Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification-Reference-Cited by-同舟云学术

Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification

Published:2021-12 Issue: Volume:425 Page:130649
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Li Jie,Zhang Weijin,Liu Tonggui,Yang Lihong,Li Hailong,Peng Haoyi,Jiang Shaojian,Wang Xiaonan,Leng Lijian

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,Environmental Chemistry,General Chemistry

Reference56 articles.

1. Hydrothermal carbonization of sewage sludge: effect of aqueous phase recycling;Xu;Chem. Eng. J.,2020

2. Bio-oil production and upgrading research: a review;Xiu;Renew. Sustain. Energy Rev.,2012

3. Prediction of bio-oil yield and hydrogen contents based on machine learning method: effect of biomass compositions and pyrolysis conditions;Tang;Energy Fuels,2020

4. Monitoring the nitrous oxide emissions and biological nutrient removal from wastewater treatment: impact of perfluorooctanoic acid;Chen;J. Hazard. Mater.,2021

5. Effects of thermal and thermal-alkaline pretreatments on continuous anaerobic sludge digestion: performance, energy balance and enhancement mechanism;Chen;Renew. Energy.,2020

Cited by 101 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning and experiments on hydrothermal liquefaction of sewage sludge: Insight into migration and transformation mechanisms of phosphorus;Journal of Environmental Chemical Engineering;2024-10

2. A complete review on the oxygen-containing functional groups of biochar: Formation mechanisms, detection methods, engineering, and applications;Science of The Total Environment;2024-10

3. Machine learning modeling of the capacitive performance of N-doped porous biochar electrodes with experimental verification;Renewable Energy;2024-09

4. From Organic Wastes to Bioenergy, Biofuels, and Value-Added Products for Urban Sustainability and Circular Economy: A Review;Urban Science;2024-08-22

5. Machine learning for the prediction and optimization of production of cellulose nanocrystals by sulfuric acid hydrolysis;Industrial Crops and Products;2024-08