A Study the Effect of Biodiesel Blends and the Injection Timing on Performance and Emissions of Common Rail Diesel Engines

Abstract

This paper presents a study on the effect of the ratio of biodiesel and injection timing on the performance of diesel engines and their emissions. The research engine is a cylinder engine AVL-5402, simulated by software AVL-Boost. The simulated fuel includes fossil diesel and biodiesel blended with a replacement rate from 0% to 50%, with a simulation mode of 2200 (rev/min), at a rate of a 25%, 50% and 75% load. In this speed range, the engine has the lowest fuel consumption. The parameters to be evaluated are power, fuel consumption and emissions, based on the proportions of blended biodiesel. The results show that there is a relationship between the proportion of blended biodiesel, injection timing and the parameters of the engine. Specifically, the ratio of the biodiesel blend increases, injection timing tends to move closer to the top dead center (TDC), the tendency reduce engine power, fuel consumption increases, the emissions of CO and soot reduces, while NOx increases.

Nghiên cứu ảnh hưởng của sự thay đổi tỷ số nén đến quá trình làm việc của động cơ tại chế độ tải thành phần

“…Ngày nay các phần mềm mô phỏng được biết điến như một công cụ hữu ích giúp các nhà nghiên cứu giải quyết vấn đề tối ưu hóa kết cấu một cách hiệu quả, một số phần mềm được ứng dụng nhiều trong lĩnh vực kĩ thuật như: CATIA, ANSYS Workbench, Hyperworks, AVL, Optistruct [6][7][8][9][10]. Mô hình mô phỏng động cơ (hình 1) được xây dựng dựa trên phần mềm AVL boost.…”

Experimental Investigation and RSM Modeling of the Effects of Injection Timing on the Performance and NOx Emissions of a Micro-Cogeneration Unit Fueled with Biodiesel Blends

“…Experimental data were statistically analyzed to individuate the parameters of influence and to quantify their respective contributions to the measured values. Indeed, after a preliminary analysis of variance (ANOVA) of the parameters (factors) influencing the performance and emissions characteristics (responses), a subsequent application of the response surface methodology (RSM) helped the experimenters to mathematically describe the effects of the statistically significant independent variables on the responses, through explicit regression-functions [19,33,[39][40][41][42] (Figure 3). Although each of these functions is completely independent from the physics of the represented process, it numerically approximates the real behavior of the system in a limited validity domain (i.e., the hyperspace delimitated by the extreme values of the factors) in the same way Taylor series do for real functions and can be used to reliably predict the value of each response [43].…”

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

“…The SVM prediction model has R2 = 0.986 very close to 1. Judging by the RMSE index, the ANN model predicts RMSE = 22.71 ms, while the SVM model has RMSE = 24.10 ms; this value is very small compared to the maximum ignition delay of 2657.407 ms. Judging by the MAPE index, the ANN model predicts MAPE = 1.13%, while the SVM model has MAPE = 0.90%; this value is very small and close to 0.…”

“…Currently, in order to reduce environmental pollution and dependence on fossil fuels, countries in the world have been focusing on the production and use of biodiesel for diesel engines. Biodiesel has many advantages over mineral diesel and other alternative fuels. , Ignition delay is one of the important parameters in the combustion process of diesel engines. It directly affects the heat dissipation rate and the starting time of the combustion process in diesel engines and indirectly affects the engine performance, noise, and formation of contaminants in the exhaust gases.…”