Combustion exhaust release impact, diesel engine performance, and optimization studies of green diesel-petrodiesel blend in a high compression ratio direct-injection compression-ignition engine

Abstract

Reports on the optimum brake-power, surrogate fuel, engine emissions, and efficiency using hybrid model on high compression ratio diesel engines are very imperative for effective application of biodiesel in power and renewable energy generation. This study presents Dyacrodes edulis biodiesel engine performance and combustion release optimization using response surface methodology-genetic algorithm (RSM-GA) as well as the variation of key engine efficiency and exhaust release indices with brake power and fuel blend in a high compression ratio (CR) diesel engine. Combustion emission impacts of the blends with respect to petro-diesel decreased in values except for NOX. Brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), brake mean effective pressure (BMEP), volumetric efficiency, and exhaust temperature increased with brake power while specific energies decreased with load. Optimum conditions obtained using integrated RSM-GA were 40.03%, 0.05 kg/kW-h, 0.03%, 132.30 ppm, and 18.84 ppm for BTE, BSFC, CO, NOx, and HC respectively at low factor (engine load, engine speed, and fuel blend) conditions. At the optimum conditions, the experimental validation results were 44.01%, 0.05 kg/kW-h, 0.04%, 130.05 ppm, and 20.33 ppm for BTE, BSFC, CO, NOx, and HC respectively. The application of the feedstock in compression ignition engine is viable.

A Study to Predict Ignition Delay of an Engine Using Diesel and Biodiesel Fuel Based on the ANN and SVM Machine Learning Methods

“…Quantitatively, the absolute value of R is closer to 1; the closer RMSE, MAE, and MAPE are to zero, the higher the performance and model accuracy. 34 , 35 …”

“…Quantitatively, the absolute value of R is closer to 1; the closer RMSE, MAE, and MAPE are to zero, the higher the performance and model accuracy. 34,35 From Table 2, the performance comparison between the ANN model and the SVM model shows that, with the results of this data set, at the compression ratio ε = 15 according to the ANN forecasting model, the predictive index R 2 = 0.987. This means that with the training data set and the trained SVM and ANN models have very good predictive power.…”

Catalytic deoxygenation of palm oil over metal phosphides supported on palm fiber waste derived activated biochar for producing green diesel fuel

“…The conversion of waste biomass to a green energy source has been recognized as a promising pathway to avoid using petroleum diesel in the future. 3,4 Conventionally, biodiesel, well-developed from trans -esterification of natural triglycerides including plant-based oils, animal fats, and algae oils into fatty acid methyl esters (FAMEs), has become the most popular transportation biofuel. However, the drawback of biodiesel is the oxygen content, which deteriorates the relative fuel properties, such as high viscosity, low freezing point, and low thermal stability, which ultimately causes serious problems in engine systems, such as blockage of the injector and fuel filter, carbon deposition on pistons, ring sticking, cylinder head, etc 4,5 .…”

“…3,4 Conventionally, biodiesel, well-developed from trans -esterification of natural triglycerides including plant-based oils, animal fats, and algae oils into fatty acid methyl esters (FAMEs), has become the most popular transportation biofuel. However, the drawback of biodiesel is the oxygen content, which deteriorates the relative fuel properties, such as high viscosity, low freezing point, and low thermal stability, which ultimately causes serious problems in engine systems, such as blockage of the injector and fuel filter, carbon deposition on pistons, ring sticking, cylinder head, etc 4,5 . To overcome these gaps, several technologies for conversion of triglycerides to efficient quality diesel-like fuel, which meet to the petroleum diesel quality need to be investigate conscientiously.…”

A comprehensive investigation of RANS and LES turbulence models for diesel spray modeling via experimental diesel schlieren imaging

“…The combustion and outlet emissions in these engines and engines that have combustion similar to diesel combustion are strongly influenced by the mixture of fuel and air zone, [1][2][3][4][5] which is determined by the characteristics of the fuel spray, including the breakup of fuel droplets and the spray development process. 6,7 These processes play a critical role in determining the engine combustion and emission characteristics. From a numerical standpoint, these spray combustion processes depend heavily on the choice of spray, breakup, evaporation, combustion, and turbulence models.…”