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All Journal International Journal of Renewable Energy Development JOIV : International Journal on Informatics Visualization International Journal of Renewable Energy Development
Le, Huu Cuong
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Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Laminar Flame Characteristics of 2,5-Dimethylfuran (DMF) Biofuel: A Comparative Review with Ethanol and Gasoline Hoang, Long Vuong; Nguyen, Danh Chan; Truong, Thanh Hai; Le, Huu Cuong; Nguyen, Minh Nhat
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.2022.42611

Abstract

Since the early years of the 21st century, the whole world has faced two very urgent problems: the depletion of fossil energy sources and climate change due to environmental pollution. Among the solutions sought, 2,5-Dimethylfuran (DMF) emerged as a promising solution. DMF is a 2nd generation biofuel capable of mass production from biomass. There have been many studies confirming that DMF is a potential alternative fuel for traditional fuels (gasoline and diesel) in internal combustion engines, contributing to solving the problem of energy security and environmental pollution. However, in order to apply DMF in practice, more comprehensive studies are needed. Not out of the above trend, this paper analyzes and discusses in detail the characteristics of DMF's combustible laminar flame and its instability under different initial conditions. The evaluation results show that the flame characteristics of DMF are similar to those of gasoline, although the burning rate of DMF is much higher than that of gasoline. This shows that DMF can become a potential alternative fuel in internal combustion engines.
Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability Le, Thanh Tuan; Priya, Jayabal Chandra; Le, Huu Cuong; Le, Nguyen Viet Linh; Duong, Minh Thai; Cao, Dao Nam
International Journal of Renewable Energy Development Vol 13, No 2 (2024): March 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60119

Abstract

The escalating trends in energy consumption and the associated emissions of pollutants in the past century have led to energy depletion and environmental pollution. Achieving comprehensive sustainability requires the optimization of energy efficiency and the implementation of efficient energy management strategies. Artificial intelligence (AI), a prominent machine learning paradigm, has gained significant traction in control applications and found extensive utility in various energy-related domains. The utilization of AI techniques for addressing energy-related challenges is favored due to their aptitude for handling complex and nonlinear data structures. Based on the preliminary inquiries, it has been observed that predictive analytics, prominently driven by artificial neural network (ANN) algorithms, assumes a crucial position in energy management across various sectors. This paper presents a comprehensive bibliometric analysis to gain deeper insights into the progression of AI in energy research from 2003 to 2023. AI models can be used to accurately predict energy consumption, load profiles, and resource planning, ensuring consistent performance and efficient resource utilization. This review article summarizes the existing literature on the implementation of AI in the development of energy management systems. Additionally, it explores the challenges and potential areas of research in applying ANN to energy system management. The study demonstrates that ANN can effectively address integration issues between energy and power systems, such as solar and wind forecasting, power system frequency analysis and control, and transient stability assessment. Based on the comprehensive state-of-the-art study, it can be inferred that the implementation of AI has consistently led to energy reductions exceeding 25%. Furthermore, this article discusses future research directions in this field.  
Exploring the feasibility of dimethyl ether (DME) and LPG fuel blend for small diesel engine: A simulation perspective Nguyen, Thoai Anh; Pham, Thi Yen; Le, Huu Cuong; Nguyen, Van Giao; Nguyen, Lan Huong
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60250

Abstract

There is a looming global crisis owing to the increase in greenhouse gases and the escalating fossil fuel process.  The issue is further compounded by the ongoing conflicts in different places in the world. Hence, there is an urgent need for a bouquet of alternative fuels suitable to power the incumbent internal combustion engine. Among various options available Dimethyl Ether (DME) is a friendly environment fuel, easy to liquefy, and suitable for use in diesel engines, while Liquefied Petroleum Gas (LPG) is another potential alternative fuel suitable for internal combustion engines. The present study is an endeavor to investigate the characteristics of a diesel engine powered with DME-diesel blends as pilot fuel while LPG was used as the main fuel.  During engine testing, different blends of diesel-DME were used containing 0%, 25%, 50%, and 75% DME. The AVL Boost software was employed for modeling the engine performance and tailpipe emission. The test fuel combination was successful in running the engine sans any abnormality in sound or performance. The results showed carbon monoxide (CO) and hydrocarbon (HC) emissions were reduced using the test fuel combination while there was a marginal increase in the oxides of nitrogen (NOx) levels. In general, the combination of DME and LPG could be considered as a potential and promising solution to reducing pollutant emissions.
Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms Le, Thanh Tuan; Paramasivam, Prabhu; Adril, Elvis; Nguyen, Van Quy; Le, Minh Xuan; Duong, Minh Thai; Le, Huu Cuong; Nguyen, Anh Quan
International Journal of Renewable Energy Development Vol 13, No 4 (2024): July 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60387

Abstract

This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects
Nanotechnology-based biodiesel: A comprehensive review on production, and utilization in diesel engine as a substitute of diesel fuel Le, Thanh Tuan; Tran, Minh Ho; Nguyen, Quang Chien; Le, Huu Cuong; Nguyen, Van Quy; Cao, Dao Nam; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 13, No 3 (2024): May 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60126

Abstract

As a sustainable replacement for fossil fuels, biodiesel is a game-changer in the energy sector. There is no strategy to minimize biodiesel's significance as a sustainable, clean fuel source in light of the increasing climate change and environmental sustainability concerns. Nevertheless, conventional biodiesel production methods often run into problems like inadequate conversion efficiency and inappropriate fuel properties, which impede their broad adoption. The revolutionary potential of nanotechnology to circumvent these limitations and revolutionize biodiesel consumption and production is explored in this review paper. There are new possibilities for improving biodiesel output and engine efficiency, thanks to nanotechnology, which can alter matter at the atomic and molecular levels. Using nano-catalysts, nano-emulsification processes, and nano-encapsulation procedures, researchers have made significant advances in improving biodiesel qualities such as stability, combustion efficiency, and viscosity. Through a comprehensive analysis of current literature and research data, this article elucidates the crucial role of nanotechnology in advancing biodiesel technology. By shedding light on the most current advancements, challenges, and potential future outcomes in nano-based biodiesel manufacturing and consumption, this review hopes to add to the growing corpus of knowledge in the field and inspire additional innovation. In conclusion, there is great hope for a sustainable energy future, increased economic growth, and reduced environmental impacts through the application of nanotechnology.  
Using hydrogen as potential fuel for internal combustion engines: A comprehensive assessment Long Huynh, Diep Ngoc; Nguyen, Thanh Hai; Nguyen, Duc Chuan; Vo, Anh Vu; Nguyen, Duy Tan; Nguyen, Van Quy; Le, Huu Cuong
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60707

Abstract

This comprehensive review explores the feasibility and potential of using hydrogen gas as a fuel for internal combustion engines, a topic of growing importance in the context of global efforts to reduce greenhouse gas emissions and transition towards sustainable energy sources. Hydrogen, known for its high energy content and clean combustion properties, presents a promising alternative to traditional fossil fuels. This paper examines the chemical properties of hydrogen and its benefits over conventional fuels, particularly focusing on the technological advancements and modifications required for compression ignition and spark ignition engines to efficiently utilize hydrogen. The review delves into the necessary engine design modification, fuel injection systems, combustion characteristics, and emission control technologies specific to both compression ignition and spark ignition engines. Furthermore, it addresses the environmental impacts, including reductions in greenhouse gases and other pollutants, and evaluates the economic implications, such as production costs and feasibility compared to other energy solutions. Key challenges associated with the storage, distribution, and safety of hydrogen are discussed, along with potential solutions and innovations currently under investigation. This paper aims to provide a thorough understanding of the current state of hydrogen as a promising fuel for internal combustion engines, guiding future research and development in this vital field.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation Vu, Van Vien; Le, Phuoc Tai; Do, Thi Mai Thom; Nguyen, Thi Thuy Hieu; Tran, Nguyen Bao Minh; Paramasivam, Prabhu; Le, Thi Thai; Le, Huu Cuong; Chau, Thanh Hieu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2641

Abstract

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.
Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy Nguyen, Tien Han; Paramasivam, Prabhu; Dong, Van Huong; Le, Huu Cuong; Nguyen, Duc Chuan
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2637

Abstract

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.
Artificial intelligence applications in solar energy Le, Thanh Tuan; Le, Thi Thai; Le, Huu Cuong; Dong, Van Huong; Paramasivam, Prabhu; Chung, Nghia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2686

Abstract

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.
Co-Authors Cao, Dao Nam Chau, Thanh Hieu Chung, Nghia Do, Thi Mai Thom Dong, Van Huong Duong, Minh Thai Elvis Adril Hoang, Long Vuong Le, Minh Xuan Le, Nguyen Viet Linh Le, Phuoc Tai Le, Thanh Tuan Le, Thi Thai Long Huynh, Diep Ngoc Nguyen, Anh Quan Nguyen, Danh Chan Nguyen, Duc Chuan Nguyen, Duy Tan Nguyen, Lan Huong Nguyen, Minh Nhat Nguyen, Quang Chien Nguyen, Thanh Hai Nguyen, Thi Thuy Hieu Nguyen, Thoai Anh Nguyen, Tien Han Nguyen, Van Giao Nguyen, Van Quy Paramasivam, Prabhu Pham, Thi Yen Priya, Jayabal Chandra Tran, Minh Ho Tran, Nguyen Bao Minh Truong, Thanh Hai Vo, Anh Vu Vu, Van Vien