Optimization of Engine Performance and Emission Characteristics

Manjunath Patel G. C., Ajith B. S., Jagadish, Arun Kumar Shettigar and Olusegun David Samuel
Additional contact information
Manjunath Patel G. C.: PES Institute of Technology and Management, Shivamogga, Visvesvaraya Technological University
Ajith B. S.: Sahyadri College of Engineering & Management, Mangaluru, Visvesvaraya Technological University
Jagadish: Indian Statistical Institute
Arun Kumar Shettigar: National Institute of Technology Karnataka
Olusegun David Samuel: Federal University of Petroleum Resources

Chapter Chapter 5 in Biofuel Production, Performance, and Emission Optimization, 2025, pp 183-210 from Springer

Abstract: Abstract The engine performance (brake thermal efficiency: BTE and brake-specific fuel consumption: BSFC) and emission characteristics (carbon monoxide: CO, nitrogen oxide: NOx, unburnt hydrocarbon: UHC) were reliant on engine parameters such as engine load (EL), blend type (BT), injection pressure (IP), and compression ratio (CR). Optimizing these parameters helps improve engine efficiency and reduce pollutants released into the atmosphere. Optimization is often complex due to conflicting requirements such as maximizing BTE and minimizing BSFC, CO, NOx, and UHC. Empirical equations with a better coefficient of determination were used as an objective function for optimization. No universal algorithm has been defined yet to produce the best-optimized conditions satisfying all applications. Six meta-heuristic search algorithms, including the JAYA Algorithm, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Teaching–Learning-Based Optimization (TLBO), Election Optimization Algorithm (EBOA), and Driving Training Optimization (DTBO), were used to conduct the optimization task. A parametric study was undertaken to tune algorithm-specific and common parameters before optimization. The algorithm performances were evaluated for global desirability value and computation time. Six cases were considered with equal weight fraction for five responses (1/5 = 0.2) corresponding to case 1, cases 2–6 assigned with maximum weight fraction (0.6) for individual output with the least weight for the rest (0.1) towards BTE, BSFC, CO, UHC, and NOx. DTBO and EBOA converged to a global fitness function value of 0.94, with DTBO being more computationally efficient than EBOA. The conditions determined by DTBO and EBOA correspond to case 2 (maximum weight fraction (i.e. 0.6) for BTE, with a minimum weight fraction of 0.1 maintained for BSFC, CO, UHC, and NOx) and are recommended as optimal ones that satisfy all outputs resulting in an average absolute percent deviation in prediction equal to 4.68% experimentally. Ag2O nanoparticles in biodiesel, fuelled by diesel engines, were found to increase by 2.77% for BTE and decrease by 5.41% for BSFC, 4.72% for CO, 2.53% for NOx, and 4.20% for UHC, respectively.

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-84806-3_5

Ordering information: This item can be ordered from
http://www.springer.com/9783031848063

DOI: 10.1007/978-3-031-84806-3_5

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().