 Shima H, Sato Y, Sakata K, Asakura T, Kikuchi J (2022) Identifying a correlation among qualitative non-numeric parameters in natural fish microbe dataset using machine learning. Appl Sci-Basel. https://doi.org/10.3390/app12125927 Sohail A, Arif F (2020) Supervised and unsupervised algorithms for bioinformatics and data science. Prog Biophys Mol Biol 151:14–22. https://doi.org/10.1016/j.pbiomolbio.2019.11.012 Somarowthu S, Yang HY, Hidebrand DGC, Ondrechen MJ (2011) High-performance prediction of functional residues in proteins with machine learning and computed input features. Biopolymers 95:390–400. https://doi.org/10.1002/bip.21589 Song ZX, Yang XL, Xu ZL, King I (2022) Graph-based semi-supervised learning: a comprehensive review. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/Tnnls.2022.3155478 Sun Y, Zhang YY, Lu L et al (2022) The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil. Sci Total Environ 829:154668. https://doi.org/10.1016/j.scitotenv.2022.154668 Suvarna M, Araujo TP, Perez-Ramirez J (2022) A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation. Appl Catal B-Environ 315:121530. https://doi.org/10.1016/j.apcatb.2022.121530 Talebkeikhah F, Rasam S, Talebkeikhah M, Torkashvand M, Salimi A, Moraveji MK (2020) Investigation of effective processes parameters on lead(II) adsorption from wastewater by biochar in mild air oxidation pyrolysis process. Int J Environ Anal Chem. https://doi.org/10.1080/03067319.2020.1777291 Tan LS, Ma ZH, Yang KQ et al (2020) Effect of three artificial aging techniques on physicochemical properties and Pb adsorption capacities of different biochars. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.134223 Tan YH, Wan XR, Ni X et al (2022) Efficient removal of Cd(II) from aqueous solution by chitosan modified kiwi branch biochar. Chemosphere. https://doi.org/10.1016/j.chemosphere.2021.133251 Tanaka A (2015) Analyses on empirical error minimzation in multiple kernel regressors. 40th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 2015 Apr 19–24. Brisbane, AUSTRALIA, pp. 2046–2050 Tang SF, Zhou H, Tan WT et al (2022) Adsorption characteristics and mechanisms of Fe-Mn oxide modified biochar for Pb(II) in wastewater. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph19148420 Tee JX, Selvarajoo A, Arumugasamy SK (2022) Prediction of carbon sequestration of biochar produced from biomass pyrolysis by artificial neural network. J Environ Chem Eng. https://doi.org/10.1016/j.jece.2022.107640 Thiruvengadam S, Edmund Murphy M, Tan JS (2021) Mathematically modelling pyrolytic polygeneration processes using artificial intelligence. Fuel. https://doi.org/10.1016/j.fuel.2021.120488 Thomas E, Borchard N, Sarmiento C, Atkinson R, Ladd B (2020) Key factors determining biochar sorption capacity for metal contaminants: a literature synthesis. Biochar 2:151–163. https://doi.org/10.1007/s42773-020-00053-3 Tomczyk A, Sokolowska Z, Boguta P (2020) Biochar physicochemical properties: Pyrolysis temperature and feedstock kind effects. Rev Environ Sci Bio 19:191–215. https://doi.org/10.1007/s11157-020-09523-3 Tran HQ, Ha C (2022) Machine learning in indoor visible light positioning systems: a review. Neurocomputing 491:117–131. https://doi.org/10.1016/j.neucom.2021.10.123 Tseng KK, Zhang YQ, Zhu QL, Yung KL, Ip WH (2020) Semi-supervised image depth prediction with deep learning and binocular algorithms. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106272 Tyagi U (2022) Enhanced adsorption of metal ions onto Vetiveria zizanioides biochar via batch and fixed bed studies. Bioresour Technol. https://doi.org/10.1016/j.biortech.2021.126475 Uddin MG, Nash S, Rahman A, Olbert AI (2022) A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment. Water Res 219:118532. https://doi.org/10.1016/j.watres.2022.118532 Van Calster B, Vickers AJ (2015) Calibration of risk prediction models: impact on decision-analytic performance. Med Decis Making 35:162–169. https://doi.org/10.1177/0272989x14547233 Wan S, Zhao XB, Niu ZH et al (2022) Influence of ambient air pollution on successful pregnancy with frozen embryo transfer: a machine learning prediction model. Ecotoxicol Environ Saf 236:113444. https://doi.org/10.1016/j.ecoenv.2022.113444 Wang B, Yu J, Liao H, Zhu WK, Ding PP, Zhou J (2020a) Adsorption of lead(II) from aqueous solution with high efficiency by hydrothermal biochar derived from honey. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph17103441 Wang L, O’Connor D, Rinklebe J et al (2020b) Biochar aging: mechanisms, physicochemical changes, assessment, and implications for field applications. Environ Sci Technol 54:14797–14814. https://doi.org/10.1021/acs.est.0c04033 Wang XM, Gao JH, Long MS, Wang JM (2021) Self-tuning for data-efficient deep learning. Int Conf Mach Learn 139(139):7748–7759 Wang W, Wu X, Long S (2022a) Optimizing the methylene blue removal from aqueous solution using pomelo peel based biochar assisted by RSM and ANN-PSO. Pol J Environ Stud 31:329–346. https://doi.org/10.15244/pjoes/137947 Wang ZW, Yang Z, Liu LJ, Ye YP, Xie XY (2022b) Electron transfer mechanism of chitosan-modified natural manganese ore-cornstalk biochar composites with activated peroxymonosulfate: the role of functional groups on the surface of biochar-based composites. Sep Purif Technol. https://doi.org/10.1016/j.seppur.2022.122107 Wani I, Sharma A, Kushvaha V, Madhushri P, Peng L (2020) Effect of pH, volatile content, and pyrolysis conditions on surface area and O/C and H/C ratios of biochar: towards understanding performance of biochar using simplified approach. J Hazard Toxic Radioact Waste. https://doi.org/10.1061/(asce)hz.2153-5515.0000545 Wei N, Zhang QJ, Zhang YJ et al (2022) Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: model development, evaluation and interpretation. Environ Int 158:106977. https://doi.org/10.1016/j.envint.2021.106977 Wong YJ, Arumugasamy SK, Chung CH, Selvarajoo A, Sethu V (2020) Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu(II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel. Environ Monit Assess 192:439. https://doi.org/10.1007/s10661-020-08268-4 Wu Y, Zhang PY, Zeng GM et al (2016) Enhancing sewage sludge dewaterability by a skeleton builder: Biochar produced from sludge cake conditioned with rice husk flour and FeCl3. ACS Sustain Chem Eng 4:5711–5717. https://doi.org/10.1021/acssuschemeng.6b01654 Wu LP, Wei CB, Zhang SR, Wang YD, Kuzyakov Y, Ding XD (2019) MgO-modified biochar increases phosphate retention and rice yields in saline-alkaline soil. J Cleaner Prod 235:901–909. https://doi.org/10.1016/j.jclepro.2019.07.043 Wu LR, Yuan LF, Zhao GJ, Lin HT, Li SZ (2022a) Deep clustering and visualization for end-to-end high-dimensional data analysis. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/Tnnls.2022.3151498 Wu ML, Liu B, Li J, Su XT, Liu WZ, Li XQ (2022b) Influence of pyrolysis temperature on sludge biochar: the ecological risk assessment of heavy metals and the adsorption of Cd(II). Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-22827-x Xia R, Wang G, Zhang Y et al (2020) River algal blooms are well predicted by antecedent environmental conditions. Water Res 185:116221. https://doi.org/10.1016/j.watres.2020.116221 Xie YQ, Wang L, Li HL et al (2022) A critical review on production, modification and utilization of biochar. J Anal Appl Pyrol. https://doi.org/10.1016/j.jaap.2021.105405 Xu XT, Wang C, Gui BX et al (2022) Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor. Environ Res 212:113175. https://doi.org/10.1016/j.envres.2022.113175 Yadav K, Jagadevan S (2021) Influence of torrefaction and pyrolysis on engineered biochar and its applicability in defluoridation: insight into adsorption mechanism, batch adsorber design and artificial neural network modelling. J Anal Appl Pyrolysis. https://doi.org/10.1016/j.jaap.2021.105015 Yadav K, Raphi M, Jagadevan S (2021) Adsorption of copper(II) on chemically modified biochar: a single-stage batch adsorber design and predictive modeling through artificial neural network. Biomass Convers Biorefin. https://doi.org/10.1007/s13399-021-01494-x Yan XL, Zhang J, Russo DP, Zhu H, Yan B (2020) Prediction of nano-bio interactions through convolutional neural network analysis of nanostructure images. ACS Sustain Chem Eng 8:19096–19104. https://doi.org/10.1021/acssuschemeng.0c07453 Yang WY, Liang H, Peng F, Liu ZL, Liu J, Qiao ZW (2019) Computational screening of metal-organic framework membranes for the separation of 15 gas mixtures. Nanomaterials 9:467. https://doi.org/10.3390/nano9030467 Yang W, Shang JY, Li BG, Flury M (2020) Surface and colloid properties of biochar and implications for transport in porous media. Crit Rev Environ Sci Technol 50:2484–2522. https://doi.org/10.1080/10643389.2019.1699381 Yang XC, Nguyen XC, Tran QB et al (2022a) Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water. Environ Res. https://doi.org/10.1016/j.envres.2022.113953 Yang YD, Piao YX, Wang RF, Su YM, Qiu JR, Liu N (2022b) Mechanism of biochar functional groups in the catalytic reduction of tetrachloroethylene by sulfides. Environ Pollut. https://doi.org/10.1016/j.envpol.2022.118921 Yau KLA, Qadir J, Khoo HL, Ling MH, Komisarczuk P (2017) A survey on reinforcement learning models and algorithms for traffic signal control. Acm Comput Surv 50:34. https://doi.org/10.1145/3068287 Ye Y, Shi J, Zhu D et al (2021) Management of medical and health big data based on integrated learning-based health care system: a review and comparative analysis. Comput Meth Prog Bio 209:106293 Yildirim H, Ozkale MR (2021) LL-ELM: a regularized extreme learning machine based on L-1-norm and Liu estimator. Neural Comput Appl 33:10469–10484. https://doi.org/10.1007/s00521-021-05806-0 Yu F, Sun L, Zhou YM et al (2016) Biosorbents based on agricultural wastes for ionic liquid removal: an approach to agricultural wastes management. Chemosphere 165:94–99. https://doi.org/10.1016/j.chemosphere.2016.08.133 Yu A, Liu Y, Li X, Yang Y, Zhou Z, Liu H (2021) Modeling and optimizing of NH4+ removal from stormwater by coal-based granular activated carbon using RSM and ANN coupled with GA. Water. https://doi.org/10.3390/w13050608 Yuan LH, Li C, Cao JT, Zhao QB (2020) Rank minimization on tensor ring: an efficient approach for tensor decomposition and completion. Mach Learn 109:603–622. https://doi.org/10.1007/s10994-019-05846-7 Zafar M, Van Vinh N, Behera SK, Park HS (2017) Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach. J Environ Sci (china) 54:114–125. https://doi.org/10.1016/j.jes.2016.06.008 Zeng ZW, Tan XF, Liu YG et al (2018) Comprehensive adsorption studies of doxycycline and ciprofloxacin antibiotics by biochars prepared at different temperatures. Front Chem. https://doi.org/10.3389/fchem.2018.00080 Zhang D, Jiao LC, Bai X, Wang S, Hou B (2018) A robust semi-supervised SVM via ensemble learning. Appl Soft Comput 65:632–643. https://doi.org/10.1016/j.asoc.2018.01.038 Zhang K, Zhong SF, Zhang HC (2020) Predicting aqueous adsorption of organic compounds onto biochars, carbon nanotubes, granular activated carbons, and resins with machine learning. Environ Sci Technol 54:7008–7018. https://doi.org/10.1021/acs.est.0c02526 Zhang HK, Wang ZL, Cai JF, Wu SC, Li JJ (2021a) Machine-learning-enabled tricks of the trade for rapid host material discovery in Li-S battery. ACS Appl Mater Inter 13:53388–53397. https://doi.org/10.1021/acsami.1c10749 Zhang X, Zheng HH, Wu J et al (2021b) Physicochemical and adsorption properties of biochar from biomass-based pyrolytic polygeneration: effects of biomass species and temperature. Biochar 3:657–670. https://doi.org/10.1007/s42773-021-00102-5 Zhang H, Li Y, Xie R et al (2022a) A particle scale micro-CT approach for 3D in-situ visualizing the Pb(II) adsorption in different crop residue-derived chars. Bioresour Technol 344:126269. https://doi.org/10.1016/j.biortech.2021.126269 Zhang W, Huang W, Tan J, Guo Q, Wu B (2022b) Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: paradigms, applications and prospects. Chemosphere. https://doi.org/10.1016/j.chemosphere.2022.136447 Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B (2022c) Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: recent progress and future perspectives. Chemosphere 311:137044. https://doi.org/10.1016/j.chemosphere.2022.137044 Zhang WA, Gu X, Tang LB, Yin YP, Liu DS, Zhang YM (2022d) Application of machine learning, deep learning and optimization algorithms in geoengineering and geoscience: comprehensive review and future challenge. Gondwana Res 109:1–17. https://doi.org/10.1016/j.gr.2022.03.015 Zhang Y, Ren M, Tang Y et al (2022e) Immobilization on anionic metal(loid)s in soil by biochar: a meta-analysis assisted by machine learning. J Hazard Mater. https://doi.org/10.1016/j.jhazmat.2022.129442 Zhao Y, Li Y, Fan D, Song J, Yang F (2021) Application of kernel extreme learning machine and Kriging model in prediction of heavy metals removal by biochar. Bioresour Technol 329:124876. https://doi.org/10.1016/j.biortech.2021.124876 Zhao Y, Fan D, Li Y, Yang F (2022) Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin. Environ Res 208:112694. https://doi.org/10.1016/j.envres.2022.112694 Zheng X, Nguyen H (2022) A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm. Chemosphere 287:132251. https://doi.org/10.1016/j.chemosphere.2021.132251 Zhou X, Zhao J, Chen M, Zhao G, Wu S (2022) Influence of catalyst and solvent on the hydrothermal liquefaction of woody biomass. Bioresour Technol 346:126354. https://doi.org/10.1016/j.biortech.2021.126354 Zhu X, Wang X, Ok YS (2019a) The application of machine learning methods for prediction of metal sorption onto biochars. J Hazard Mater 378:120727. https://doi.org/10.1016/j.jhazmat.2019.06.004 Zhu XZ, Li YN, Wang XN (2019b) Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresour Technol. https://doi.org/10.1016/j.biortech.2019.121527 Zhu X, He M, Sun Y et al (2022a) Insights into the adsorption of pharmaceuticals and personal care products (PPCPs) on biochar and activated carbon with the aid of machine learning. J Hazard Mater 423:127060. https://doi.org/10.1016/j.jhazmat.2021.127060 Zhu X, Xu Z, You S et al (2022b) Machine learning exploration of the direct and indirect roles of Fe impregnation on Cr(VI) removal by engineered biochar. Chem Eng J. https://doi.org/10.1016/j.cej.2021.131967 Zhu XF, Labianca C, He MJ et al (2022c) Life-cycle assessment of pyrolysis processes for sustainable production of biochar from agro-residues. Bioresour Technol. https://doi.org/10.1016/j.biortech.2022.127601 Zuo HF, Qin XX, Liu ZG, Fu YL (2021) Preparation and characterization of modified corn stalk biochar. BioResources 16:7427–7442. https://doi.org/10.15376/biores.16.4.7428-7443