Optimization of a common rail direct injection engine fuelled with gossypium seed biodiesel–tert‐butyl alcohol blends using Taguchi–Grey relational analysis and ANN
Year: 2026
Type: article
Abstract: This work investigates the applicability of gossypium seed biodiesel (B20) blended with tert‐butyl alcohol (TBA) for improving the performance and emission behavior of diesel engines operating under conditions representative of automotive traction applications. Experimental trials were conducted on a Common Rail Direct Injection (CRDI) engine, focusing on the optimization of injection timing (IT), injection pressure (IP), and TBA blending ratio. A combined optimization strategy employing the Taguchi method and Grey Relational Analysis (GRA) was used to identify the most favorable operating conditions, while an Artificial Neural Network (ANN) model was developed to predict engine responses. The optimization framework identified an injection timing of 27°CA bTDC, a 5% TBA blending ratio, and an injection pressure of 260 bar as the optimal parameter combination. Under these conditions, ANN predictions closely matched experimental results, with limited prediction errors of 6.9% for brake thermal efficiency (BTE), 3.7% for brake‐specific fuel consumption (BSFC), 1% for CO, 0.7% for NOx, and 11% for smoke emissions. In comparison with neat diesel operation, the optimized biodiesel TBA blend exhibited a marginal change in BTE (≈0.9%), while BSFC increased by about 8.7%, primarily attributed to the lower calorific value of the oxygenated fuel. Emission analysis revealed a significant reduction in NOx emissions of approximately 21.8%, along with a nearly 7.4% decrease in CO 2 emissions. Results show cottonseed biodiesel–TBA blends with optimized injection parameters can run effectively in CRDI engines, delivering cleaner emissions with acceptable performance trade‐offs, supporting their viability for sustainable automotive diesel applications.
DOI: 10.1002/ep.70395
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7130309136