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

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Advances in Chlorella microalgae for sustainable wastewater treatment and bioproduction

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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 36 · see citing works

Interpretable machine learning for predicting and evaluating hydrogen production from supercritical water gasification of coal

Tian, J., Dong, R., Jia, H., Peng, Z., Liu, Z., Wang, L., Yi, L., Xu, J., Jin, H., Chen, B., et al. (2025) · Fuel · doi:10.1016/j.fuel.2025.136173 · Cited by 36 · see citing works

Investigation and prediction of co-pyrolysis between oily sludge and high-density polyethylene via in-situ DRIFTS, TGA, and artificial neural network

Ai, Z., Zhang, W., Yang, L., Chen, H., Xu, Z., Leng, L., & Li, H. (2022) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2022.105610 · Cited by 35 · see citing works

Review and assessment of models for predicting biocrude yields from hydrothermal liquefaction of biomass

Guirguis, P. M., Seshasayee, M. S., Motavaf, B., & Savage, P. E. (2024) · RSC Sustainability · doi:10.1039/d3su00458a · Cited by 34 · see citing works

Predictive modeling of engine performance and emissions for castor oil ethyl ester biodiesel blends: A Gaussian process regression approach

Ariyarit, A., Aengchuan, P., Wiangkham, A., Pumpuang, A., Klinkaew, N., Theinnoi, K., Chuepeng, S., & Sukjit, E. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102362 · Cited by 34 · see citing works

A critical review of machine-learning for “multi-omics” marine metabolite datasets

Manochkumar, J., Cherukuri, A. K., Kumar, R. S., Almansour, A. I., Ramamoorthy, S., & Efferth, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107425 · Cited by 33 · see citing works

Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes

Leng, L., Zhou, J., Zhang, W., Chen, J., Wu, Z., Xu, D., Zhan, H., Yuan, X., Xu, Z., Peng, H., et al. (2023) · Energy · doi:10.1016/j.energy.2023.129854 · Cited by 35 · see citing works

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

Kavitha, S. K., Ravi, Y. K., Kumar, G., Yogalakshmi, K. N., & Banu, J. R. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120135 · Cited by 33 · see citing works

Novel Intelligent System Based on Automated Machine Learning for Multiobjective Prediction and Early Warning Guidance of Biogas Performance in Industrial-Scale Garage Dry Fermentation

Zhang, Y., Zhao, Y., Feng, Y., Yu, Y., Li, Y., Li, J., Ren, Z., Chen, S., Feng, L., Pan, J., et al. (2023) · ACS ES&T Engineering · doi:10.1021/acsestengg.3c00079 · Cited by 31 · see citing works

A review on recent advances in clean microalgal bio-oil production via catalytic hydrothermal deoxygenation

Liu, X., Guo, Y., Xu, D., & Guan, Q. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.132978 · Cited by 30 · see citing works

Mathematical models application in optimization of hydrothermal liquefaction of biomass

Hao, B., Xu, D., Wei, Y. N., Diao, Y., Yang, L., Fan, L., & Guo, Y. (2023) · Fuel Processing Technology · doi:10.1016/j.fuproc.2023.107673 · Cited by 29 · see citing works

Experimental and ReaxFF molecular dynamic study on pyrolysis of phenylalanine

Yang, L., Wang, G., Liu, T., Wan, Y., Peng, H., Leng, L., & Zhong, Q. (2022) · Fuel · doi:10.1016/j.fuel.2022.124690 · Cited by 28 · see citing works

Mining the synergistic effect in hydrothermal co-liquefaction of real feedstocks through machine learning approaches

Yu, J., Zhong, X., Huang, Z., Lin, X., Weng, H., Ye, D., He, Q. S., & Yang, J. (2022) · Fuel · doi:10.1016/j.fuel.2022.126715 · Cited by 28 · see citing works

Recent advances in the production processes of hydrothermal liquefaction biocrude and aid-in investigation techniques

Yu, J., Lin, X., Huang, J., Ye, W., Lan, Q., Du, S., Liu, Z., Wu, Y., Zhao, Z., Xu, X., et al. (2023) · Renewable Energy · doi:10.1016/j.renene.2023.119348 · Cited by 28 · see citing works

Application of life cycle assessment and machine learning for the production and environmental sustainability assessment of hydrothermal bio-oil

Guo, G., He, Y., Jin, F., Mašek, O., & Huang, Q. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.129027 · Cited by 27 · see citing works

Predicting performance of in-situ microbial enhanced oil recovery process and screening of suitable microbe-nutrient combination from limited experimental data using physics informed machine learning approach

Pavan, P. S., Arvind, K., Nikhil, B., & Sivasankar, P. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127023 · Cited by 27 · see citing works

Machine learning prediction of supercapacitor performance of N-doped biochar from biomass wastes based on N-containing groups, element compositions, and pore structures

Liao, H., Fan, S., Han, W., Wang, M., Shi, Q., Xie, Y., Yang, X., & Chen, W. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.113548 · Cited by 26 · see citing works

Research Progress, Trends, and Future Prospects on Hydrothermal Liquefaction of Algae for Biocrude Production: A Bibliometric Analysis

Bassoli, S. C., da Fonseca, Y. A., Wandurraga, H. J. L., Baeta, B. E. L., & de Souza Amaral, M. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-023-03905-7 · Cited by 25 · see citing works

Machine learning-aided model for predicting oily sludge pyrolysis under various feedstock and operating conditions

Lu, C. L., Li, D., Xi, B., Hu, G., & Li, J. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.137654 · Cited by 25 · see citing works

Biomass carbon mining to develop nature-inspired materials for a circular economy

Bachs-Herrera, A., York, D., Stephens-Jones, T., Mabbett, I. W., Yeo, J., & Martín‐Martínez, F. J. (2023) · iScience · doi:10.1016/j.isci.2023.106549 · Cited by 27 · see citing works

Prediction of bio-oil yield by machine learning model based on 'enhanced data' training

Zhao, C., Lu, X., Jiang, Z., Ma, H., Chen, J., & Liu, X. (2024) · Renewable Energy · doi:10.1016/j.renene.2024.120218 · Cited by 24 · see citing works

Machine-learning-aided life cycle assessment and techno-economic analysis of hydrothermal liquefaction of sewage sludge for bio-oil production

Zhou, J., Chen, J., Zhang, W., Tong, Y., Liu, S., Xu, D., Leng, L., & Li, H. (2025) · Energy · doi:10.1016/j.energy.2025.135026 · Cited by 24 · see citing works

Prediction of Thermogravimetric Data in the Thermal Recycling of e-waste Using Machine Learning Techniques: A Data-driven Approach

Ali, L., Sivaramakrishnan, K., Kuttiyathil, M. S., Chandrasekaran, V., Ahmed, O. H., Al-Harahsheh, M., & Altarawneh, M. (2023) · ACS Omega · doi:10.1021/acsomega.3c07228 · Cited by 24 · see citing works

Comprehensive review of hydrothermal liquefaction data for use in machine‐learning models

Haarlemmer, G. W., Matricon, L., & Roubaud, A. (2024) · Biofuels Bioproducts and Biorefining · doi:10.1002/bbb.2637 · Cited by 22 · see citing works

Interpretable machine learning for predicting heavy metal removal and optimizing biochar characteristics

Wang, C., Zhao, Y., Gao, Y., Chen, H., Li, X., Zhou, B., Fan, D., Fang, Z., & Liu, J. (2024) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2024.106484 · Cited by 26 · see citing works

AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations

Sharma, D., & Singh, K. (2025) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-025-04494-5 · Cited by 23 · see citing works

Evaluating the minimum fuel selling price of algae-derived biofuel from hydrothermal liquefaction

Cheng, F., & Luo, H. (2021) · Bioresource Technology Reports · doi:10.1016/j.biteb.2021.100901 · Cited by 23 · see citing works

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges

Huang, X., Zhang, X., Xing, L., Huang, S., Zhang, C., Hu, X.-C., & Liu, C. (2025) · Bioresource Technology · doi:10.1016/j.biortech.2025.132434 · Cited by 22 · see citing works

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

Adeleke, A. A., Okolie, J. A., Ogbaga, C. C., Ikubanni, P. P., Okoye, P. U., & Akande, O. (2023) · BioEnergy Research · doi:10.1007/s12155-023-10681-9 · Cited by 21 · see citing works

Machine-learning-aided prediction and optimization of struvite recovery from synthetic wastewater

Leng, L., Kang, B., Xu, D., Kapusta, K., Xiong, T., Xu, Z., Fan, L., Liu, T., Peng, H., & Li, H. (2024) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2024.104896 · Cited by 21 · see citing works

Low-carbon biofuels from macroalgae towards a sustainable circular bioeconomy and green future

Joshiba, G. J., Nagarajan, D., Rathinam, B., & Chien, J.-R. C. (2024) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2024.107389 · Cited by 20 · see citing works

A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis

Qian, L., Zhang, X., Ma, X., Xue, P., Tang, X., Li, X., & Wang, S. (2024) · Energies · doi:10.3390/en17215254 · Cited by 19 · see citing works

Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments

Zhou, X., Zhao, J. L., Chen, M., Wu, S., Zhao, G., & Xu, S. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.126923 · Cited by 19 · see citing works

Machine Learning Approach for Predicting Hydrothermal Liquefaction of Lignocellulosic Biomass

Katongtung, T., Phromphithak, S., Onsree, T., & Tippayawong, N. (2024) · BioEnergy Research · doi:10.1007/s12155-024-10773-0 · Cited by 19 · see citing works

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass

Zhang, W., Ai, Z., Chen, Q., Chen, J., Xu, D., Cao, J., Kapusta, K., Peng, H., Leng, L., & Li, H. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.173939 · Cited by 19 · see citing works

Hydrothermal bio-oil yield and higher heating value of high moisture and lipid biomass: Machine learning modeling and feature response behavior analysis

Liu, X., Zhang, X., Charoenkal, K., Yuan, Q., & Cao, H. (2024) · Journal of the Energy Institute · doi:10.1016/j.joei.2024.101859 · Cited by 19 · see citing works

Progresses and Challenges of Machine Learning Approaches in Thermochemical Processes for Bioenergy: A Review

Ogunsola, N. O., Oh, S. S., Jeon, P. R., Ling, J. L. J., Park, H. J., Park, H. S., Lee, H. E., Sohn, J. M., & Lee, S. H. (2024) · Korean Journal of Chemical Engineering · doi:10.1007/s11814-024-00181-7 · Cited by 18 · see citing works

Machine Learning Model Insights into Base-Catalyzed Hydrothermal Lignin Depolymerization

García, A. C., Cheng, S., McGlynn, S. E., & Cross, J. S. (2023) · ACS Omega · doi:10.1021/acsomega.3c04168 · Cited by 18 · see citing works

Prediction and optimization design of porous structure properties of biomass-derived biochar using machine learning methods

Ai, Z., Luo, S., Xu, Z., Cao, J., Leng, L., & Li, H. (2024) · Energy · doi:10.1016/j.energy.2024.133707 · Cited by 16 · see citing works

Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite–Corundum Ceramics

Chen, Q., Zhang, W., Liang, X., Feng, H., Xu, W., Wang, P., Pan, J., & Cheng, B. (2025) · Materials · doi:10.3390/ma18061384 · Cited by 16 · see citing works

Hydrothermal liquefaction of wastewater as part of tailoring biocrude composition for a circular bioeconomy: A review

Mkilima, T. (2025) · Biofuels Bioproducts and Biorefining · doi:10.1002/bbb.70004 · Cited by 16 · see citing works

Transfer learning improves predictions in lignin content of Chinese fir based on Raman spectra

Gao, W., Jiang, Q., Guan, Y., Huang, H., Liu, S., Ling, S., & Zhou, L. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.132147 · Cited by 16 · see citing works

Machine learning modeling reveals interactions between biochemical components during hydrothermal liquefaction of biomass

Gao, J., Zhang, W., Dai, N., Zhou, J., Leng, L., & Li, H. (2025) · Fuel · doi:10.1016/j.fuel.2025.135144 · Cited by 15 · see citing works

Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review

Wang, C., Zhang, X., Zhao, G., & Chen, Y. (2023) · Chemosphere · doi:10.1016/j.chemosphere.2023.140191 · Cited by 15 · see citing works

Advanced dual-artificial neural network system for biomass combustion analysis and emission minimization

Postawa, K., Klimek, K. E., Maj, G., Kapłan, M., & Szczygieł, J. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.119543 · Cited by 15 · see citing works

Automated machine learning for bio-oil yield prediction from lignocellulosic biomass pyrolysis

Campos, D., Ervilha, R., Macêdo, B. D. S., Bodini, M., Saporetti, C. M., & Goliatt, L. (2025) · Fuel · doi:10.1016/j.fuel.2025.136480 · Cited by 14 · see citing works

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics

Li, W., Xu, Z., Xu, X., Wang, L., Sun, H., & Liu, C. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.178003 · Cited by 14 · see citing works

Interpretable machine learning model for bio-oil property prediction from hydrothermal liquefaction of biomass via graph neural networks-based molecular structure

Tian, Y., Kim, J., & Liu, Y. (2025) · Energy · doi:10.1016/j.energy.2025.136708 · Cited by 13 · see citing works

Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803

Yu, T., Fan, F., Huang, L., Wang, W., Wan, M., & Li, Y. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2023.130166 · Cited by 15 · see citing works

Machine learning and experiments on hydrothermal liquefaction of sewage sludge: Insight into migration and transformation mechanisms of phosphorus

Zheng, P., Xu, D., Liu, T., Wang, Y., Xu, M.-X., Wang, S., & Kapusta, K. (2024) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2024.113538 · Cited by 13 · see citing works

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 166 · see citing works

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 · see citing works

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 36 · see citing works

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 · see citing works