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Response Surface Analysis of the Energy Performance and Emissions of a Dual-Fuel Engine Generator Using Biodiesel and Hydrogen-Enriched Biogas
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Response Surface Analysis of the Energy Performance and Emissions of a Dual-Fuel Engine Generator Using Biodiesel and Hydrogen-Enriched Biogas
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Response Surface Analysis of the Energy Performance and Emissions of a Dual-Fuel Engine Generator Using Biodiesel and Hydrogen-Enriched Biogas
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Journal Article
Response Surface Analysis of the Energy Performance and Emissions of a Dual-Fuel Engine Generator Using Biodiesel and Hydrogen-Enriched Biogas
2025
Overview
In this study, we investigate the dual-fuel operation of compression ignition engines using biodiesel at varying concentrations in combination with biogas, with and without hydrogen enrichment. A response surface methodology, based on a central composite experimental design was employed to optimize energy efficiency and minimize pollutant emissions. The partial substitution of diesel with gaseous fuel substantially reduces the specific fuel consumption, achieving a maximum decrease of 21% compared with conventional diesel operation. Enriching biogas with hydrogen, accounting for 13.3% of the total flow rate, increases the thermal efficiency by 0.8%, compensating for the low calorific value and reduced volumetric efficiency of biogas. Variations in biodiesel concentration exhibits a nonlinear effect, yielding an additional average efficiency gain of 0.4%. Regarding emissions, the addition of hydrogen to biogas contributes to an average reduction of 5% in carbon monoxide emissions compared to the standard dual-fuel operation. However, dual-fuel operation leads to higher unburned hydrocarbon emissions relative to neat diesel; hydrogen enrichment mitigates this drawback by reducing hydrocarbon emissions by 4.1%. Although NOx emissions increase by an average of 26.6% with hydrogen addition, dual-fuel strategies achieve NOx reductions of 11.5% (hydrogen-enriched mode) and 33.3% (pure biogas mode) relative to diesel-only operation. Furthermore, the application of response surface methodology is robust and reliable, with experimental validation showing errors of 0.55–8.66% and an overall uncertainty of 4.84%.
