Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Renewable Energy Development International Journal of Renewable Energy Development
Le, Van Vang
Unknown Affiliation
Chemistry Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A Review on the Role and Impact of Typical Alcohol Additives in Controlling Emissions from Diesel Engines Chau, Minh Quang; Le, Van Vang; Le, Tri Hieu; Bui, Van Tam
International Journal of Renewable Energy Development Vol 11, No 1 (2022): February 2022
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2022.42263

Abstract

Today, most of the essential energy needs of humans and production are met by fossil fuels that are expected to be exhausted in the next century. Furthermore, fossil fuels are not renewable and sensitive to the environment. In particular, there is growing concerned about the negative impact of internal combustion engine emissions on climate change and global environmental pollution. Fuel and alcohol-based additives are being considered as good candidates for sustainable alternative fuels used on compression ignition engines. In this review, the different key production pathways and properties of each of the five alcohol additive candidates were discussed. Besides, their effects on the emission characteristics of diesel engines when alcohol additives are added to diesel fuel are also carefully considered. Five candidates including methanol, ethanol, propanol, butanol, and pentanol have been shown to control pollutants from combustion engines while using alcohol-based additives. This is of great significance in the strategy of coping with the threats of pollution and climate change caused by the operation of transport vehicles
Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine Le, Khac Binh; Duong, Minh Thai; Cao, Dao Nam; Le, Van Vang
International Journal of Renewable Energy Development Vol 13, No 6 (2024): November 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60724

Abstract

In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel. This work investigates the modeling of injecting strategies for a waste-derived biogas-powered dual-fuel engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, and support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) were among the criteria used in evaluations of the models. Random Forest has shown better performance for Brake Thermal Efficiency (BTE) with a test R² of 0.9938 and a low test MAPE of 3.0741%. Random Forest once more exceeded other models with a test R² of 0.9715 and a test MAPE of 4.2242% in estimating Brake Specific Energy Consumption (BSEC). With a test R² of 0.9821 and a test MAPE of 2.5801% Random Forest emerged as the most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results for the carbon monoxide (CO) prediction model based on Random Forest obtained a test R² of 0.8339 with a test MAPE of 3.6099%. Random Forest outperformed Linear Regression with a test R² of 0.9756% and a test MAPE of 7.2056% in the case of nitrogen oxide (NOx) emissions. Random Forest showed the most constant performance overall criteria. This paper emphasizes how well machine learning models especially Random Forest can prognosticate the performance of biogas dual-fuel engines.
Co-Authors Bui, Van Tam Cao, Dao Nam Chau, Minh Quang Duong, Minh Thai Le, Khac Binh Le, Tri Hieu