Performance enhancement of SI engine through LIVC miller cycle approach with Producer gas-biogas blends: A simulation and optimization study
Year: 2025
Type: article
Abstract: There are many difficulties by using a sole renewable energy source. In this view, the current research intends to use equi-volume producer gas (PG) and biogas (BG) produced by waste biomass via gasification and anaerobic digestion. The purpose of this research is to enhance the performance and reduce the emissions of the SI engine. So, a comprehensive quasi-dimension thermodynamical model has been used to simulate the performance of SI engines with various input variables, such as late inlet valve closure timing (LIVC), start of ignition timing (SOI), and inlet pressure. This work is novel in using FORTRAN code to simulate the impact of LIVC, SOI, and inlet pressure on SI engine performance and emissions. Additionally, It seeks to ascertain the optimum operating parameters for the most responsive power, efficiency, fuel usage, and pollution limits. The response-surface methodology was used to carry out the optimization of operating and response parameters. Using an ANOVA-based regression model shows 95% confidence level of accuracy. The optimum operating settings were found 850 aBDC-LIVC, 22.860 bTDC-SOI, and 2.5 bar intake pressure. Corresponding to these optimal inputs, the optimum response performance were found to be 4.18 kW, 30.23%, 12785.4kJ/kWh, 0.444 (vol.%), 1142.61 ppm for indicated power (IP), indicated thermal efficiency (ITE), brake specific energy consumption (BSEC), CO, and NO respectively, with a composite desirability of 0.841. According to the study's findings, modeling and optimization can be used to estimate the overall performance of SI engines that run on a blend of BG and PG fuel.
Source: Biomass and Bioenergy
Authors: Lawalesh Kumar Prajapati, Jeewan Vachan Tirkey
Institution: Indian Institute of Technology BHU
Language: English
Subfield: Fluid Flow and Transfer Processes
Field: Chemical Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,270
OpenAlex ID: W4411136106
