Machine-learning-aided thermochemical treatment of biomass: a review

Document Type : Review Paper

Authors

1 School of Energy Science and Engineering, Central South University, Changsha, Hunan 410083, People’s Republic of China.

2 Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland.

Abstract

Thermochemical treatment is a promising technique for biomass disposal and valorization. Recently, machine learning (ML) has been extensively used to predict yields, compositions, and properties of biochar, bio-oil, syngas, and aqueous phases produced by the thermochemical treatment of biomass. ML demonstrates great potential to aid the development of thermochemical processes. The present review aims to 1) introduce the ML schemes and strategies as well as descriptors of the input and output features in thermochemical processes; 2) summarize and compare the up-to-date research in both ML-aided wet (hydrothermal carbonization/liquefaction/gasification) and dry (torrefaction/pyrolysis/gasification) thermochemical treatment of biomass (i.e., predicting the yields, compositions, and properties of oil/char/gas/aqueous phases as well as thermal conversion behavior or kinetics); and 3) identify the gaps and provide guidance for future studies concerning how to improve predictive performance, increase generalizability, aid mechanistic and application studies, and effectively share data and models in the community. The development of biomass thermochemical treatment processes is envisaged to be greatly accelerated by ML in the near future.

Graphical Abstract

Machine-learning-aided thermochemical treatment of biomass: a review

Highlights

  • Machine learning (ML) schemes and descriptors of input and output features were examined.
  • ML can predict yields, compositions, and properties of oil/char/gas/aqueous phases.
  • ML predictive performance for wet and dry thermochemical processes is different.
  • Improving ML predictive performance, generalizability, and application are future needs.

Keywords

References

  1. Abdulsalam, J., Lawal, A.I., Setsepu, R.L., Onifade, M., Bada, S., 2020. Application of gene expression programming, artificial neural network and multilinear regression in predicting hydrochar physicochemical properties. Bioresour. Bioprocess. 7(1), 1-22.
  2. Akiya, N., Savage, P.E., 2002. Roles of water for chemical reactions in high-temperature water. Chem. Rev. 102(8), 2725-2750.
  3. Alabdrabalnabi, A., Gautam, R., Sarathy, S.M.,  2021. Utilization of Machine Learning to Predict Bio-Oil and Biochar Yields from CoPyrolysis of Biomass with Waste Polymers. Fuel. 328, 125303.
  4. Ascher, S., Sloan, W., Watson, I., You, S., 2022a. A comprehensive artificial neural network model for gasification process prediction. Appl. Energy. 320, 119289.
  5. Ascher, S., Watson, I., You, S., 2022b. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renew. Sust. Energy Rev. 155, 111902.
  6. Ayodele, B.V., Mustapa, S.I., Kanthasamy, R., Zwawi, M., Cheng, C.K., 2021. Modeling the prediction of hydrogen production by co-gasification of plastic and rubber wastes using machine learning algorithms. Int. J. Energy Res. 45(6), 9580-9594.
  7. Bagheri, M., Esfilar, R., Golchi, M.S., Kennedy, C.A., 2019. A comparative data mining approach for the prediction of energy recovery potential from various municipal solid waste. Renew. Sust. Energy Rev. 116, 109423.
  8. Bi, Y., Chen, C., Huang, X., Wang, H., Wei, G., 2023. Discrimination method of biomass slagging tendency based on particle swarm optimization deep neural network (DNN). Energy. 262, 125368.
  9. Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., Walsh, A., 2018. Machine learning for molecular and materials science. Nature. 559(7715), 547-555.
  10. Cao, H., Milan, Y.J., Mood, S.H., Ayiania, M., Zhang, S., Gong, X., Lora, E.E.S., Yuan, Q., Garcia-Perez, M., 2021. A novel elemental composition based prediction model for biochar aromaticity derived from machine learning. Artif. Intell. Agric. 5, 133-141.
  11. Castro Garcia, A., Shuo, C., Cross, J.S., 2022. Machine learning based analysis of reaction phenomena in catalytic lignin depolymerization. Bioresour. Technol. 345, 126503.
  12. Chen, J., Wang, P., Ding, L., Yu, T., Leng, S., Chen, J., Fan, L., Li, J., Wei, L., Li, J., Lu, Q., Leng, L., Zhou, W., 2021. The comparison study of multiple biochar stability assessment methods. J. Anal. Appl. Pyrolysis. 156, 105070.
  13. Chen, C., Liang, R., Ge, Y., Li, J., Yan, B., Cheng, Z., Tao, J., Wang, Z., Li, M., Chen, G., 2022a. Fast characterization of biomass pyrolysis oil via combination of ATR-FTIR and machine learning models. Renewable Energy. 194, 220-231.
  14. Chen, J., Zhang, J., Pan, W., An, G., Deng, Y., Li, Y., Hu, Y., Xiao, Y., Liu, T., Leng, S., Chen, Jiefeng, Li, J., Peng, H., Leng, L., Zhou, W., 2022b. A novel strategy to simultaneously enhance bio-oil yield and nutrient recovery in sequential hydrothermal liquefaction of high protein microalgae. Energy Convers. Manage. 255, 115330.
  15. Chen, J., Ding, L., Wang, P., Zhang, W., Li, J., A. Mohamed, B.A., Chen, J., Leng, S., Liu, T., Leng, L., Zhou, W., 2022c. The estimation of the higher heating value of biochar by data-driven modeling. J. Renew. Mater. 10, 1555-1574.
  16. Chen, W.H., Aniza, R., Arpia, A.A., Lo, H.J., Hoang, A.T., Goodarzi, V., Gao, J., 2022d. A comparative analysis of biomass torrefaction severity index prediction from machine learning. Appl. Energy. 324, 119689.
  17. Chen, W.H., Lo, H.J., Aniza, R., Lin, B.J., Park, Y.K., Kwon, E.E., Sheen, H.K., Alan, L., Grafilo, L.A.D.R., 2022e. Forecast of glucose production from biomass wet torrefaction using statistical approach along with multivariate adaptive regression splines , neural network and decision tree. Appl. Energy. 324, 119775.
  18. Chen, X., Zhang, H., Song, Y., Xiao, R., 2018. Prediction of product distribution and bio-oil heating value of biomass fast pyrolysis. Chem. Eng. Process.-Process Intensif. 130, 36-42.
  19. Cheng, F., Belden, E.R., Li, W., Shahabuddin, M., Paffenroth, R.C., Timko, M.T., 2022. Accuracy of predictions made by machine learned models for biocrude yields obtained from hydrothermal liquefaction of organic wastes. Chem. Eng. J. 442, 136013.
  20. Cheng, F., Luo, H., Colosi, L.M., 2020a. Slow pyrolysis as a platform for negative emissions technology: an integration of machine learning models, life cycle assessment, and economic analysis. Energy Convers. Manage. 223, 113258.
  21. Cheng, F., Porter, M.D., Colosi, L.M., 2020b. Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology?. Energy Convers. Manage. 203, 112252.
  22. Chong, J.W., Thangalazhy-Gopakumar, S., Tan, R.R., Aviso, K.B., Chemmangattuvalappil, N.G., 2022. Estimation of fast pyrolysis bio-oil properties from feedstock characteristics using rough-set-based machine learning. Int. J. Energy Res. 46(13), 19159-19176.
  23. Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn. 20, 273-297.
  24. da Silva, C.M.S., Carneiro, A.D.C.O., Vital, B.R., Figueiró, C.G., de Freitas Fialho, L., de Magalhães, M.A., Carvalho, A.G., Cândido, W.L., 2018. Biomass torrefaction for energy purposes-definitions and an overview of challenges and opportunities in Brazil. Renew. Sust. Energy Rev. 82, 2426-2432.
  25. Dai, L., Wang, Y., Liu, Y., Ruan, R., He, C., Yu, Z., Jiang, L., Zeng, Z., Tian, X., 2019. Integrated process of lignocellulosic biomass torrefaction and pyrolysis for upgrading bio-oil production: a state-of-the-art review. Renew. Sust. Energy Rev. 107, 20-36.
  26. Ding, R., Wang, R., Ding, Y., Yin, W., Liu, Y., Li, J., Liu, J., 2020. Designing ai-aided analysis and prediction models for nonprecious metal electrocatalyst-based proton-exchange membrane fuel cells. Angew. Chemie. 132(43), 19335-19345.
  27. Djandja, O.S., Duan, P.G., Yin, L.X., Wang, Z.C., Duo, J., 2021. A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge. Energy. 232, 121010.
  28. Djandja, O.S., Salami, A.A., Wang, Z.C., Duo, J., Yin, L.X., Duan, P.G., 2022. Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge. Energy. 245, 123295.
  29. Elmaz, F., Yücel, Ö., 2020. Data-driven identification and model predictive control of biomass gasification process for maximum energy production. Energy. 195, 117037.
  30. Elmaz, F., Yücel, Ö., Mutlu, A.Y., 2020. Predictive modeling of biomass gasification with machine learning-based regression methods. Energy. 191, 116541.
  31. Fan, L., Zhang, H., Li, Jingjing, Wang, Y., Leng, L., Li, Jun, Yao, Y., Lu, Q., Yuan, W., Zhou, W., 2020. Algal biorefinery to value-added products by using combined processes based on thermochemical conversion: a review. Algal Res. 47, 101819.
  32. Freitas, R.S., Lima, Á.P., Chen, C., Rochinha, F.A., Mira, D., 2022. Towards predicting liquid fuel physicochemical properties using molecular dynamics guided machine learning models. Fuel. 329, 125415.
  33. Ge, S., Shi, Y., Xia, C., Huang, Z., Manzo, M., Cai, L., Ma, H., Zhang, S., Jiang, J., Sonne, C., Lam, S.S., 2021. Progress in pyrolysis conversion of waste into value-added liquid pyro-oil, with focus on heating source and machine learning analysis. Energy Convers. Manage. 245, 114638.
  34. Ghugare, S.B., Tiwary, S., Tambe, S.S., 2017. Computational intelligence based models for prediction of elemental composition of solid biomass fuels from proximate analysis. Int. J. Syst. Assur. Eng. Manage. 8, 2083-2096.
  35. Gu, C., Wang, X., Song, Q., Li, H., Qiao, Y., 2021. Prediction of gas‐liquid‐solid product distribution after solid waste pyrolysis process based on artificial neural network model. Int. J. Energy Res. 45(9), 13786-13800.
  36. Haq, Z.U., Ullah, H., Khan, M.N.A., Naqvi, S.R., Ahsan, M., 2022. Hydrogen production optimization from sewage sludge supercritical gasification process using machine learning methods integrated with genetic algorithm. Chem. Eng. Res. Des. 184, 614-626.
  37. He, C., Chen, C.L., Giannis, A., Yang, Y., Wang, J.Y., 2014. Hydrothermal gasification of sewage sludge and model compounds for renewable hydrogen production: a review. Renew. Sust. Energy Rev. 39, 1127-1142.
  38. Hough, B.R., Beck, D.A., Schwartz, D.T., Pfaendtner, J., 2017. Application of machine learning to pyrolysis reaction networks: reducing model solution time to enable process optimization. Comput. Chem. Eng. 104, 56-63.
  39. Hu, Z., Yuan, Y., Li, X., Tu, Z., Dacres, O.D., Zhu, Y., Shi, L., Hu, H., Liu, H., Luo, G., Yao, H., 2022. Yield prediction of “thermal-dissolution based carbon enrichment” treatment on biomass wastes through coupled model of artificial neural network and AdaBoost. Bioresour. Technol. 343, 126083.
  40. Huang, H., Yuan, X., 2015. Recent progress in the direct liquefaction of typical biomass. Prog. Energy Combust. Sci. 49, 59-80.
  41. Huang, H.J., Yuan, X.Z., Zhu, H.N., Li, H., Liu, Y., Wang, X.L., Zeng, G.M., 2013. Comparative studies of thermochemical liquefaction characteristics of microalgae, lignocellulosic biomass and sewage sludge. Energy. 56, 52-60.
  42. Ippolito, J.A., Cui, L., Kammann, C., Wrage-Mönnig, N., Estavillo, J.M., Fuertes-Mendizabal, T., Cayuela, M.L., Sigua, G., Novak, J., Spokas, K., Borchard, N., 2020. Feedstock choice, pyrolysis temperature and type influence biochar characteristics: a comprehensive meta-data analysis review. Biochar. 2, 421-438.
  43. Kambo, H.S., Dutta, A., 2015. A comparative review of biochar and hydrochar in terms of production, physico-chemical properties and applications. Renew. Sust. Energy Rev. 45, 359-378.
  44. Kan, T., Strezov, V., Evans, T.J., 2016. Lignocellulosic biomass pyrolysis: a review of product properties and effects of pyrolysis parameters. Renew. Sust. Energy Rev. 57, 1126-1140.
  45. Kanaujia, P.K., Sharma, Y.K., Garg, M.O., Tripathi, D., Singh, R., 2014. Review of analytical strategies in the production and upgrading of bio-oils derived from lignocellulosic biomass. J. Anal. Appl. Pyrolysis 105, 55-74.
  46. Kartal, F., Özveren, U., 2022a. Prediction of torrefied biomass properties from raw biomass. Renewable Energy. 182, 578-591.
  47. Kartal, F., Özveren, U., 2022b. Investigation of the chemical exergy of torrefied biomass from raw biomass by means of machine learning. Biomass Bioenergy. 159, 106383.
  48. Kartal, F., Özveren, U., 2022c. Prediction of activation energy for combustion and pyrolysis by means of machine learning. Therm. Sci. Eng. Prog. 33, 101346.
  49. Kartal, F., Özveren, U., 2021. An improved machine learning approach to estimate hemicellulose, cellulose, and lignin in biomass. Carbohydr. Polym. Technol. Appl. 2, 100148.
  50. Katongtung, T., Onsree, T., Tippayawong, N., 2022. Machine learning prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes. Bioresour. Technol. 344, 126278.
  51. Ke, B., Nguyen, H., Bui, X.N., Bui, H.B., Choi, Y., Zhou, J., Moayedi, H., Costache, R., Nguyen-Trang, T., 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.
  52. Ke, B., Nguyen, H., Bui, X.N., Bui, H.B., 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.
  53. Khan, M., Ullah, Z., Mašek, O., Naqvi, S.R., Khan, M.N.A., 2022. Artificial neural networks for the prediction of biochar yield: a comparative study of metaheuristic algorithms. Bioresour. Technol. 355, 127215.
  54. Kusdhany, M.I.M., Lyth, S.M., 2021. New insights into hydrogen uptake on porous carbon materials via explainable machine learning. Carbon. 179, 190-201.
  55. Lecun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature. 521(7553), 436-444.
  56. Lee, B.H., Thieu Trinh, V., Moon, H. Bin, Lee, J.H., Kim, H.T., Lee, J.W., Jeon, C.H., 2023. Physicochemical properties and pyrolysis behavior of petcoke with artificial neural network modeling. Fuel. 331, 125735.
  57. Leng, , Yuan, X., Huang, H., Jiang, H., Chen, X., Zeng, G., 2014. The migration and transformation behavior of heavy metals during the liquefaction process of sewage sludge. Bioresour. Technol. 167, 144-150.
  58. Leng, L., Yuan, X., Huang, H., Peng, X., Chen, H., Wang, H., Wang, L., Chen, X., Zeng, G., 2015. Distribution behavior and risk assessment of metals in bio-oils produced by liquefaction/pyrolysis of sewage sludge. Environ. Sci. Pollut. Res. 22, 18945-18955.
  59. Leng, L., Zhou, W., 2018a. Chemical compositions and wastewater properties of aqueous phase (wastewater) produced from the hydrothermal treatment of wet biomass: a review. Energy Sources, Part A. 40(22), 2648-2659.
  60. Leng, L., Leng, S., Chen, J., Yuan, X., Li, J., Li, K., Wang, Y., Zhou, W., 2018b. The migration and transformation behavior of heavy metals during co-liquefaction of municipal sewage sludge and lignocellulosic biomass. Bioresour. Technol. 259, 156-163.
  61. Leng, L., Li, H., Yuan, X., Zhou, W., Huang, H., 2018c. Bio-oil upgrading by emulsification/microemulsification: a review. Energy. 161, 214-232.
  62. Leng, L., Li, J., Wen, Z., Zhou, W., 2018d. Use of microalgae to recycle nutrients in aqueous phase derived from hydrothermal liquefaction process. Bioresour. Technol. 256, 529-542.
  63. Leng, L., Bogush, A.A., Roy, A., Stegemann, J.A., 2019a. Characterisation of ashes from waste biomass power plants and phosphorus recovery. Sci. Total Environ. 690, 573-583.
  64. Leng, L., Huang, H., Li, H., Li, J., Zhou, W., 2019b. Biochar stability assessment methods: a review. Sci. Total Environ. 647, 210-222.
  65. Leng, L., Xu, X., Wei, L., Fan, L., Huang, H., Li, Jianan, Lu, Q., Li, Jun, Zhou, W., 2019c. Biochar stability assessment by incubation and modelling: methods, drawbacks and recommendations. Sci. Total Environ. 664, 11-23.
  66. Leng, L., Li, W., Li, H., Jiang, S., Zhou, W., 2020a. Cold Flow Properties of Biodiesel and the Improvement Methods: a Review. Energy Fuels. 34(9), 10364-10383.
  67. Leng, L., Xu, S., Liu, R., Yu, T., Zhuo, X., Leng, S., Xiong, Q., Huang, H., 2020b. Nitrogen containing functional groups of biochar: an overview. Bioresour. Technol. 298, 122286.
  68. Leng, L., Yang, L., Chen, J., Leng, S., Li, H., Li, H., Yuan, X., Zhou, W., Huang, H., 2020c. A review on pyrolysis of protein-rich biomass: Nitrogen transformation. Bioresour. Technol. 315, 123801.
  69. Leng, L., Zhang, W., Leng, S., Chen, J., Yang, L., Li, H., Jiang, S., Huang, H., 2020d. Bioenergy recovery from wastewater produced by hydrothermal processing biomass: progress, challenges, and opportunities. Sci. Total Environ. 748, 142383.
  70. Leng, L., Zhang, W., Peng, H., Li, H., Jiang, S., Huang, H., 2020e. Nitrogen in bio-oil produced from hydrothermal liquefaction of biomass: a review. Chem. Eng. J. 401, 126030.
  71. Leng, S., Leng, L., Chen, L., Chen, J., Chen, J., Zhou, W., 2020f. The effect of aqueous phase recirculation on hydrothermal liquefaction/carbonization of biomass: a review. Bioresour. Technol. 318, 124081.
  72. Leng, L., Xiong, Q., Yang, L., Li, H., Zhou, Y., Zhang, W., Jiang, S., Li, H., Huang, H., 2021a. An overview on engineering the surface area and porosity of biochar. Sci. Total Environ. 763, 144204.
  73. Leng, L., Yang, L., Chen, J., Hu, Y., Li, H., Li, H., Jiang, S., Peng, H., Yuan, X., Huang, H., 2021b. Valorization of the aqueous phase produced from wet and dry thermochemical processing biomass : a review. J. Clean. Prod. 294, 126238.
  74. Leng, L., Yang, L., Leng, S., Zhang, W., Zhou, Y., Peng, H., Li, Hui, Hu, Y., Jiang, S., Li, Hailong, 2021c. A review on nitrogen transformation in hydrochar during hydrothermal carbonization of biomass containing nitrogen. Sci. Total Environ. 756, 143679.
  75. Leng, E., He, B., Chen, J., Liao, G., Ma, Y., Zhang, F., Liu, S., Jiaqiang, E., 2021d. Prediction of three-phase product distribution and bio-oil heating value of biomass fast pyrolysis based on machine learning. Energy. 236, 121401.
  76. Leng, L., Liu, R., Xu, S., Mohamed, B.A., Yang, Z., Hu, Y., Chen, J., Zhao, S., Wu, Z., Peng, H., Li, Hui, Li, Hailong, 2022a. An overview of sulfur-functional groups in biochar from pyrolysis of biomass. J. Environ. Chem. Eng. 10(2), 107185.
  77. Leng, L., Yang, L., Lei, X., Zhang, W., Ai, Z., Yang, Z., Zhan, H., Yang, J., Yuan, X., Peng, H., Li, H., 2022b. Machine learning predicting and engineering the yield, N content, and specific surface area of biochar derived from pyrolysis of biomass. Biochar. 4(1), 63.
  78. Leng, L., Zhang, W., Chen, Q., Zhou, J., Peng, H., Zhan, H., Li, H., 2022c. Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass. Bioresour. Technol. 362, 127791.
  79. Leng, L., Zhang, W., Liu, T., Zhan, H., Li, J., Yang, L., Li, J., Peng, H., Li, H., 2022d. Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass. Bioresour. Technol. 358, 127348.
  80. Li, H., Wang, S., Yuan, X., Xi, Y., Huang, Z., Tan, M., Li, C., 2018a. The effects of temperature and color value on hydrochars’ properties in hydrothermal carbonization. Bioresour. Technol. 249, 574-581.
  81. Li, L., Wang, Y., Xu, J., Flora, J.R., Hoque, S., Berge, N.D., 2018b. Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content. Bioresour. Technol. 262, 284-293.
  82. Li, J., Pan, L., Suvarna, M., Tong, Y.W., Wang, X., 2020. Fuel properties of hydrochar and pyrochar: prediction and exploration with machine learning. Appl. Energy. 269, 115166.
  83. Li, J., Pan, L., Suvarna, M., Wang, X., 2021a. Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening. Chem. Eng. J. 426, 131285.
  84. Li, J., Suvarna, M., Pan, L., Zhao, Y., Wang, X., 2021b. A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification. Appl. Energy. 304, 117674.
  85. Li, J., Zhang, W., Liu, T., Yang, L., Li, H., Peng, H., Jiang, S., Wang, X., Leng, L., 2021c. Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification. Chem. Eng. J. 425, 130649.
  86. Li, J., Zhu, X., Li, Y., Tong, Y.W., Ok, Y.S., Wang, X., 2021d. Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: application of machine learning on waste-to-resource. J. Clean. Prod. 278, 123928.
  87. Li, J., Suvarna, M., Li, L., Pan, L., Pérez-Ramírez, J., Ok, Y.S., Wang, X., 2022a. A review of computational modeling techniques for wet waste valorization: research trends and future perspectives. J. Clean. Prod. 367, 133025.
  88. Li, Y., Gupta, R., You, S., 2022b. Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass. Bioresour. Technol. 359, 127511.
  89. Li, H., Ai, Z., Yang, L., Zhang, W., Yang, Z., Peng, H., Leng, L., 2023. Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar. Bioresour. Technol. 369, 128417.
  90. Liao, M., Kelley, S.S., 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(4), 1015-1027.
  91. Liao, M., Yao, Y., 2021. Applications of artificial intelligence-based modeling for bioenergy systems: a review. GCB Bioenergy. 13(5), 774-802.
  92. Liu, W.J., Jiang, H., Yu, H.Q., 2015. Development of biochar-based functional materials: toward a sustainable platform carbon material. Chem. Rev. 115(22), 12251-12285.
  93. Mahmood, A., Wang, J.L., 2021. Machine learning for high performance organic solar cells: current scenario and future prospects. Energy Environ. Sci. 14(1), 90-105.
  94. Manatura, K., Chalermsinsuwan, B., Kaewtrakulchai, N., Kwon, E.E., Chen, W.H., 2023. Machine learning and statistical analysis for biomass torrefaction: a review. Bioresour. Technol. 369, 128504.
  95. Mari Selvam, S., Balasubramanian, P., 2022. Influence of biomass composition and microwave pyrolysis conditions on biochar yield and its properties: a machine learning approach. Bioenergy Res.
  96. Meena, M., Shubham, S., Paritosh, K., Pareek, N., Vivekanand, V., 2021. Production of biofuels from biomass: predicting the energy employing artificial intelligence modelling. Bioresour. Technol. 340, 125642.
  97. Mohamed, B.A., Bilal, M., Salama, E.S., Periyasamy, S., Fattah, I.R., Ruan, R., Awasthi, M.K., Leng, L., 2022. Phenolic-rich bio-oil production by microwave catalytic pyrolysis of switchgrass: experimental study , life cycle assessment , and economic analysis. J. Clean. Prod. 366, 132668.
  98. Molino, A., Chianese, S., Musmarra, D., 2016. Biomass gasification technology: the state of the art overview. J. Energy Chem. 25(1), 10-25.
  99. Mu, L., Wang, Z., Wu, D., Zhao, L., Yin, H., 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.
  100. Mutlu, A.Y., Yucel, O., 2018. An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification. Energy. 165, 895-901.
  101. Ögren, Y., Tóth, P., Garami, A., Sepman, A., Wiinikka, H., 2018. Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors. Appl. Energy. 226, 450-460.
  102. Olatunji, o.o., akinlabi, s., madushele, n., adedeji, p.a., 2019. estimation of the elemental composition of biomass using hybrid adaptive neuro-fuzzy inference system. Bioenergy Res. 12, 642-652.
  103. Onsree, T., Tippayawong, N., 2021. Machine learning application to predict yields of solid products from biomass torrefaction. Renewable Energy. 167, 425-432.
  104. Ozbas, E.E., Aksu, D., Ongen, A., Aydin, M.A., Ozcan, H.K., 2019. Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms. Int. J. Hydrogen Energy. 44(32), 17260-17268.
  105. Palansooriya, K.N., Li, J., Dissanayake, P.D., Suvarna, M., Li, L., Yuan, X., Sarkar, B., Tsang, D.C.W., Rinklebe, J., Wang, X., Ok, Y.S., 2022. Prediction of soil heavy metal immobilization by biochar using machine learning. Environ. Sci. Technol. 56(7), 4187-4198.
  106. Pathy, A., Meher, S., Balasubramanian, P., 2020. Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods. Algal Res. 50, 102006.
  107. Pavlov, Y.L., 2001. Random forests. De Gruyter. Berlin, Boston.
  108. Peng, C., Zhai, Y., Zhu, Y., Wang, Tengfei, Xu, B., Wang, Tao, Li, C., Zeng, G., 2017. Investigation of the structure and reaction pathway of char obtained from sewage sludge with biomass wastes, using hydrothermal treatment. J. Clean. Prod. 166, 114-123.
  109. Perera, S.M., Wickramasinghe, C., Samarasiri, B.K.T., Narayana, M., 2021. Modeling of thermochemical conversion of waste biomass-a comprehensive review. Biofuel Res. J. 8(4), 1481-1528.
  110. Peterson, A.A., Vogel, F., Lachance, R.P., Fröling, M., Antal, Jr., M.J., Tester, J.W., 2008. Thermochemical biofuel production in hydrothermal media: a review of sub- and supercritical water technologies. Energy Environ. Sci. 1(1), 32-56.
  111. Qasem, M.A.A., Al-Mutairi, E.M., Jameel, A.G.A., 2023. Smoke point prediction of oxygenated fuels using neural networks. Fuel. 332, 126026.
  112. Quinlan, J.R., 1986. Induction of decision trees. Mach. Learn. 1, 81-106.
  113. Ragauskas, A.J., Beckham, G.T., Biddy, M.J., Chandra, R., Chen, F., Davis, M.F., Davison, B.H., Dixon, R.A., Gilna, P., Keller, M., Langan, P., Naskar, A.K., Saddler, J.N., Tschaplinski, T.J., Tuskan, G.A., Wyman, C.E., 2014. Lignin valorization: improving lignin processing in the biorefinery. Science. 344(6185), 1246843.
  114. Sawangkeaw, R., Ngamprasertsith, S., 2013. A review of lipid-based biomasses as feedstocks for biofuels production. Renew. Sust. Energy Rev. 25, 97-108.
  115. Sezer, S., Kartal, F., Özveren, U., 2022. Prediction of combustion reactivity for lignocellulosic fuels by means of machine learning. J. Therm. Anal. Calorim. 147(17), 9793-9809.
  116. Sezer, S., Kartal, F., Özveren, U., 2021. Prediction of chemical exergy of syngas from downdraft gasifier by means of machine learning. Therm. Sci. Eng. Prog. 26, 101031.
  117. Sezer, S., Özveren, U., 2021. Investigation of syngas exergy value and hydrogen concentration in syngas from biomass gasification in a bubbling fluidized bed gasifier by using machine learning. Int. J. Hydrogen Energy. 46(39), 20377-20396.
  118. Shafizadeh, A., Shahbeig, H., Nadian, M.H., Mobli, H., Dowlati, M., Gupta, V.K., Peng, W., Lam, S.S., Tabatabaei, M., Aghbashlo, M., 2022. Machine learning predicts and optimizes hydrothermal liquefaction of biomass. Chem. Eng. J. 445, 136579.
  119. Shahbeik, H., Rafiee, S., Shafizadeh, A., Jeddi, D., Jafary, T., Lam, S.S., Pan, J., Tabatabaei, M., Aghbashlo, M., 2022. Characterizing sludge pyrolysis by machine learning: towards sustainable bioenergy production from wastes. Renewable Energy. 199, 1078-1092.
  120. Shenbagaraj, S., Sharma, P.K., Sharma, A.K., Raghav, G., Kota, K.B., Ashokkumar, V., 2021. Gasification of food waste in supercritical water: an innovative synthesis gas composition prediction model based on Artificial Neural Networks. Int. J. Hydrogen Energy. 46(24), 12739-12757.
  121. Sigmund, G., Gharasoo, M., Hüffer, T., Hofmann, T., 2020. Deep learning neural network approach for predicting the sorption of ionizable and polar organic pollutants to a wide range of carbonaceous materials. Environ. Sci. Technol. 54(7), 4583-4591.
  122. Su, Y., Zhu, W., Gong, M., Zhou, H., Fan, Y., Amuzu-Sefordzi, B., 2015. Interaction between sewage sludge components lignin (phenol) and proteins (alanine) in supercritical water gasification. Int. J. Hydrogen Energy. 40(30), 9125-9136.
  123. Sun, Y., Zhang, Yuyao, Lu, L., Wu, Y., Zhang, Y., Kamran, M.A., Chen, B., 2022. The application of machine learning methods for prediction of metal immobilization remediation by biochar amendment in soil. Sci. Total Environ. 829, 154668.
  124. Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wang, S., Chen, H., Raza Naqvi, S., 2021. Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: effects of pyrolysis conditions and biomass characteristics. Bioresour. Technol. 339, 125581.
  125. Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wu, Z., Chen, H., Naqvi, S.R., 2020. Prediction of bio-oil yield and hydrogen contents based on machine learning method: effect of biomass compositions and pyrolysis conditions. Energy Fuels. 34(9), 11050-11060.
  126. Tao, J., Liang, R., Li, J., Yan, B., Chen, G., Cheng, Z., Li, W., Lin, F., Hou, L., 2020. Fast characterization of biomass and waste by infrared spectra and machine learning models. J. Hazard. Mater. 387, 121723.
  127. Hoang Pham, L.K.,Tran, T.T.V., Kongparakul, S., Reubroycharoen, P., Ding, M., Guan, G., Vo, D.V.N., Jaiyong, P., Youngvises, N., Samart, C., 2021. Data-driven prediction of biomass pyrolysis pathways toward phenolic and aromatic products. J. Environ. Chem. Eng. 9(2), 104836.
  128. Tuck, C.O., Pérez, E., Horváth, I.T., Sheldon, R.A., Poliakoff, M., 2012. Valorization of biomass: deriving more value from waste. Science. 337(6095), 695-699.
  129. Ullah, Z., khan, M., Naqvi, S.R., Farooq, W., Yang, H., Wang, S., Vo, D.V.N., 2021. A comparative study of machine learning methods for bio-oil yield prediction-a genetic algorithm-based features selection. Bioresour. Technol. 335, 125292.
  130. Ullah, Z., Khan, M., Naqvi, S.R., Khan, M.N.A., Farooq, W., Anjum, M.W., Yaqub, M.W., AlMohamadi, H., Almomani, F., 2022. An integrated framework of data-driven, metaheuristic, and mechanistic modeling approach for biomass pyrolysis. Process Saf. Environ. Prot. 162, 337-345.
  131. Umenweke, G.C., Afolabi, I.C., Epelle, E.I., Okolie, J.A., 2022. Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: a review. Bioresour. Technol. Rep. 17, 100976.
  132. Vassilev, S. V., Baxter, D., Andersen, L.K., Vassileva, C.G., 2013. An overview of the composition and application of biomass ash.: part 2. potential utilisation, technological and ecological advantages and challenges. Fuel. 105, 19-39.
  133. Vassilev, S. V., Baxter, D., Andersen, L.K., Vassileva, C.G., 2010. An overview of the chemical composition of biomass. Fuel. 89(5), 913-933.
  134. Vassilev, S.V., Baxter, D., Andersen, L.K., Vassileva, C.G., Morgan, T.J., 2012. An overview of the organic and inorganic phase composition of biomass. Fuel. 94, 1-33.
  135. Wang, S., Dai, G., Yang, H., Luo, Z., 2017. Lignocellulosic biomass pyrolysis mechanism: a state-of-the-art review. Prog. Energy Combust. Sci. 62, 33-86.
  136. Watson, J., Wang, T., Si, B., Chen, W.T., Aierzhati, A., Zhang, Y., 2020. Valorization of hydrothermal liquefaction aqueous phase: pathways towards commercial viability. Prog. Energy Combust. Sci. 77, 100819.
  137. Wang, F., Harindintwali, J.D., Yuan, Zhizhang, Wang, M., Wang, Faming, Li, S., Yin, Z., Huang, L., Fu, Y., Li, L., Chang, S.X., Zhang, L., Rinklebe, J., Yuan, Zuoqiang, Zhu, Q., Xiang, L., Tsang, D.C.W., Xu, L., Jiang, X., Liu, J., Wei, N., Kästner, M., Zou, Y., Ok, Y.S., Shen, J., Peng, D., Zhang, W., Barceló, D., Zhou, Y., Bai, Z., Li, B., Zhang, B., Wei, K., Cao, H., Tan, Z., Zhao, L. bin, He, X., Zheng, J., Bolan, N., Liu, X., Huang, C., Dietmann, S., Luo, M., Sun, N., Gong, J., Gong, Y., Brahushi, F., Zhang, T., Xiao, C., Li, X., Chen, W., Jiao, N., Lehmann, J., Zhu, Y.G., Jin, H., Schäffer, A., Tiedje, J.M., Chen, J.M., 2021a. Technologies and perspectives for achieving carbon neutrality. Innovation. 2(4), 100180.
  138. Wang, Y., Liao, Z., Mathieu, S., Bin, F., Tu, X., 2021b. Prediction and evaluation of plasma arc reforming of naphthalene using a hybrid machine learning model. J. Hazard. Mater. 404, 123965.
  139. Wang, S., Shi, Z., Jin, Y., Zaini, I.N., Li, Y., Tang, C., Mu, W., Wen, Y., Jiang, J., Jönsson, P.G., Yang, W., 2022. A machine learning model to predict the pyrolytic kinetics of different types of feedstocks. Energy Convers. Manage. 260, 115613.
  140. Xia, C., Cai, L., Zhang, H., Zuo, L., Shi, S.Q., Lam, S.S., 2021. A review on the modeling and validation of biomass pyrolysis with a focus on product yield and composition. Biofuel Res. J. 8(1), 1296-1315.
  141. Xing, J., Luo, K., Wang, H., Fan, J., 2019. Estimating biomass major chemical constituents from ultimate analysis using a random forest model. Bioresour. Technol. 288, 121541.
  142. Yan, Y., Mattisson, T., Moldenhauer, P., Anthony, E.J., Clough, P.T., 2020. Applying machine learning algorithms in estimating the performance of heterogeneous, multi-component materials as oxygen carriers for chemical-looping processes. Chem. Eng. J. 387, 124072.
  143. Xu, S., Chen, J., Peng, H., Leng, S., Li, Hui, Qu, W., Hu, Y., Li, Hailong, Jiang, S., Zhou, W., Leng, L., 2021a. Effect of biomass type and pyrolysis temperature on nitrogen in biochar, and the comparison with hydrochar. Fuel. 291, 120128.
  144. Xu, Y., Liu, Xin, Cao, X., Huang, C., Liu, E., Qian, S., Liu, Xingchen, Wu, Y., Dong, F., Qiu, C.W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, Lifu, Zhao, T., Dai, J., Yang, J., Lan, L., Luo, M., Liu, Z., An, T., Zhang, B., He, X., Cong, S., Liu, Xiaohong, Zhang, W., Lewis, J.P., Tiedje, J.M., Wang, Q., An, Z., Wang, Fei, Zhang, Libo, Huang, T., Lu, C., Cai, Z., Wang, Fang, Zhang, J., 2021b. Artificial intelligence: a powerful paradigm for scientific research. Innovation. 2(4), 100179.
  145. Xu, Z.X., Ma, X.Q., Zhou, J., Duan, P.G., Zhou, W.Y., Ahmad, A., Luque, R., 2022. The influence of key reactions during hydrothermal carbonization of sewage sludge on aqueous phase properties: a review. J. Anal. Appl. Pyrolysis. 167, 105678.
  146. Yang, K., Wu, K., Zhang, H., 2022a. Machine learning prediction of the yield and oxygen content of bio-oil via biomass characteristics and pyrolysis conditions. Energy. 254, 124320.
  147. Yang, L., Wang, G., Liu, T., Wan, Y., Peng, H., Leng, L., Zhong, Q., 2022b. Experimental and ReaxFF molecular dynamic study on pyrolysis of phenylalanine. Fuel. 324, 124690.
  148. Yang, X., Yuan, C., He, S., Jiang, D., Cao, B., Wang, S., 2023. Machine learning prediction of specific capacitance in biomass derived carbon materials : effects of activation and biochar characteristics. Fuel. 331, 125718.
  149. Yapıcı, E., Akgün, H., Özkan, K.E.M.A.L., Günkaya, Z., Özkan, A., Banar, M., 2022. Prediction of gas product yield from packaging waste pyrolysis: support vector and Gaussian process regression models. Int. J. Environ. Sci. Technol. 20, 461-476.
  150. Yuan, X., Leng, L., Huang, H., Chen, X., Wang, H., Xiao, Z., Zhai, Y., Chen, H., Zeng, G., 2015. Speciation and environmental risk assessment of heavy metal in bio-oil from liquefaction/pyrolysis of sewage sludge. Chemosphere. 120, 645-652.
  151. Yuan, X., Suvarna, M., Low, S., Dissanayake, P.D., Lee, K.B., Li, J., Wang, X., Ok, Y.S., 2021. Applied machine learning for prediction of CO2 adsorption on biomass waste-derived porous carbons. Environ. Sci. Technol. 55(17), 11925-11936.
  152. Zhai, Y., Peng, C., Xu, B., Wang, T., Li, C., Zeng, G., Zhu, Y., 2017. Hydrothermal carbonisation of sewage sludge for char production with different waste biomass: effects of reaction temperature and energy recycling. Energy. 127, 167-174.
  153. Zhang, J., Liu, J., Evrendilek, F., Zhang, X., Buyukada, M., 2019. TG-FTIR and Py-GC/MS analyses of pyrolysis behaviors and products of cattle manure in CO2 and N2 atmospheres: kinetic, thermodynamic, and machine-learning models. Energy Convers. Manage. 195, 346-359.
  154. Zhang, K., Zhong, S., Zhang, H., 2020. Predicting aqueous adsorption of organic compounds onto biochars, carbon nanotubes, granular activated carbons, and resins with machine learning. Environ. Sci. Technol. 54(11), 7008-7018.
  155. Zhang, T., Cao, D., Feng, X., Zhu, J., Lu, X., Mu, L., Qian, H., 2022. Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions. Fuel. 312, 122812.
  156. Zhang, W., Chen, Q., Chen, J., Xu, D., Zhan, H., Peng, H., Pan, J., Vlaskin, M., Leng, L., Li, H., 2023. Machine learning for hydrothermal treatment of biomass : a review. Bioresour. Technol. 370, 128547.
  157. Zhang, W., Li, J., Liu, T., Leng, S., Yang, L., Peng, H., Jiang, S., Zhou, W., Leng, L., Li, H., 2021. Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae. Bioresour. Technol. 342, 126011.
  158. Zhao, S., Li, J., Chen, C., Yan, B., Tao, J., Chen, G., 2021. Interpretable machine learning for predicting and evaluating hydrogen production via supercritical water gasification of biomass. J. Clean. Prod. 316, 128244.
  159. Zhu, X., Li, Y., Wang, X., 2019a. Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresour. Technol. 288, 121527.
  160. Zhu, X., Wang, X., Ok, Y.S., 2019b. The application of machine learning methods for prediction of metal sorption onto biochars. J. Hazard. Mater. 378, 120727.
  161. Zhu, X., Tsang, D.C.W., Wang, L., Su, Z., Hou, D., Li, L., Shang, J., 2020. Machine learning exploration of the critical factors for CO2 adsorption capacity on porous carbon materials at different pressures. J. Clean. Prod. 273, 122915.
  162. Zhou, X., Zhao, J., Chen, M., Wu, S., Zhao, G., Xu, S., 2022a. Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments. Bioresour. Technol. 350, 126923.
  163. Zhou, X., Zhao, J., Chen, M., Zhao, G., Wu, S., 2022b. Influence of catalyst and solvent on the hydrothermal liquefaction of woody biomass. Bioresour. Technol. 346, 126354.
  164. Zhu, X., He, M., Sun, Y., Xu, Z., Wan, Z., Hou, D., Alessi, D.S., Tsang, D.C.W., 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.
  165. Zhu, L.T., Chen, X.Z., Ouyang, B., Yan, W.C., Lei, H., Chen, Z., Luo, Z.H., 2022b. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors. Ind. Eng. Chem. Res. 61(28), 9901-9949.

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