IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v191y2020ics0360544219322364.html
   My bibliography  Save this item

Predictive modeling of biomass gasification with machine learning-based regression methods

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Safarian, Sahar & Ebrahimi Saryazdi, Seyed Mohammad & Unnthorsson, Runar & Richter, Christiaan, 2020. "Artificial neural network integrated with thermodynamic equilibrium modeling of downdraft biomass gasification-power production plant," Energy, Elsevier, vol. 213(C).
  2. Ma, Zherui & Wang, Jiangjiang & Feng, Yingsong & Wang, Ruikun & Zhao, Zhenghui & Chen, Hongwei, 2023. "Hydrogen yield prediction for supercritical water gasification based on generative adversarial network data augmentation," Applied Energy, Elsevier, vol. 336(C).
  3. Ayub, Yousaf & Hu, Yusha & Ren, Jingzheng, 2023. "Estimation of syngas yield in hydrothermal gasification process by application of artificial intelligence models," Renewable Energy, Elsevier, vol. 215(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. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.
  6. Onsree, Thossaporn & Tippayawong, Nakorn, 2021. "Machine learning application to predict yields of solid products from biomass torrefaction," Renewable Energy, Elsevier, vol. 167(C), pages 425-432.
  7. Yang, Yadong & Shahbeik, Hossein & Shafizadeh, Alireza & Rafiee, Shahin & Hafezi, Amir & Du, Xinyi & Pan, Junting & Tabatabaei, Meisam & Aghbashlo, Mortaza, 2023. "Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning," Energy, Elsevier, vol. 278(PB).
  8. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
  9. Namita Kumari & Ankush Sharma & Binh Tran & Naveen Chilamkurti & Damminda Alahakoon, 2023. "A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced," Energies, MDPI, vol. 16(14), pages 1-19, July.
  10. Zachl, Angelika & Buchmayr, Markus & Gruber, Johann & Anca-Couce, Andrés & Scharler, Robert & Hochenauer, Christoph, 2024. "Experimental-data-based, easy-to-use product gas composition prediction of a commercial open-top gasifier based on commercially used properties of softwood chips," Renewable Energy, Elsevier, vol. 226(C).
  11. Chen, Zhiwen & Zhao, Ming & Lv, Yi & Wang, Iwei & Tariq, Ghulam & Zhao, Sheng & Ahmed, Shakil & Dong, Weiguo & Ji, Guozhao, 2024. "Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models," Energy, Elsevier, vol. 288(C).
  12. El Moçayd, Nabil & Seaid, Mohammed, 2021. "Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  13. Zhang, Jinchun & Hou, Jinxiu & Zhang, Zichuan, 2022. "Real-time identification of out-of-control and instability in process parameter for gasification process: Integrated application of control chart and kalman filter," Energy, Elsevier, vol. 238(PB).
  14. Pierdicca, Roberto & Balestra, Mattia & Micheletti, Giulia & Felicetti, Andrea & Toscano, Giuseppe, 2022. "Semi-automatic detection and segmentation of wooden pellet size exploiting a deep learning approach," Renewable Energy, Elsevier, vol. 197(C), pages 406-416.
  15. Ascher, Simon & Sloan, William & Watson, Ian & You, Siming, 2022. "A comprehensive artificial neural network model for gasification process prediction," Applied Energy, Elsevier, vol. 320(C).
  16. Pourali, Mostafa & Esfahani, Javad Abolfazli, 2022. "Performance analysis of a micro-scale integrated hydrogen production system by analytical approach, machine learning, and response surface methodology," Energy, Elsevier, vol. 255(C).
  17. Wang, Zhengxin & Peng, Xinggan & Xia, Ao & Shah, Akeel A. & Yan, Huchao & Huang, Yun & Zhu, Xianqing & Zhu, Xun & Liao, Qiang, 2023. "Comparison of machine learning methods for predicting the methane production from anaerobic digestion of lignocellulosic biomass," Energy, Elsevier, vol. 263(PD).
  18. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
  19. 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.
  20. 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).
  21. 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).
  22. 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).
  23. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
  24. Sahar Safarian & Seyed Mohammad Ebrahimi Saryazdi & Runar Unnthorsson & Christiaan Richter, 2021. "Artificial Neural Network Modeling of Bioethanol Production Via Syngas Fermentation," Biophysical Economics and Resource Quality, Springer, vol. 6(1), pages 1-13, March.
  25. Shamsi, Meisam & Babazadeh, Reza, 2022. "Estimation and prediction of Jatropha cultivation areas in China and India," Renewable Energy, Elsevier, vol. 183(C), pages 548-560.
  26. Md Sumon Reza & Zhanar Baktybaevna Iskakova & Shammya Afroze & Kairat Kuterbekov & Asset Kabyshev & Kenzhebatyr Zh. Bekmyrza & Marzhan M. Kubenova & Muhammad Saifullah Abu Bakar & Abul K. Azad & Hrido, 2023. "Influence of Catalyst on the Yield and Quality of Bio-Oil for the Catalytic Pyrolysis of Biomass: A Comprehensive Review," Energies, MDPI, vol. 16(14), pages 1-39, July.
  27. Ziółkowski, Paweł & Badur, Janusz & Pawlak- Kruczek, Halina & Stasiak, Kamil & Amiri, Milad & Niedzwiecki, Lukasz & Krochmalny, Krystian & Mularski, Jakub & Madejski, Paweł & Mikielewicz, Dariusz, 2022. "Mathematical modelling of gasification process of sewage sludge in reactor of negative CO2 emission power plant," Energy, Elsevier, vol. 244(PA).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.