Combination of integrated machine learning model frameworks and infrared spectroscopy towards fast and interpretable characterization of model pyrolysis oil
Author
Abstract
Suggested Citation
DOI: 10.1016/j.renene.2024.121434
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yang, Zixu & Kumar, Ajay & Huhnke, Raymond L., 2015. "Review of recent developments to improve storage and transportation stability of bio-oil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 859-870.
- Kim, Jun Young & Shin, Ui Hyeon & Kim, Kwangsu, 2023. "Predicting biomass composition and operating conditions in fluidized bed biomass gasifiers: An automated machine learning approach combined with cooperative game theory," Energy, Elsevier, vol. 280(C).
- Chen, Chao & Liang, Rui & Ge, Yadong & Li, Jian & Yan, Beibei & Cheng, Zhanjun & Tao, Junyu & Wang, Zhenyu & Li, Meng & Chen, Guanyi, 2022. "Fast characterization of biomass pyrolysis oil via combination of ATR-FTIR and machine learning models," Renewable Energy, Elsevier, vol. 194(C), pages 220-231.
- 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).
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Hu, Hangli & Luo, Yanru & Zou, Jianfeng & Zhang, Shukai & Yellezuome, Dominic & Rahman, Md Maksudur & Li, Yingkai & Li, Chong & Cai, Junmeng, 2022. "Exploring aging kinetic mechanisms of bio-oil from biomass pyrolysis based on change in carbonyl content," Renewable Energy, Elsevier, vol. 199(C), pages 782-790.
- Kumar, R. & Strezov, V., 2021. "Thermochemical production of bio-oil: A review of downstream processing technologies for bio-oil upgrading, production of hydrogen and high value-added products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- Mirkouei, Amin & Haapala, Karl R. & Sessions, John & Murthy, Ganti S., 2017. "A review and future directions in techno-economic modeling and optimization of upstream forest biomass to bio-oil supply chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 15-35.
- Santhappan, Joseph Sekhar & Boddu, Muralikrishna & Gopinath, Arun S. & Mathimani, Thangavel, 2024. "Analysis of 27 supervised machine learning models for the co-gasification assessment of peanut shell and spent tea residue in an open-core downdraft gasifier," Renewable Energy, Elsevier, vol. 235(C).
- Perkins, Greg & Bhaskar, Thallada & Konarova, Muxina, 2018. "Process development status of fast pyrolysis technologies for the manufacture of renewable transport fuels from biomass," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 292-315.
- Taghipour, Alireza & Ramirez, Jerome A. & Brown, Richard J. & Rainey, Thomas J., 2019. "A review of fractional distillation to improve hydrothermal liquefaction biocrude characteristics; future outlook and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
- Zhang, Shuping & Su, Yinhai & Xu, Dan & Zhu, Shuguang & Zhang, Houlei & Liu, Xinzhi, 2018. "Effects of torrefaction and organic-acid leaching pretreatment on the pyrolysis behavior of rice husk," Energy, Elsevier, vol. 149(C), pages 804-813.
- Wang, Haoxiang & Liu, Jing, 2024. "Corrosion-induced changes in bio-oil aging: A gas chromatography exploration," Renewable Energy, Elsevier, vol. 234(C).
- Fang, Shuqi & Jiang, Luyao & Li, Pan & Bai, Jing & Chang, Chun, 2020. "Study on pyrolysis products characteristics of medical waste and fractional condensation of the pyrolysis oil," Energy, Elsevier, vol. 195(C).
- Zhang, Congyu & Felix, Charles B. & Chen, Wei-Hsin & Zhang, Ying, 2024. "Supervised and unsupervised machine learning for elemental changes evaluation of torrefied biochars," Energy, Elsevier, vol. 312(C).
- Dimitriadis, Athanasios & Chrysikou, Loukia P. & Meletidis, George & Terzis, George & Auersvald, Miloš & Kubička, David & Bezergianni, Stella, 2021. "Bio-based refinery intermediate production via hydrodeoxygenation of fast pyrolysis bio-oil," Renewable Energy, Elsevier, vol. 168(C), pages 593-605.
- Sylwia Polesek-Karczewska & Paulina Hercel & Behrouz Adibimanesh & Izabela Wardach-Świȩcicka, 2024. "Towards Sustainable Biomass Conversion Technologies: A Review of Mathematical Modeling Approaches," Sustainability, MDPI, vol. 16(19), pages 1-43, October.
- Nanduri, Arvind & Kulkarni, Shreesh S. & Mills, Patrick L., 2021. "Experimental techniques to gain mechanistic insight into fast pyrolysis of lignocellulosic biomass: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
- Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Chen, Yong, 2024. "Study on the Co-gasification characteristics of biomass and municipal solid waste based on machine learning," Energy, Elsevier, vol. 290(C).
- 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).
- 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).
- 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.
- Wang, Chu & Ding, Haozhi & Zhang, Yiming & Zhu, Xifeng, 2020. "Analysis of property variation and stability on the aging of bio-oil from fractional condensation," Renewable Energy, Elsevier, vol. 148(C), pages 720-728.
- Ribeiro, Luiz Augusto Badan & Martins, Robson Cristiano & Mesa-Pérez, Juan Miguel & Bizzo, Waldir Antonio, 2019. "Study of bio-oil properties and ageing through fractionation and ternary mixtures with the heavy fraction as the main component," Energy, Elsevier, vol. 169(C), pages 344-355.
- Qi, Jingwei & Wang, Yijie & Xu, Pengcheng & Hu, Ming & Huhe, Taoli & Ling, Xiang & Yuan, Haoran & Li, Jiadong & Chen, Yong, 2024. "Biomass hydrothermal gasification characteristics study: based on deep learning for data generation and screening strategies," Energy, Elsevier, vol. 312(C).
More about this item
Keywords
Feature selection; Machine learning; Ensemble learning; Model interpretation;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:236:y:2024:i:c:s0960148124015027. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.