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Nguyen, Du
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Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Journal : International Journal of Renewable Energy Development

 1 
Application of response surface methodology to optimize the dual-fuel engine running on producer gas Nguyen, Phuoc Quy Phong; Tran, Viet Dung; Nguyen, Du; Luong, Cong Nho; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 14, No 2 (2025): March 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60927

Abstract

This work develops a computational framework that optimizes the performance and emissions of a dual-fuel diesel engine running on biomass-derived producer gas as the main fuel and diesel as the pilot fuel. The study connects essential responses, brake thermal efficiency, peak combustion pressure, and emissions of nitrogen oxides (NOx), carbon monoxide (CO), and unburnt hydrocarbon (HC) with controllable factors like engine load and pilot fuel injection duration. The approach consists of simulating the impacts of these controllable inputs on engine performance, then optimization to find the optimal fuel injection pressure to balance performance and emissions. The results show that engine load considerably affects NOx emissions and brake thermal efficiency; greater loads lower CO emissions but raise HC emissions at low compression ratios. Although it had little effect on NOx emissions, fuel injection pressure was vital in balancing general engine performance. Using optimization, an optimal fuel injection pressure value of 218.5 bar was identified, thereby producing a brake thermal efficiency of 27.35% and lowering emissions to 80 ppm HC, 202 ppm NOx, and 92 ppm CO. This computational method offers a strategic means for improving the efficiency of dual-fuel engines while reducing their environmental impact, hence guiding more sustainable and effective engine operation.
Integrated multi-objective optimization of fuel injection and engine strategy in oxyhydrogen/producer gas-powered dual-fuel diesel engine Nguyen, Du; Nguyen, Lan Huong; Nguyen, Duy Tan; Chung, Nghia; Truong, Thanh Hai
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 Nguyen, Du; Nguyen, Thi Bich Ngoc; Nguyen, Duc Chuan; Chau, Thanh Hieu; Duong, Minh Thai; Dang, Thanh Nam
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 Chau, Thanh Hieu Chung, Nghia Dang, Thanh Nam Duong, Minh Thai Luong, Cong Nho Nguyen, Duc Chuan Nguyen, Duy Tan Nguyen, Lan Huong Nguyen, Phuoc Quy Phong Nguyen, Thi Bich Ngoc Paramasivam, Prabhu Tran, Viet Dung Truong, Thanh Hai