Machine learning prediction and optimization of bio-oil production from hydrothermal liquefaction of algae

Gradient boosting machines, a tutorial

Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Dietterich, T. G. (2000) · Machine Learning · doi:10.1023/a:1007607513941 · Cited by 2,996

Thermochemical biofuel production in hydrothermal media: A review of sub- and supercritical water technologies

Peterson, A. A., Vogel, F., Lachance, R. P., Fröling, M., Antal, Jr., & Tester, J. W. (2008) · Energy & Environmental Science · doi:10.1039/b810100k · Cited by 2,132

Hydrothermal liquefaction of biomass: A review of subcritical water technologies

Toor, S. S., Rosendahl, L., & Rudolf, A. (2011) · Energy · doi:10.1016/j.energy.2011.03.013 · Cited by 1,840

Nitrogen containing functional groups of biochar: An overview

Leng, L., Xu, S., Liu, R., Yu, T., Zhuo, X., Leng, S., Xiong, Q., & Huang, H. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2019.122286 · Cited by 532

Use of microalgae based technology for the removal of antibiotics from wastewater: A review

Leng, L., Liang, W., Xiong, Q., Xu, S., Li, W., Lv, S., Lu, Q., Wan, L., Wen, Z., & Zhou, W. (2019) · Chemosphere · doi:10.1016/j.chemosphere.2019.124680 · Cited by 449

Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions

Zhu, X., Li, Y., & Wang, X. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121527 · Cited by 447

Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition

Liu, A.-A., Su, Y., Nie, W., & Kankanhalli, M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2537337 · Cited by 380

Recent progress in the direct liquefaction of typical biomass

Huang, H.-J., & Yuan, X.-Z. (2015) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2015.01.003 · Cited by 323

Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning

Li, J., Pan, L., Suvarna, M., Tong, Y. W., & Wang, X. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115166 · Cited by 323

Nitrogen in bio-oil produced from hydrothermal liquefaction of biomass: A review

Leng, L., Zhang, W., Peng, H., Li, H., Jiang, S., & Huang, H. (2020) · Chemical Engineering Journal · doi:10.1016/j.cej.2020.126030 · Cited by 285

Effect of biomass type and pyrolysis temperature on nitrogen in biochar, and the comparison with hydrochar

Xu, S., Chen, J., Peng, H., Leng, S., Li, H., Qu, W., Hu, Y., Li, H., Jiang, S., Zhou, W., et al. (2021) · Fuel · doi:10.1016/j.fuel.2021.120128 · Cited by 270

Use of microalgae to recycle nutrients in aqueous phase derived from hydrothermal liquefaction process

Leng, L., Li, J., Wen, Z., & Zhou, W. (2018) · Bioresource Technology · doi:10.1016/j.biortech.2018.01.121 · Cited by 260

Predicting algal biochar yield using eXtreme Gradient Boosting (XGB) algorithm of machine learning methods

Pathy, A., Meher, S., & Balasubramanian, P. (2020) · Algal Research · doi:10.1016/j.algal.2020.102006 · Cited by 231

Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

Leow, S., Witter, J. R., Vardon, D. R., Sharma, B. K., Guest, J. S., & Strathmann, T. J. (2015) · Green Chemistry · doi:10.1039/c5gc00574d · Cited by 205

Hydrothermal liquefaction of biomass model components for product yield prediction and reaction pathways exploration

Yang, J., He, Q. S., Niu, H., Corscadden, K. W., & Astatkie, T. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.06.142 · Cited by 197

Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource

Li, J., Zhu, X., Li, Y., Tong, Y. W., Ok, Y. S., & Wang, X. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.123928 · Cited by 195

Particle swarm optimization algorithm and its parameters: A review

Juneja, M., & Nagar, S. K. (2016) · International Conference on Control, Computing, Communication and Materials (ICCCCM) · doi:10.1109/iccccm.2016.7918233 · Cited by 188

Bio-oil upgrading by emulsification/microemulsification: A review

Leng, L., Li, H., Yuan, X., Zhou, W., & Huang, H. (2018) · Energy · doi:10.1016/j.energy.2018.07.117 · Cited by 186

Machine learning aided supercritical water gasification for H2-rich syngas production with process optimization and catalyst screening

Li, J., Pan, L., Suvarna, M., & Wang, X. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.131285 · Cited by 185

Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification

Li, J., Zhang, W., Liu, T., Yang, L., Li, H., Peng, H., Jiang, S., Wang, X., & Leng, L. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130649 · Cited by 169

Prediction of Bio-oil Yield and Hydrogen Contents Based on Machine Learning Method: Effect of Biomass Compositions and Pyrolysis Conditions

Tang, Q., Chen, Y., Yang, H., Liu, M., Xiao, H., Wu, Z., Chen, H., & Naqvi, S. R. (2020) · Energy & Fuels · doi:10.1021/acs.energyfuels.0c01893 · Cited by 164

Evaluate the pyrolysis pathway of glycine and glycylglycine by TG–FTIR

Li, J., Wang, Z., Yang, X., Hu, L. H., Liu, Y., & Wang, C. (2007) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2007.03.001 · Cited by 163

Thermal decomposition of castor oil, corn starch, soy protein, lignin, xylan, and cellulose during fast pyrolysis

Qiao, Y., Wang, B., Ji, Y., Xu, F., Zong, P., Zhang, J., & Tian, Y. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.01.102 · Cited by 149

Beneficial synergistic effect on bio-oil production from co-liquefaction of sewage sludge and lignocellulosic biomass

Leng, L., Li, J., Yuan, X., Li, J., Han, P., Hong, Y., Wei, F., & Zhou, W. (2018) · Bioresource Technology · doi:10.1016/j.biortech.2017.12.018 · Cited by 139

Is hydrothermal treatment coupled with carbon capture and storage an energy-producing negative emissions technology?

Cheng, F., Porter, M. D., & Colosi, L. M. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2019.112252 · Cited by 131

Quantitative multiphase model for hydrothermal liquefaction of algal biomass

Li, Y., Leow, S., Fedders, A. C., Sharma, B. K., Guest, J. S., & Strathmann, T. J. (2017) · Green Chemistry · doi:10.1039/c6gc03294j · Cited by 130

Prediction model of biocrude yield and nitrogen heterocyclic compounds analysis by hydrothermal liquefaction of microalgae with model compounds

Sheng, L., Wang, X., & Yang, X. (2017) · Bioresource Technology · doi:10.1016/j.biortech.2017.08.011 · Cited by 112

Aqueous phase recirculation during hydrothermal carbonization of microalgae and soybean straw: A comparison study

Leng, S., Li, W., Han, C., Chen, L., Chen, J., Fan, L., Lu, Q., Li, J., Leng, L., & Zhou, W. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.122502 · Cited by 106

Algae Under Pressure and in Hot Water

Savage, P. E. (2012) · Science · doi:10.1126/science.1224310 · Cited by 103

Algal biorefinery to value-added products by using combined processes based on thermochemical conversion: A review

Fan, L., Zhang, H., Li, J., Wang, Y., Leng, L., Li, J., Yao, Y., Lu, Q., Yuan, W. W., & Zhou, W. (2020) · Algal Research · doi:10.1016/j.algal.2020.101819 · Cited by 94

A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification

Li, J., Suvarna, M., Pan, L., Zhao, Y., & Wang, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117674 · Cited by 91

Hydrothermal co-liquefaction of biomasses – quantitative analysis of bio-crude and aqueous phase composition

Madsen, R. B., Bernberg, R. Z. K., Biller, P., Becker, J., Iversen, B. B., & Glasius, M. (2017) · Sustainable Energy & Fuels · doi:10.1039/c7se00104e · Cited by 90

Hydrothermal Liquefaction of the Microalgae Phaeodactylum tricornutum: Impact of Reaction Conditions on Product and Elemental Distribution

Christensen, P. S., Peng, G., Vogel, F., & Iversen, B. B. (2014) · Energy & Fuels · doi:10.1021/ef5012808 · Cited by 72

Characterization of liquefaction bio-oil from sewage sludge and its solubilization in diesel microemulsion

Leng, L., Yuan, X., Chen, X., Huang, H., Wang, H., Li, H., Zhu, R., Li, S., & Zeng, G. (2015) · Energy · doi:10.1016/j.energy.2015.01.032 · Cited by 66

Fast and Isothermal Hydrothermal Liquefaction of Polysaccharide Feedstocks

Gollakota, A., & Savage, P. E. (2020) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.9b06873 · Cited by 63

Optimization of hydrothermal liquefaction process through machine learning approach: process conditions and oil yield

Gopirajan, P. V., Gopinath, K. P., Sivaranjani, G., & Arun, J. (2021) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-020-01233-8 · Cited by 57

High-yield bio-oil production from macroalgae (Saccharina japonica) in supercritical ethanol and its combustion behavior

Zeb, H., Park, J., Riaz, A., Ryu, C., & Kim, J. (2017) · Chemical Engineering Journal · doi:10.1016/j.cej.2017.06.078 · Cited by 35

Comprehensive potential evaluation of the bio-oil production and nutrient recycling from seven algae through hydrothermal liquefaction

Song, W., Wang, S., Xu, D., Guo, Y., Yang, C., Zhang, J., & Li, Y. (2019) · Korean Journal of Chemical Engineering · doi:10.1007/s11814-019-0345-4 · Cited by 32

Machine Learning Prediction of Syngas Composition of Hydrothermal Gasification from Wet Organic WastesPreprint

Li, J., Pan, L., Suvarna, M., Tong, Y. W., & Wang, X. (2021) · doi:10.46855/energy-proceedings-7161 · Cited by 19

Evolutionary Random Forest Algorithms for Predicting the Maximum Failure Depth of Open Stope Hangingwalls

Qi, C., & Chen, Q. (2018) · IEEE Access · doi:10.1109/access.2018.2880009 · Cited by 12

Learning to rank using multiple loss functions

Lin, Y., Wu, J., Xu, B., Xu, K., & Lin, H. (2017) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-017-0730-4 · Cited by 9

Hydrothermal Treatment of Biomass Feedstocks for Sustainable Production of Chemicals, Fuels, and Materials: Progress and Perspectives

Zhang, B., Biswal, B. K., Zhang, J., & Balasubramanian‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, R. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00673 · Cited by 441

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu, L., Chen, X., Ouyang, B., Yan, W., Lei, H., Chen, Z., & Luo, Z. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c01036 · Cited by 313

Applications of machine learning in thermochemical conversion of biomass-A review

Khan, M., Naqvi, S. R., Ullah, Z., Taqvi, S. A. A., Khan, M. N. A., Farooq, W., Mehran, M. T., Juchelková, D., & Štěpanec, L. (2022) · Fuel · doi:10.1016/j.fuel.2022.126055 · Cited by 223

Artificial Intelligence and Machine Learning Tools for High-Performance Microalgal Wastewater Treatment and Algal Biorefinery: A Critical Review

Oruganti, R. K., Biji, A. P., Lanuyanger, T., Show, P. L., Sriariyanun, M., Upadhyayula, V. K. K., Gadhamshetty, V., & Bhattacharyya, D. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162797 · Cited by 211

Machine learning predicts and optimizes hydrothermal liquefaction of biomass

Shafizadeh, A., Shahbeik, H., Nadian, M. H., Mobli, H., Dowlati, M., Gupta, V. K., Peng, W., Lam, S. S., Tabatabaei, M., & Aghbashlo, M. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.136579 · Cited by 209

Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification

Li, J., Zhang, W., Liu, T., Yang, L., Li, H., Peng, H., Jiang, S., Wang, X., & Leng, L. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.130649 · Cited by 169

Machine learning aids co-hydrothermal liquefaction of algae and waste: Prediction of synergistic effect and bio-oil yield

Hu, Y., Ma, Y., Liu, Z., Zhang, H., Jiang, D. Y., Qian, L., He, S., Wang, S., & Wang, Q. (2025) · Journal of the Energy Institute · doi:10.1016/j.joei.2025.102340 · Cited by 1

Predicting co-liquefaction bio-oil of sewage sludge and algal biomass via machine learning with experimental optimization: Focus on yield, nitrogen content, and energy recovery rate

Liu, T., Zhang, W., Xu, D., Leng, L., Li, H., Wang, S., & He, Y. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170779 · Cited by 37

Predicting bio-oil yield from hydrothermal liquefaction of lignocellulosic biomass: machine learning approach and uncertainty propagation via joint bootstrap Monte Carlo

Djandja, O. S., Hu, Y., Zeng, Y., & He, Q. S. (2026) · Fuel · doi:10.1016/j.fuel.2026.141091

Numerical Evaluation of Bio-Oil Yield Prediction Models for Hydrothermal Liquefaction of High-Lipid Microalgae

Borazjani, Z., Azin, R., & Osfouri, S. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-07951-1 · Cited by 4