Hydrothermal bio-oil yield and higher heating value of high moisture and lipid biomass: Machine learning modeling and feature response behavior analysis
{"title":"Hydrothermal bio-oil yield and higher heating value of high moisture and lipid biomass: Machine learning modeling and feature response behavior analysis","authors":"","doi":"10.1016/j.joei.2024.101859","DOIUrl":null,"url":null,"abstract":"<div><div>The yield and higher heating value (HHV) of bio-oil products are significant performance parameters for the hydrothermal conversion of high-water and high-lipid biomass. Machine learning (ML) modeling prediction is a fast and convenient means of obtaining performance parameters. An informative dataset with 243 samples was prepared, and two highly adapted ML algorithms were used: Random Forest (RF) and Extreme Gradient Boosting Tree (XGBoost). It is interesting to note that the developed ML models demonstrated great prediction ability; for example, the regression coefficient (<span><math><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></math></span>) of the XGBoost model for yield and HHV prediction was as high as 0.942 and 0.940, respectively. Furthermore, partial dependence plots (PDP) and SHapley Additive exPlanations (SHAP) interpretability methodologies were adopted to address the main contributions of the feature identification and response behavior analysis of the features. The results demonstrated that the biomass composition had the greatest effect on bio-oil yield, with fat contributing up to 40 %. In contrast, the elemental composition had the most significant effect on the HHV of bio-oil. Notably, hydrogen content affected the HHV of up to 4.5 units. The interaction response behavior showed that the interaction of the process parameters with feedstock properties was most common and significant. The information obtained from the response mechanism can be used to enhance the subsequent hydrothermal fuel preparation process for bio-oils.</div></div>","PeriodicalId":17287,"journal":{"name":"Journal of The Energy Institute","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of The Energy Institute","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1743967124003374","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The yield and higher heating value (HHV) of bio-oil products are significant performance parameters for the hydrothermal conversion of high-water and high-lipid biomass. Machine learning (ML) modeling prediction is a fast and convenient means of obtaining performance parameters. An informative dataset with 243 samples was prepared, and two highly adapted ML algorithms were used: Random Forest (RF) and Extreme Gradient Boosting Tree (XGBoost). It is interesting to note that the developed ML models demonstrated great prediction ability; for example, the regression coefficient () of the XGBoost model for yield and HHV prediction was as high as 0.942 and 0.940, respectively. Furthermore, partial dependence plots (PDP) and SHapley Additive exPlanations (SHAP) interpretability methodologies were adopted to address the main contributions of the feature identification and response behavior analysis of the features. The results demonstrated that the biomass composition had the greatest effect on bio-oil yield, with fat contributing up to 40 %. In contrast, the elemental composition had the most significant effect on the HHV of bio-oil. Notably, hydrogen content affected the HHV of up to 4.5 units. The interaction response behavior showed that the interaction of the process parameters with feedstock properties was most common and significant. The information obtained from the response mechanism can be used to enhance the subsequent hydrothermal fuel preparation process for bio-oils.
期刊介绍:
The Journal of the Energy Institute provides peer reviewed coverage of original high quality research on energy, engineering and technology.The coverage is broad and the main areas of interest include:
Combustion engineering and associated technologies; process heating; power generation; engines and propulsion; emissions and environmental pollution control; clean coal technologies; carbon abatement technologies
Emissions and environmental pollution control; safety and hazards;
Clean coal technologies; carbon abatement technologies, including carbon capture and storage, CCS;
Petroleum engineering and fuel quality, including storage and transport
Alternative energy sources; biomass utilisation and biomass conversion technologies; energy from waste, incineration and recycling
Energy conversion, energy recovery and energy efficiency; space heating, fuel cells, heat pumps and cooling systems
Energy storage
The journal''s coverage reflects changes in energy technology that result from the transition to more efficient energy production and end use together with reduced carbon emission.