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

Gradient boosting machines, a tutorial

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

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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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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

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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

Review on machine learning-based bioprocess optimization, monitoring, and control systems

Mondal, P. P., Galodha, A., Verma, V., Singh, V., Show, P. L., Awasthi, M. K., Lall, B., Anees, S., Pollmann, K., & Jain, R. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128523 · Cited by 185

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

Li, H., Chen, J., Zhang, W., Zhan, H., He, C. H., Yang, Z., Peng, H., & Leng, L. (2023) · Biofuel Research Journal · doi:10.18331/brj2023.10.1.4 · Cited by 158

Machine learning for hydrothermal treatment of biomass: A review

Zhang, W., Chen, Q., Chen, J., Xu, D., Zhan, H., Peng, H., Pan, J., Vlaskin, M. S., Leng, L., & Li, H. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128547 · Cited by 151

Machine learning assisted predicting and engineering specific surface area and total pore volume of biochar

Li, H., Ai, Z., Yang, L., Zhang, W., Yang, Z., Peng, H., & Leng, L. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128417 · Cited by 140

From biomass to biocrude: Innovations in hydrothermal liquefaction and upgrading

Usman, M., Cheng, S., Boonyubol, S., & Cross, J. S. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.118093 · Cited by 141

Understanding and optimizing the gasification of biomass waste with machine learning

Li, J., Li, L., Tong, Y. W., & Wang, X. (2022) · Green Chemical Engineering · doi:10.1016/j.gce.2022.05.006 · Cited by 132

Biochar in the 21st century: A data-driven visualization of collaboration, frontier identification, and future trend

Qin, F., Li, J., Zhang, C., Zeng, G., Huang, D., Tan, X., Qin, D., & Tan, H. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.151774 · Cited by 126

Microalgae bio-oil production by pyrolysis and hydrothermal liquefaction: Mechanism and characteristics

Ağbulut, Ü., Sirohi, R., Lichtfouse, E. A., Chen, W., Len, C., Show, P. L., Tuấn, L. A., Nguyễn, X. P., & Hoang, A. T. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.128860 · Cited by 122

Wet wastes to bioenergy and biochar: A critical review with future perspectives

Li, J., Li, L., Suvarna, M., Pan, L., Tabatabaei, M., Ok, Y. S., & Wang, X. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.152921 · Cited by 117

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Cheng, Y., Bi, X., Xu, Y., Liu, Y., Li, J., Du, G., Lv, X., & Liu, L. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128451 · Cited by 118

A review on machine learning approaches for microalgae cultivation systems

Syed, T., Krujatz, F., Ihadjadene, Y., Mühlstädt, G., Hamedi, H., Mädler, J., & Urbas, L. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108248 · Cited by 92

Hydrothermal liquefaction of wet microalgal biomass for biofuels and platform chemicals: advances and future prospects

Deepika, C., Mrinal, Pohrmen, C. B., Jaiswal, K. S., Sangmesh, B., Jaiswal, K. K., Ramasamy, A. P., & Jaiswal, A. K. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05911-4 · Cited by 90

Machine learning for microalgae detection and utilization

Ning, H., Li, R., & Zhou, T. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.947394 · Cited by 83

Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries

Yang, Y., Shahbeik, H., Shafizadeh, A., Masoudnia, N., Rafiee, S., Zhang, Y., Pan, J., Tabatabaei, M., & Aghbashlo, M. (2022) · Renewable Energy · doi:10.1016/j.renene.2022.11.028 · Cited by 85

Machine learning predicting wastewater properties of the aqueous phase derived from hydrothermal treatment of biomass

Leng, L., Zhang, W., Liu, T., Zhan, H., Li, J., Yang, L., Li, J., Peng, H., & Li, H. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127348 · Cited by 81

Machine learning for sustainable organic waste treatment: a critical review

Gupta, R., Ouderji, Z. H., Uzma, U., Yu, Z., Sloan, W. T., & You, S. (2024) · npj Materials Sustainability · doi:10.1038/s44296-024-00009-9 · Cited by 96

Sustainable energies and machine learning: An organized review of recent applications and challenges

Ifaei, P., Nazari-Heris, M., Tayerani Charmchi, A. S., Asadi, S., & Yoo, C. (2023) · Energy · doi:10.1016/j.energy.2022.126432 · Cited by 79

Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning

Jeon, P. R., Moon, J.-H., Ogunsola, N. O., Lee, S. H., Ling, J. L. J., You, S., & Park, Y.-K. (2023) · Chemical Engineering Journal · doi:10.1016/j.cej.2023.144503 · Cited by 84

Machine-learning-aided prediction and engineering of nitrogen-containing functional groups of biochar derived from biomass pyrolysis

Leng, L., Lei, X., Al-Dhabi, N. A., Wu, Z., Yang, Z., Li, T., Zhang, W., Liu, W., Zhan, H., Peng, H., et al. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.149862 · Cited by 69

Hydrothermal liquefaction of algal feedstocks: The effect of biomass characteristics and extraction solvents

Ratha, S. K., Renuka, N., Abunama, T., Rawat, I., & Bux, F. (2021) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2021.111973 · Cited by 63

Machine learning prediction of nitrogen heterocycles in bio-oil produced from hydrothermal liquefaction of biomass

Leng, L., Zhang, W., Chen, Q., Zhou, J., Peng, H., Zhan, H., & Li, H. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.127791 · Cited by 64

Microalgal-Based Bioenergy: Strategies, Prospects, and Sustainability

Choudhary, S., Tripathi, S., & Poluri, K. M. (2022) · Energy & Fuels · doi:10.1021/acs.energyfuels.2c02922 · Cited by 64

Machine learning-aided hydrothermal carbonization of biomass for coal-like hydrochar production: Parameters optimization and experimental verification

Liu, Q., Zhang, G., Yu, J., Kong, G., Cao, T., Ji, G., Zhang, X., & Han, L. (2023) · Bioresource Technology · doi:10.1016/j.biortech.2023.130073 · Cited by 63

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

Leng, L., Zhou, J., Li, T., Vlaskin, M. S., Zhan, H., Peng, H., Huang, H., & Li, H. (2022) · Fuel · doi:10.1016/j.fuel.2022.126995 · Cited by 62

Machine learning for algal biofuels: a critical review and perspective for the future

Çoşgun, A., Günay, M. E., & Yıldırım, R. (2023) · Green Chemistry · doi:10.1039/d3gc00389b · Cited by 61

Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning.

Zhen, Y., Wang, L., Sun, H., & Liu, C. (2023) · Environmental Pollution · doi:10.1016/j.envpol.2023.121834 · Cited by 49

Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques

Rahimi, M., Mashhadimoslem, H., Thanh, H. V., Ranjbar, B., Khosrowshahi, M. S., Rohani, A., & Elkamel, A. (2023) · Energy · doi:10.1016/j.energy.2023.128546 · Cited by 58

Application of heavy metal immobilization in soil by biochar using machine learning

Guo, G., Lin, L., Jin, F., Mašek, O., & Huang, Q. (2023) · Environmental Research · doi:10.1016/j.envres.2023.116098 · Cited by 60

Predicting gas production by supercritical water gasification of coal using machine learning

Liu, S., Yang, Y., Yu, L., Zhu, F., Cao, Y., Liu, X., Yao, A., & Cao, Y. (2022) · Fuel · doi:10.1016/j.fuel.2022.125478 · Cited by 56

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Chong, J. W. R., Tang, D. Y. Y., Leong, H. Y., Khoo, K. S., Show, P. L., & Chew, K. W. (2023) · Bioengineered · doi:10.1080/21655979.2023.2244232 · Cited by 61

A review of computational modeling techniques for wet waste valorization: Research trends and future perspectives

Li, J., Suvarna, M., Li, L., Pan, L., Pérez‐Ramírez, J., Ok, Y. S., & Wang, X. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.133025 · Cited by 53

Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields

Jiang, Z., Yang, S., Smith, P., & Pang, Q. (2023) · Field Crops Research · doi:10.1016/j.fcr.2023.108821 · Cited by 49

Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass

Leng, L., Li, T., Zhan, H., Rizwan, M. S., Zhang, W., Peng, H., Yang, Z., & Li, H. (2023) · Energy · doi:10.1016/j.energy.2023.127967 · Cited by 48

Recent advances in hydrothermal liquefaction of manure wastes into value-added products

Liu, Q., Kong, G., Zhang, G., Cao, T., Wang, K., Zhang, X., & Han, L. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117392 · Cited by 49

Prediction of syngas properties of biomass steam gasification in fluidized bed based on machine learning method

Xue, P., Chen, T., Huang, X., Hu, Q., Hu, J., Zhang, H., Yang, H., & Chen, H. (2023) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2023.08.259 · Cited by 49

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects

Yang, C., Kristiani, E., Leong, Y. K., & Chang, J. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2024.131549 · Cited by 49

Advances in machine learning for high value-added applications of lignocellulosic biomass

Ge, H., Zheng, J., & Xu, H. (2022) · Bioresource Technology · doi:10.1016/j.biortech.2022.128481 · Cited by 46

The challenge of nitrogen compounds in hydrothermal liquefaction of algae

Matricon, L., Roubaud, A., Haarlemmer, G. W., & Geantet, C. (2023) · The Journal of Supercritical Fluids · doi:10.1016/j.supflu.2023.105867 · Cited by 46

Advancing sustainable development goals with machine learning and optimization for wet waste biomass to renewable energy conversion

Zhu, S.-D., Preuss, N., & You, F. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.138606 · Cited by 46

Machine learning algorithms for rapid estimation of holocellulose content of poplar clones based on Raman spectroscopy

Gao, W., Zhou, L., Liu, S., Guan, Y., Gao, H., & Hu, J. (2022) · Carbohydrate Polymers · doi:10.1016/j.carbpol.2022.119635 · Cited by 43

Artificial intelligence and machine learning applications for cultured meat

Todhunter, M. E., Jubair, S., Verma, R., Saqe, R., Shen, K., & Duffy, B. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1424012 · Cited by 51

Co-pyrolysis of biomass and plastic wastes and application of machine learning for modelling of the process: A comprehensive review

Bhushan, D., Hooda, S., & Mondal, P. (2025) · Journal of the Energy Institute · doi:10.1016/j.joei.2025.101973 · Cited by 50

A novel intelligent system based on machine learning for hydrochar multi-target prediction from the hydrothermal carbonization of biomass

Zhang, W., Zhou, J., Liu, Q., Xu, Z., Peng, H., Leng, L., & Li, H. (2024) · Biochar · doi:10.1007/s42773-024-00303-8 · Cited by 40

Advances in Chlorella microalgae for sustainable wastewater treatment and bioproduction

Abuhasheesh, Y. H., Ghazal, A., Tang, D. Y. Y., BANAT, F. A., Hasan, S. W., & Show, P. L. (2025) · Chemical Engineering Journal Advances · doi:10.1016/j.ceja.2025.100715 · Cited by 43

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

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

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

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 41

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

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

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 36

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

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

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

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

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 29

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

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

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

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

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

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

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

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

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

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

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 26

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 26

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

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

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

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 24

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

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

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

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

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

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

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 20

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

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

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 19

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

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

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

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

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

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 17

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

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

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

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

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 14

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

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