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

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Nitrogen containing functional groups of biochar: An overview

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

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

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Recent progress in the direct liquefaction of typical biomass

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

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Novel machine learning unlocks high lipid productivity and resolves trade-offs in algal biofuel production

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Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation

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Integrating Automation in Biomass Transformation: Opportunities, Challenges, and Future Directions

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Algae to Biofuels: Catalytic Strategies and Sustainable Technologies for Green Energy Conversion

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Machine learning-assisted separation of lignin from hydrothermal treatment of biomass by deep eutectic solvents

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Aspen Plus® Modeling and Simulation of an Industrial Biomass Direct Liquefaction Process

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Comprehensive Assessment of Eutrophication and the Mechanisms Driving Phytoplankton Blooms in Multifunctional Reservoirs

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Advancing algal biofuel production through data-driven insights: A comprehensive review of machine learning applications

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Artificial Intelligence–driven microalgal biorefineries: Advancing wastewater phycoremediation toward sustainable biofuel and high-value product generation

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Development of a generalizable and interpretable machine learning model for predicting hydrogen production from supercritical water gasification using generative adversarial network data augmentation

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Machine learning prediction of bio-polyol yields and hydroxyl values from acid-catalyzed liquefaction of lignocellulosic biomass

Wu, S., Zhao, J., Li, C., Li, X., Xu, Z., & Wang, H. (2024) · Industrial Crops and Products · doi:10.1016/j.indcrop.2024.119022 · Cited by 9

Machine learning-aided engineering of biochar from biomass pyrolysis and activation processes for enhanced CO2 adsorption

Ai, Z., Cao, C., Zhang, W., Xu, Z., Cao, J., Peng, H., Leng, L., & Li, H. (2025) · Separation and Purification Technology · doi:10.1016/j.seppur.2025.133882 · Cited by 8

Characterization of Biocrude Oils from Hydrothermal Liquefaction of De-ashed Energy Grass

Katongtung, T., Phromphithak, S., Onsree, T., Lauterbach, J. A., & Tippayawong, N. (2023) · JOM · doi:10.1007/s11837-023-06234-2 · Cited by 8

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning

Zhan, H., Wang, M., Wang, J., Chen, J., Yin, X., & Yue, Y. (2024) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ad460f · Cited by 8

Machine learning prediction and exploration of phosphorus migration and transformation during hydrothermal treatment of biomass waste

Tong, Y., Zhang, W., Zhou, J., Liu, S., Kang, B., Wang, J., Jiang, S., Leng, L., & Li, H. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.176780 · Cited by 7

Machine Learning Strategies for Forecasting Mannosylerythritol Lipid Production Through Fermentation: A Proof-of-Concept

Vares, C. A., Agostinho, S. P., Fred, A. L. N., Faria, N. T., & Rodrigues, C. A. V. (2025) · Applied Sciences · doi:10.3390/app15073709 · Cited by 8

Upgrading microalgae for petroleum alternatives: CO2 upcycling for photosynthesis-based society

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The state-of-the-art applications of machine learning technologies in liquid biofuel biorefineries

Xie, D., Shi, Y., Wang, J., Cárdenas‐Oscanoa, A. J., Wang, S., & Huang, C. (2025) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2025.108510 · Cited by 7

Machine-learning-aided prediction of the building block profiles of lignocellulosic biomass using elemental composition

Leng, L., Gao, J., Zhang, W., Liu, W., Jiang, L., Zhan, H., Huang, H., & Li, H. (2025) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2025.108531 · Cited by 7

State-of-the-art applications of machine learning in the life cycle of solid waste management

Liang, R., Chen, C., Kumar, A., Tao, J., Yan, K., Dong, H., Jiang, X., Tang, P., Yan, B., & Chen, G. (2022) · Frontiers of Environmental Science & Engineering · doi:10.1007/s11783-023-1644-x · Cited by 7

Bio-oil production from biogenic wastes, the hydrothermal conversion step

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Machine-learning-guided predict and design of polymeric carbon nitride for H2O2 photosynthesis

Xu, P., Yuan, X., Wang, H., Li, K., Chen, S., Zhang, W., & Jiang, L. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146446 · Cited by 6

Artificial intelligence driven microalgae based green fabrication and bioenergy systems for sustainable energy materials and biowaste valorization

Abbaszadeh, M., Punna, S., Pusuluru, S., Samuel, M. S., Khandge, R. S., Ethiraj, S., Almukhlifi, H. A., & Menaa, F. (2026) · Frontiers in Chemistry · doi:10.3389/fchem.2026.1858141 · Cited by 6

Kinetic Modelling for Hydrothermal Conversion of Food Wastes

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Harnessing microalgae from wastewater for biocrude via hydrothermal Liquefaction: A sustainable pathway to biofuels and biochemicals

Sivaramakrishnan, R., Kumar, S., Hosseinzadeh, H., & Balan, V. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116759 · Cited by 5

Sustainable bioenergy from microalgal lipid remodeling: An AI and genetic engineering approach for the circular economy

Singh, R. P., Singh, P. K., Kumar, I., Kumar, M., Gaur, V. K., Kaushik, A., Arya, A., Saikia, M., Santos‐Villalobos, S. D. L., Kumar, A., et al. (2026) · Energy Strategy Reviews · doi:10.1016/j.esr.2026.102050 · Cited by 5

Qualitative improvement of bio-oil derived from hydrothermal liquefaction of liquid fertiliser drained Kappaphycus alvarezii

Santhosh, V., & Sivanandi, P. (2023) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-023-03749-1 · Cited by 5

Machine learning technologies for digestate-derived hydrochar yields

Wang, W., Chang, J., & Lee, D. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.106905 · Cited by 5

Biocrude production via hydrothermal liquefaction of cycas circinalis seed shell: A machine learning approach

Vanisree, G. S., Saral, J. S., Chandran, A. M., Varun, S., Ranganathan, P., & Aparna, K. (2025) · International Journal of Green Energy · doi:10.1080/15435075.2025.2450626 · Cited by 5

Prediction of Thermogravimetric Data for Asphaltenes Extracted from Deasphalted Oil Using Machine Learning Techniques

Sivaramakrishnan, K., Tannous, J. H., & Chandrasekaran, V. (2023) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.3c01798 · Cited by 4

Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular EconomyPreprint

Despotović, M., & Glatschke, M. (2024) · doi:10.31235/osf.io/6qmhf · Cited by 4

Development of algal industry 4.0 for efficient cultivation, resource recovery, and bioproducts production

Malik, H. A., Shahid, R., Rehman, O., Su, K., Fan, Z., Usman, M., Zhao, A., Solovchenko, A. E., Shahid, A., & Xu, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34063-5.00014-3 · Cited by 3

Sustainable conversion of wet biomass, algae, and food waste to fuels in hot compressed water: Multi-Scale analysis

Hosseinpour, M., Fakhroleslam, M., Salimi, M., Short, M., Phan, A. N., Timko, M. T., & Borhani, T. N. G. (2025) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2025.101264 · Cited by 3

Predictive modeling of energy demand in hydrothermal liquefaction of biomass using machine learning algorithms

Aledaily, A. N., Yadav, K., Quadri, N. S., Dutta, D., Altayeb, M., & Abbasi, H. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109152 · Cited by 3

Artificial Intelligence Methods in Marine Biotechnology

Kavitha, M. S., Jackson, A., Manikandarajan, V., Mariappan, M., Paul, A., & Maran, B. A. V. (2023) · Springer eBooks · doi:10.1007/978-981-99-0624-6_16 · Cited by 3

Advancing bioenergy: leveraging artificial intelligence for efficient production and optimization

Adhikary, S., Sau, S., Debnath, S., & Ghosh, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28947-7.00010-0 · Cited by 3

Hybrid Machine Learning Model for the Evaluation of Biomethane Potential Based on the Biochemical Composition of BiomassPreprint

Okolie, J. A., Ogbaga, C. C., Adeleke, A. A., Ikubanni, P. P., Okoye, P. U., & Akande, O. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4479420 · Cited by 3

Machine learning algorithms for prediction of Sb adsorption by iron-based materials and exploration of critical factors

Zhou, G., Hu, X., Wang, W., Shi, J., & Zheng, D. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.112730 · Cited by 2

Microplastics pollution modulated by farming regimes under multi-scenarios

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A novel clustering-regression machine learning framework for biomass classification and biochemical composition prediction from elemental composition

Gao, J., Zhang, W., Leng, L., & Li, H. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.133954 · Cited by 2

Machine learning for optimizing biomass and high-value metabolite production in microalgal biotechnology: A review

Tang, J., Sun, Z., Zhang, Z., Wang, J., Kong, F., & Chi, Z. (2026) · Algal Research · doi:10.1016/j.algal.2026.104606 · Cited by 2

Machine-learning-aided predicting anaerobic digestion of the aqueous phase by-product from biomass hydrothermal conversion

Gao, J., Ban, D., Zhang, W., Zhan, H., Leng, L., & Li, H. (2026) · Bioresource Technology · doi:10.1016/j.biortech.2026.134360 · Cited by 2

Prediction of nitrogen product distribution in algal pyrolysis bio-oil driven by machine learning

Hu, Y., Liu, Z., Ma, Y., Jiang, D., Yuan, C., Qian, L., He, S., Wang, S., & Wang, Q. (2026) · Energy · doi:10.1016/j.energy.2026.140874 · Cited by 2

Machine-learning-based prediction of energy requirements for biomass pyrolysis via compositional and operational parameters

Mohammad, A. A. S., Mohammad, S. I., Vasudevan, A., mohammed, S., Malathi, H., Panigrahi, R., Christy, D. H. J., Arora, V., Shit, D., & Sherzod, S. (2026) · Waste Disposal & Sustainable Energy · doi:10.1007/s42768-026-00296-z · Cited by 2

Enhancing robustness in small-sample prediction of oily sludge supercritical water gasification: A generative adversarial network data augmented hybrid machine learning strategy

Tian, J., Hou, J., Shi, L., Peng, Z., Yi, L., Wang, L., Rong, S., Xu, J., Lu, L., & Jin, H. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.123197 · Cited by 2

Review on Smart Algae Bio Panel and its Growth Forecasting Using Machine Learning

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Hydrothermal liquefaction data for use in machine learning models

Haarlemmer, G. W., Matricon, L., & Roubaud, A. (2024) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/b978-0-443-28824-1.50529-9 · Cited by 2

Machine Learning Framework for the Methyl Chloride Production Process

Gollangi, R., NagamalleswaraRao, K., Dhanushkodi, S. R., & Mahinpey, N. (2024) · Journal of Environmental Informatics Letters · doi:10.3808/jeil.202400142 · Cited by 2

Prediction of Syngas Properties of Biomass Steam Gasification in Fluidized Bed Based on Machine Learning MethodPreprint

Xue, P., Huang, X., Hu, Q., Hu, J., Zhang, H., Yang, H., & Chen, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4484517 · Cited by 2

Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis

Katongtung, T., Sukpancharoen, S., Sinthupinyo, S., & Tippayawong, N. (2025) · F1000Research · doi:10.12688/f1000research.156514.2 · Cited by 1

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

Mechanistic pathways and influencing factors in the liquefaction of single and mixed plastics toward waste-to-energy conversion: A structured and critical review

Ayazi, A., Daud, W. M. A. W., Patah, M. F. A., Amir, Z., & Javanmard, A. (2026) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2026.108542 · Cited by 1

Machine learning prediction and inverse design for high-yield 5-HMF production from cellulose in metal salt catalysts

Zhao, H., Yu, F., Xie, Y., Jia, Y., Zhang, Y., Song, F., Tan, H., & Cui, H. (2026) · Catalysis Today · doi:10.1016/j.cattod.2026.115703 · Cited by 1

Interpretable machine learning assisted optimization of antimony adsorption by metal–organic frameworks: combined with characterization methods for adsorption mechanism analysis

Wang, C., Zhao, Y., Chen, H., Gao, Y., Han, Q., Leng, Y., & Fang, Z. (2026) · Environmental Science and Pollution Research · doi:10.1007/s11356-026-37524-2 · Cited by 1

The potential of artificial intelligence in the meat industry

Горбунова, Н. А., & Nikitina, M. A. (2026) · Theory and practice of meat processing · doi:10.21323/2414-438x-2026-11-1-4-34 · Cited by 1

Data-driven modeling required energy of fuel production process through biomass hydrothermal liquefaction

Mohammad, A. A. S., Mohammad, S. I., Vasudevan, A., mohammed, S., Malathi.H, M., Panigrahi, R., Jemmy Christy, H., Arora, V., Shit, D., & Sherzod, S. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110738 · Cited by 2

Digitalising bioenergy processes: Surveying literature and industrial experience to understand progress, challenges and opportunities

Dekhici, B., & Short, M. (2026) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2026.109507 · Cited by 1

Role of artificial intelligence and machine learning in microalgal biofuel production

Mallick, S., Qamar, Q., & Nayak, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29262-0.00014-9 · Cited by 1

Algal Industry 5.0 for sustainable aquafeeds: integrating digital technologies and bioprocessing

Soon, C. F., Sunar, N. M., Adris, N. A., Paiman, N. A., Arifin, S. N. H., Lixandru, L.-M., Yaakob, M., Jamia’an, M., Tee, K. S., & Heng, C. (2026) · Aquaculture International · doi:10.1007/s10499-026-02604-0 · Cited by 1

Machine Learning-Guided Biochar Design for Antibiotics AdsorptionPreprint

Li, J., Pan, L., Huang, Y., Liu, X., Ye, Z., & Wang, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4569092

Anaerobic Digestion Effects on Biomass and Biodegradable Waste Hydrothermal Carbonization: An Analysis Using Statistic and Machine Learning MethodsPreprint

Wen, Y., Tan, Y. B., Wang, Y., & Wang, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4864707

Advancing Algal Biofuel Production Through Data-driven Insights: a Comprehensive Review of Machine Learning ApplicationsPreprint

Akande, O., Ogbaga, C. C., Okolie, J. A., & Kimera, R. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4898510

A critical review on hydrothermal liquefaction screening using Microalgae as a two-way approach to sewage water treatment and biofuel production

Avanish K, T., Sharma, R., Lamba, B. Y., Bora, J., Talukdar, N., Jain, S., Shah, S. V., Choudhary, P., Rustagi, S., Singh, D., et al. (2024) · Global NEST Journal · doi:10.30955/gnj.05497

Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis

Katongtung, T., Sukpancharoen, S., Sinthupinyo, S., & Tippayawong, N. (2024) · F1000Research · doi:10.12688/f1000research.156514.1

Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis

Katongtung, T., Sukpancharoen, S., Sinthupinyo, S., & Tippayawong, N. (2025) · F1000Research · doi:10.12688/f1000research.156514.3

Empirical and Intelligent Modelling of Biomass Hydrothermal Liquefaction

Yang, J., He, Q. S., Ye, W., Lin, X., & Kang, K. (2025) · Biofuel and biorefinery technologies · doi:10.1007/978-3-031-85036-3_6

Bio-oil and aviation production: applications and upgradation

Khan, A., Arif, M., Sharma, K., & Salama, E. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26718-5.00007-5

Neural Engineering Intergraded Research Surface Methodology and Box Behnken Design for Maximizing Biofuel Production Based Algal Biocomposition: New ApproachPreprint

Sangor, F., Ketife, A. M. D. A., & AlMomani, F. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5976137

Prospects of Microalgae Industry 5.0: Customization and New Advanced Technology for Microalgae Pigments Production

Yarkent, Ç., Mutaf, T., & Öncel, S. Ş. (2026) · Springer eBooks · doi:10.1007/978-3-032-05438-8_15

Robust prediction of biochar cation exchange capacity via interpretable ensemble and deep machine learning algorithms

Almansour, S., Alkwai, L. M., Yadav, K., Dutta, D. K., Altayeb, M., & Hekmatyar, Z. (2026) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2026.100374

Machine learning-based prediction of CO2 emissions from biomass solvent extraction

Mohammad, A. A. S., Mohammad, S. I., Vasudevan, A., mohammed, S., Malathi, H., Panigrahi, R., Jemmy Christy, H., Arora, V., Shit, D., & Sherzod, S. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.109651

Advanced Nanomaterials Synthesis During Bioenergy Production

Debbarma, R., Lodh, B. K., Biswas, S., & Bandyopadhyay, T. K. (2026) · Sustainable landscape planning and natural resources management · doi:10.1007/978-3-032-18644-7_2

A Comprehensive Review on Machine Learning Approaches for Thermochemical Conversion of Biomass to Biofuels

Kaur, R., Budhiraja, I., & Shrivastava, R. K. (2026) · International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC) · doi:10.1109/icisessc68634.2026.11542793

Interpretable machine learning and DFT validation of Fe-Mn/graphene biochar Gel for Sb(III) adsorption and oxidation

Wang, C., Fang, Z., Lin, W., Chen, H., Liu, J., Sun, C., Gao, Y., & Wang, H. (2026) · Environmental Technology & Innovation · doi:10.1016/j.eti.2026.105174

Enhancing Biomass Hydrothermal Liquefaction through Natural Sea Salt and Machine Learning Enabled Additive Selection

Lin, X., Yu, J., Ye, W., Zhong, X., Shen, J., Lin, X., He, Q. S., & Yang, J. (2026) · Waste and Biomass Valorization · doi:10.1007/s12649-026-03688-8

An in-depth insight into microbial-enhanced oil recovery: a systematic review and potential application in the Middle East and Southern Iraqi fields

Al-Dujaili, A. N., Mahdi, Z. S., Asad, M. S., Thamer, M., Al-Dujaili, A. N., & Taher, M. Y. (2026) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-026-02179-9

Unraveling outdoor growth dynamics of Synechocystis sp. PCC 6803 via machine learning in a multi-factor coupled frameworkPreprint

Yu, T., Wu, X., Wang, W., Li, Y., & Wan, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7162470

Neural network integrated RSM–BBD optimization for enhanced biocrude production from algal biomass

Ketife, A. M. D. A., Almomani, F., & Sangor, F. (2026) · Algal Research · doi:10.1016/j.algal.2026.104921

Assessing the Implementation of Artificial Intelligence-Based Systems for Sustainable Solid Waste Management in Selected Countries in Asia, Europe and North America: A Systematic Review

Andeobu, L., Wibowo, S., & Grandhi, S. (2026) · Sustainability · doi:10.3390/su18178802

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

Prediction of hydrogen production via supercritical water gasification of biomass using interpretable machine learning models: Considering the element compositions to enhance model generalization and robustness

Hou, J., Tian, J., Peng, Z., Tan, J., Wang, L., Yi, L., Peng, Z., Xu, J., & Jin, H. (2026) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2026.157416

Thermally targeted mineralization of perfluorooctanoic acid for fluorine resource transformation through tricalcium phosphate-cleaved interfacial reactions

Fang, L., Han, Y., Xing, Y., Li, J., & Li, J. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.143519

Machine learning based prediction of CO2 emissions in biomass hydrothermal liquefaction

Yadav, K., Alkwai, L. M., Almansour, S., Dutta, D., Abu-Shareha, A. A., & Pashtun, F. (2026) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2026.102300

Interpretable machine learning reveals physicochemical drivers of bio-crude yield in hydrothermal liquefaction of water hyacinth

Kumar, A. (2026) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-026-07274-9

Advances in modelling and optimization of hydrothermal liquefaction for sustainable biofuel production: A review

Sarker, T. R., Sarker, B., Alam, M. S., & Dalai, A. K. (2026) · Energy Nexus · doi:10.1016/j.nexus.2026.100835

Solubility of NH3 in polyethylene glycol polymer: robust modeling and data-driven scrutinization

Yadav, A., Fathalla, M., Khwaldeh, A., Gowrishankar, J., Mostafa, S. A., Hota, S., Rajput, P. K., Kaur, N., & Abolhekmat, M. (2026) · Chemical Papers · doi:10.1007/s11696-026-05711-7

Machine learning prediction of hydrochar and bio-oil properties during biomass hydrothermal conversion with aqueous phase recirculation

Gao, J., Li, Q., Zhang, W., Leng, L., & Li, H. (2026) · Energy · doi:10.1016/j.energy.2026.142783

Bio-AIchemy: Transforming Biorefinery Efficiency and Productivity

Yadav, S., Shahi, S., & Singh, S. K. (2026) · Green energy and technology · doi:10.1007/978-981-95-9528-0_15

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