Modelling Engine Performance and Emissions Fueled By Biodiesel Blends for Optimal Operation

Abstract

Engines fueled by diesel play a pivotal role in today's economy, especially in agriculture and transport sectors. However, concerns over diesel-related greenhouse gas (GHG) emissions and its depletion of reserves has spurred interest in biodiesel as an alternative fuel. Biodiesel blends were introduced to address reduced engine power and efficiency due to poor fuel atomization. However, determining optimal biodiesel blend level for engine performance has proven a challenge due to their diverse properties and combustion behaviors since they are sourced from different oils. This research aims to model engine performance and emission fueled by biodiesel blends to optimize the engine operation. Use of biodiesels blends could significantly reduce dependence on imported oil, stimulate the economy by creating jobs and reduce vulnerability and decreasing reliance on global oil. The research developed mathematical models using Buckingham pi-theorem for brake thermal efficiency (Bte), specific fuel consumption (Sfc), carbon monoxide (CO) emissions and nitrogen oxides (NOx) emission for an engine fueled by different biodiesel blends. The performance and emission tests were carried out using a 3.5 kW one cylinder four stroke engine on a test rig connected to an eddy current electric dynamometer. The fuels used for experiments were WVO, canola, oleander, sunflower and coconut biodiesels blended at 10%, 15%, 20%, 25% and 30% with diesel, to run the engine operated at speeds of 1500 rpm and loaded at 0, 3, 6, 9 and 12 kg. Finally, the Non-Dominated Sorting Genetic Algorithm II (NSGA II) method was used to determine the biodiesel blending for optimal engine operation. The study established that biodiesel; densities ranged between 872 to 925 kg/m3; kinematic viscosity of 4.2 to 5.2 mm2/s and Lower heat value from 36200 to 39400 kJ/kg The results observed lower Bte and CO emissions, while Sfc and NOx emission were higher for engine fueled with biodiesel blends as compared those of diesel fuel. The developed mathematical model predicted Bte, Sfc, CO and NOx with error margins of 1.65%, 15.98%, 4.69% and 2.78 % respectively as compared to the experimental results. The study successfully developed a mathematical model to predict Bte, Sfc, NOx, and CO emissions for CI engines fueled by biodiesel blends. It also identified optimal blend levels to be 22.5, 21.9, 20.6, 19.98 and 19.6 percent for biodiesel; WVO, canola, oleander, sunflower and oleander which gave the Bte as 21.9, 23.6, 23.3 23.7 and 23.0 while NOx as 139.0, 135.3, 135.9, 134.9 and 136.5 respectively. While the model provides a foundation for simulating biodiesel blend combustion in CI engines, future studies should expand its inputs to cover a broader range of biodiesel properties, engine types, and real-world operating conditions is essential, enhance the model's accuracy and reliability, making it a more versatile tool for optimizing biodiesel blends.