Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling
The increasing demand for sustainable fuels has driven interest in bio-oil as an alternative to fossil diesel. This study investigates the production of bio-crude oil from Arthrospira platensis biomass via catalytic hydrothermal liquefaction (HTL) and evaluates its potential use as a blend in intern...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025020456 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849426127721857024 |
|---|---|
| author | Mozas Santhose Kumar J Prakash Ramakrishnan Padmanathan Panneerselvam |
| author_facet | Mozas Santhose Kumar J Prakash Ramakrishnan Padmanathan Panneerselvam |
| author_sort | Mozas Santhose Kumar J |
| collection | DOAJ |
| description | The increasing demand for sustainable fuels has driven interest in bio-oil as an alternative to fossil diesel. This study investigates the production of bio-crude oil from Arthrospira platensis biomass via catalytic hydrothermal liquefaction (HTL) and evaluates its potential use as a blend in internal combustion engines. The research focuses on the fuel properties, engine performance, emission characteristics, and combustion behavior of Arthrospira platensis oil (APO) and its blends with diesel. The engine was tested using pure diesel and three APO–diesel blends: APO10 (10 % v/v), APO20 (20 % v/v), and APO30 (30 % v/v). Among these, APO10 exhibited a 6 % reduction in brake thermal efficiency (BTE) and a slight increase in brake-specific fuel consumption (BSFC) compared to diesel. Emission analysis showed reductions in carbon monoxide (CO) and unburned hydrocarbons (HC) by approximately 20 % and 6.2 %, respectively. Combustion analysis revealed that the Peak in-cylinder pressures for APO blends were 70.3 bar for APO10, 70.1 bar for APO20, and 69.8 bar for APO30. Correspondingly, the peak HRR values were 35.2 J/degree for APO10, 34.4 J/degree for APO20, and 34.2 J/degree for APO30. A multiple linear regression model, developed using the least squares method, predicted blend ratio (R² = 0.96, RMSE = 2.20) and load (R² = 0.9995, RMSE = 1.11) based on performance and emission parameters. Hydrocarbons (p = 0.0028), CO (p = 0.0131), and exhaust gas temperature (EGT, p = 0.0145) were identified as statistically significant predictors. Additionally, a laboratory-scale cost assessment demonstrated the economic feasibility of catalytic HTL bio-oil production. |
| format | Article |
| id | doaj-art-20e6df3d9b2b4e9abbdc4f197d07acb1 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-20e6df3d9b2b4e9abbdc4f197d07acb12025-08-20T03:29:32ZengElsevierResults in Engineering2590-12302025-09-012710597310.1016/j.rineng.2025.105973Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modelingMozas Santhose Kumar J0Prakash Ramakrishnan1Padmanathan Panneerselvam2School of Mechanical Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Mechanical Engineering, Vellore Institute of Technology, Vellore, IndiaCorresponding author.; School of Mechanical Engineering, Vellore Institute of Technology, Vellore, IndiaThe increasing demand for sustainable fuels has driven interest in bio-oil as an alternative to fossil diesel. This study investigates the production of bio-crude oil from Arthrospira platensis biomass via catalytic hydrothermal liquefaction (HTL) and evaluates its potential use as a blend in internal combustion engines. The research focuses on the fuel properties, engine performance, emission characteristics, and combustion behavior of Arthrospira platensis oil (APO) and its blends with diesel. The engine was tested using pure diesel and three APO–diesel blends: APO10 (10 % v/v), APO20 (20 % v/v), and APO30 (30 % v/v). Among these, APO10 exhibited a 6 % reduction in brake thermal efficiency (BTE) and a slight increase in brake-specific fuel consumption (BSFC) compared to diesel. Emission analysis showed reductions in carbon monoxide (CO) and unburned hydrocarbons (HC) by approximately 20 % and 6.2 %, respectively. Combustion analysis revealed that the Peak in-cylinder pressures for APO blends were 70.3 bar for APO10, 70.1 bar for APO20, and 69.8 bar for APO30. Correspondingly, the peak HRR values were 35.2 J/degree for APO10, 34.4 J/degree for APO20, and 34.2 J/degree for APO30. A multiple linear regression model, developed using the least squares method, predicted blend ratio (R² = 0.96, RMSE = 2.20) and load (R² = 0.9995, RMSE = 1.11) based on performance and emission parameters. Hydrocarbons (p = 0.0028), CO (p = 0.0131), and exhaust gas temperature (EGT, p = 0.0145) were identified as statistically significant predictors. Additionally, a laboratory-scale cost assessment demonstrated the economic feasibility of catalytic HTL bio-oil production.http://www.sciencedirect.com/science/article/pii/S2590123025020456Hydrothermal liquefactionMicroalgaeCatalytic-HTL-oilEngine testPerformance evaluationRegression model |
| spellingShingle | Mozas Santhose Kumar J Prakash Ramakrishnan Padmanathan Panneerselvam Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling Results in Engineering Hydrothermal liquefaction Microalgae Catalytic-HTL-oil Engine test Performance evaluation Regression model |
| title | Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling |
| title_full | Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling |
| title_fullStr | Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling |
| title_full_unstemmed | Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling |
| title_short | Performance, emissions, and combustion analysis of catalytic hydrothermal liquefaction derived fuel from Arthrospira platensis in IC engines: A predictive approach using least squares regression modeling |
| title_sort | performance emissions and combustion analysis of catalytic hydrothermal liquefaction derived fuel from arthrospira platensis in ic engines a predictive approach using least squares regression modeling |
| topic | Hydrothermal liquefaction Microalgae Catalytic-HTL-oil Engine test Performance evaluation Regression model |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025020456 |
| work_keys_str_mv | AT mozassanthosekumarj performanceemissionsandcombustionanalysisofcatalytichydrothermalliquefactionderivedfuelfromarthrospiraplatensisinicenginesapredictiveapproachusingleastsquaresregressionmodeling AT prakashramakrishnan performanceemissionsandcombustionanalysisofcatalytichydrothermalliquefactionderivedfuelfromarthrospiraplatensisinicenginesapredictiveapproachusingleastsquaresregressionmodeling AT padmanathanpanneerselvam performanceemissionsandcombustionanalysisofcatalytichydrothermalliquefactionderivedfuelfromarthrospiraplatensisinicenginesapredictiveapproachusingleastsquaresregressionmodeling |