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Le, Khac Binh
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Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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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.
Economic-environmental analysis of solar-wind-biomass hybrid renewable energy system for hydrogen production: A case study in Vietnam Nguyen, Huu Hieu; Bui, Van Ga; Le, Khac Binh; Nguyen, Van Trieu; Hoang, Anh Tuan
International Journal of Renewable Energy Development Vol 14, No 3 (2025): May 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61233

Abstract

Combining biomass with solar and wind energy to produce electricity and hydrogen, referred to as the Solar-Wind-Biomass Hybrid Renewable Energy System (SWB-HRES), provides optimal economic and environmental efficiency. This paper presents research findings from a case study of SWB-HRES implemented in Hoa Bac commune, Danang City, Vietnam, utilizing HOMER software for system modeling and optimization. The study aims to identify the optimal configuration for SWB-HRES with hydrogen production and assess its compatibility with grid-connected SWB-HRES without hydrogen production. A detailed analysis of greenhouse gas (GHG) emission reductions corresponding to different system configurations is also provided. The results indicate that the optimal SWB-HRES configuration for Hoa Bac includes a 15-kW solar panel, a 9-kW wind turbine, an 8.3 kW syngas generator, a 20-kW electrolyzer, a 24-kW converter, and a hydrogen storage tank with a capacity of 1 kg. This setup supports an annual electricity load of 7,300 kWh and produces 1,183 kilograms of hydrogen per year. For grid-connected HRES with hydrogen production, the solar-biomass system demonstrates superior economic and environmental efficiency compared to the wind-biomass configuration. The economic efficiency of SWB-HRES with hydrogen production matches that of SWB-HRES selling electricity to the grid when the hydrogen cost is $4.5/kg for discontinuous syngas generator operation and $5/kg for continuous operation. Furthermore, integrating biomass energy into HRES proves to be an effective strategy for GHG emission reduction. For the same electricity output of 62,863 kWh/year, the solar-wind HRES without hydrogen production achieves a GHG emission reduction of 33 tons of CO2-eq, while the solar-wind-biomass HRES with hydrogen production achieves a reduction of 217 tons of CO2-eq. Given that the performance of HRES depends on geographic location, equipment availability, and energy pricing, practical implementations should validate simulation results with experimental data collected on-site.
Co-Authors Bui, Van Ga Cao, Dao Nam Duong, Minh Thai Hoang, Anh Tuan Le, Van Vang Nguyen, Huu Hieu Nguyen, Van Trieu