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

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  1. Yuan, Xiangzhou & Wang, Junyao & Deng, Shuai & Suvarna, Manu & Wang, Xiaonan & Zhang, Wei & Hamilton, Sara Triana & Alahmed, Ammar & Jamal, Aqil & Park, Ah-Hyung Alissa & Bi, Xiaotao & Ok, Yong Sik, 2022. "Recent advancements in sustainable upcycling of solid waste into porous carbons for carbon dioxide capture," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
  2. Mu, Lin & Wang, Zhen & Sun, Meng & Shang, Yan & Pu, Hang & Dong, Ming, 2024. "Machine learning model with a novel self–adjustment method: A powerful tool for predicting biomass ash fusibility and enhancing its potential applications," Renewable Energy, Elsevier, vol. 237(PA).
  3. Teimouri, Zahra & Abatzoglou, Nicolas & Dalai, Ajay K., 2024. "A novel machine learning framework for designing high-performance catalysts for production of clean liquid fuels through Fischer-Tropsch synthesis," Energy, Elsevier, vol. 289(C).
  4. Onsree, Thossaporn & Tippayawong, Nakorn & Phithakkitnukoon, Santi & Lauterbach, Jochen, 2022. "Interpretable machine-learning model with a collaborative game approach to predict yields and higher heating value of torrefied biomass," Energy, Elsevier, vol. 249(C).
  5. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2022. "Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction," Sustainability, MDPI, vol. 14(23), pages 1-11, December.
  6. Mohamed Chaibi & EL Mahjoub Benghoulam & Lhoussaine Tarik & Mohamed Berrada & Abdellah El Hmaidi, 2021. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction," Energies, MDPI, vol. 14(21), pages 1-19, November.
  7. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Masoudnia, Nima & Rafiee, Shahin & Zhang, Yijia & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries," Renewable Energy, Elsevier, vol. 201(P2), pages 70-86.
  8. Theodoros N. Kapetanakis & Ioannis O. Vardiambasis & Christos D. Nikolopoulos & Antonios I. Konstantaras & Trinh Kieu Trang & Duy Anh Khuong & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & D, 2021. "Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge," Energies, MDPI, vol. 14(11), pages 1-15, May.
  9. Tan, Daniel & Suvarna, Manu & Shee Tan, Yee & Li, Jie & Wang, Xiaonan, 2021. "A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing," Applied Energy, Elsevier, vol. 291(C).
  10. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
  11. Zhang, Bowei & Guo, Simao & Jin, Hui, 2022. "Production forecast analysis of BP neural network based on Yimin lignite supercritical water gasification experiment results," Energy, Elsevier, vol. 246(C).
  12. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
  13. Büyükkanber, Kaan & Haykiri-Acma, Hanzade & Yaman, Serdar, 2023. "Calorific value prediction of coal and its optimization by machine learning based on limited samples in a wide range," Energy, Elsevier, vol. 277(C).
  14. Parthasarathy Velusamy & Jagadeesan Srinivasan & Nithyaselvakumari Subramanian & Rakesh Kumar Mahendran & Muhammad Qaiser Saleem & Maqbool Ahmad & Muhammad Shafiq & Jin-Ghoo Choi, 2023. "Optimization-Driven Machine Learning Approach for the Prediction of Hydrochar Properties from Municipal Solid Waste," Sustainability, MDPI, vol. 15(7), pages 1-14, March.
  15. Zheng, Xidong & Zhou, Sheng & Jin, Tao, 2023. "A new machine learning-based approach for cross-region coupled wind-storage integrated systems identification considering electricity demand response and data integration: A new provincial perspective," Energy, Elsevier, vol. 283(C).
  16. Izanloo, Milad & Aslani, Alireza & Zahedi, Rahim, 2022. "Development of a Machine learning assessment method for renewable energy investment decision making," Applied Energy, Elsevier, vol. 327(C).
  17. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
  18. Leng, Lijian & Zhou, Junhui & Zhang, Weijin & Chen, Jiefeng & Wu, Zhibin & Xu, Donghai & Zhan, Hao & Yuan, Xingzhong & Xu, Zhengyong & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2024. "Machine-learning-aided hydrochar production through hydrothermal carbonization of biomass by engineering operating parameters and/or biomass mixture recipes," Energy, Elsevier, vol. 288(C).
  19. Wang, Zhen & Mu, Lin & Miao, Hongchao & Shang, Yan & Yin, Hongchao & Dong, Ming, 2023. "An innovative application of machine learning in prediction of the syngas properties of biomass chemical looping gasification based on extra trees regression algorithm," Energy, Elsevier, vol. 275(C).
  20. Gabriella Gonnella & Giulia Ischia & Luca Fambri & Luca Fiori, 2022. "Thermal Analysis and Kinetic Modeling of Pyrolysis and Oxidation of Hydrochars," Energies, MDPI, vol. 15(3), pages 1-21, January.
  21. Li, Jie & Yu, Di & Pan, Lanjia & Xu, Xinhai & Wang, Xiaonan & Wang, Yin, 2023. "Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications," Applied Energy, Elsevier, vol. 346(C).
  22. Shahbeik, Hossein & Rafiee, Shahin & Shafizadeh, Alireza & Jeddi, Dorsa & Jafary, Tahereh & Lam, Su Shiung & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2022. "Characterizing sludge pyrolysis by machine learning: Towards sustainable bioenergy production from wastes," Renewable Energy, Elsevier, vol. 199(C), pages 1078-1092.
  23. Xiaorui Liu & Haiping Yang & Jiamin Yang & Fang Liu, 2023. "Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization," Energies, MDPI, vol. 16(3), pages 1-11, February.
  24. Li, Chunxing & Wang, Yu & Xie, Shengyu & Wang, Ruming & Sheng, Hu & Yang, Hongmin & Yuan, Zengwei, 2024. "Synergistic treatment of sewage sludge and food waste digestate residues for efficient energy recovery and biochar preparation by hydrothermal pretreatment, anaerobic digestion, and pyrolysis," Applied Energy, Elsevier, vol. 364(C).
  25. Djandja, Oraléou Sangué & Duan, Pei-Gao & Yin, Lin-Xin & Wang, Zhi-Cong & Duo, Jia, 2021. "A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 232(C).
  26. Liu, Shanke & Yang, Yan & Yu, Lijun & Cao, Yu & Liu, Xinyi & Yao, Anqi & Cao, Yaping, 2023. "Self-heating optimization of integrated system of supercritical water gasification of biomass for power generation using artificial neural network combined with process simulation," Energy, Elsevier, vol. 272(C).
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