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Du Nguyen
Institute of Engineering, HUTECH University, Ho Chi Minh City
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Performance and emissions of a diesel engine fuelled with ultrasonically produced tobacco seed oil methyl ester: An RSM optimization study Binh Vu Duc; Van Vuong Nguyen; Du Nguyen; Thanh Hai Truong
International Journal of Renewable Energy Development Vol 15, No 4 (2026): July 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62705

Abstract

Biodiesel plays an important role in making diesel engines more environmentally friendly and sustainable. Biodiesel use can significantly lower emissions of harmful pollutants, contributing to cleaner air and a reduced impact on climate change. Although there is an increasing body of research on non-edible biodiesel feedstocks, few studies have been able to systematically correlate fuel production, blend variation, and engine load optimization with a single statistical framework. This study fills this gap by combining ultrasonic-assisted two-step transesterification of tobacco seed oil (TSO) with response surface methodology to determine engine performance and emissions. Acid esterification was performed to produce TSO methyl ester, which was subjected to transesterification with NaOH under ultrasonic irradiation, to guarantee efficient conversion and low levels of free fatty acids. Indeed, TSO biodiesel and diesel fuel blends were tested on the engine under different loads. The findings indicate that the engine has a critical operating point of Engine Load (EL) = 96.90% and Lower Heating Value (LHV) = 41.82 MJ/kg, at which the engine has a peak thermal performance with BTE = 32.98% and BSFC = 0.27 kg/kWh. This indicates a very effective conversion of energy because of high in-cylinder temperature and pressure. Additionally, CO and HC emissions are significantly reduced, meaning that the combustion is almost complete. Nevertheless, NOx emissions increase dramatically to 657.74 ppm, proving the thermal penalty of high-temperature operation. This trade-off is validated by multi-objective optimization, which offers a strong framework to balance efficiency and emissions in biodiesel-powered engines.
Integrated multi-objective optimization of fuel injection and engine strategy in oxyhydrogen/producer gas-powered dual-fuel diesel engine Du Nguyen; Lan Huong Nguyen; Duy Tan Nguyen; Nghia Chung; Thanh Hai Truong
International Journal of Renewable Energy Development Vol 15, No 1 (2026): January 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62062

Abstract

Biomass gasification has taken on a new significance as a decentralized and sustainable route of turning solid biomass into oxyhydrogen (HHO) enriched producer gas that can be employed in internal combustion engines using diesel as the pilot fuel. This dual fuel system can cut down on reliance on fossil diesel as well as improve the energy security of rural and semi-urban applications. This study examines the engine operation and emissions characteristics of the producer-gas-diesel dual-fuel engine under the main operating parameters and uses statistical optimization to reduce the emissions and still attain acceptable efficiency. Indeed, Prosopis juliflora wood gasification was conducted in a small, fixed-bed downdraft gasifier, which is only intended to be used in decentralized and experimental engines. Downdraft design was chosen because of the intrinsic effect that it provides low-tar PG, which must be supplied to internal combustion engines. The optimization findings reveal that the maximum brake mean effective pressure (BMEP) is 4.23 bar, pilot fuel injection pressure (PFIP) is 240 bar, and HHO flow rate (HHOFR) is 2.08 LPM. The predicted values of Brake Thermal Efficiency (BTE), Brake Specific Energy Consumption (BSEC), and carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx) emissions at these settings are estimated to be 20.71 %, 4.17 MJ/kWh, and 77.95, 79.47, and 335.99 ppm, respectively. The findings indicate that the balance between the supply of producer gas and the optimization of injection parameters can greatly enhance the sustainability and emission characteristics of the dual-fuel engine running on gaseous fuel that is produced from biomass.
Towards self-diagnostic solar farms: Leveraging EfficientNet and class activation mapping for predictive maintenance Du Nguyen; Thi Bich Ngoc Nguyen; Duc Chuan Nguyen; Thanh Hieu Chau; Minh Thai Duong; Thanh Nam Dang
International Journal of Renewable Energy Development Vol 15, No 2 (2026): March 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62298

Abstract

The high rate of utility photovoltaic (PV) system development has increased the demand for stable, automated, and interpretable fault diagnostic systems that can be utilised in real-world environments. Solar farms with a large size are increasingly making conventional manual inspection methods impractical, and triggering the use of intelligent data-driven solutions. This paper presents a justifiable deep learning model for automated fault classification of solar panels based on the EfficientNet-B2 architecture combined with Gradient-weighted Class Activation Mapping (Grad-CAM). A six-class image dataset made of clean panels and five prevalent fault types is used. The two stages of transfer learning used to train the model include a warm-up phase and selective fine-tuning of upper network layers. Data augmentation is also performed extensively to make it more robust to changing illumination, viewing angles, and environmental noise. The experimental findings reveal consistent convergence and excellent generalization ability, and a high level of classification accuracy of all types of faults, as it achieved high classification accuracy, macro-averaged F1-scores exceeding 0.90 for most fault classes, and a macro-averaged ROC–AUC of approximately 0.981, highlighting the robustness and reliability of the proposed diagnostic model. The suggested structure will provide a scalable, interpretable, and realistic predictive maintenance of solar farms of the next generation with self-diagnostic capabilities.
Co-Authors Binh Vu Duc Duc Chuan Nguyen Duy Tan Nguyen Lan Huong Nguyen Minh Thai Duong Nghia Chung Thanh Hai Truong Thanh Hai Truong Thanh Hieu Chau Thanh Nam Dang Thi Bich Ngoc Nguyen Van Vuong Nguyen