Modeling-optimization of performance and emission characteristics of dual-fuel engine powered with pilot diesel and agricultural-food waste-derived biogas

Said, Zafar; Sharma, Prabhakar; Bora, Bhaskor Jyoti; Nguyen, Van Nhanh; Bui, Thi Anh Em; Nguyen, Dinh Tuyen; Dinh, Xuan Thanh; Nguyen, Xuan Phuong

doi:10.1016/j.ijhydene.2022.07.150

Cited by 37 publications

(10 citation statements)

References 96 publications

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“…The availability of significant quantities of volatile material enhances volatility as well as reactivity, favoring bio-oil production . Studies conducted on different biomass revealed that biomass with higher AC resulted in lower bio-oil and higher biochar yields and vice versa. ,− The low MC observed in the samples makes them well suited for the pyrolysis process. This characteristic is advantageous as it requires less energy to vaporize the biomass in the reactor, thereby enhancing the generation of thermal energy and conversion efficiency during pyrolysis .…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Characterization of Some Selected Biomass for Bioenergy Production

Yahya,

Adeleke,

Nzerem

et al. 2023

ACS Omega

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There is a lack of information about the detailed characterization of biomass of Nigerian origin. This study presents a comprehensive characterization of six biomass, groundnut shells, corncob, cashew leaves, Ixora coccinea (flame of the woods), sawdust, and lemongrass, to aid appropriate selection for bio-oil production. The proximate, ultimate, calorific value and compositional analyses were carried out following the American Standard for Testing and Materials (ASTM) standards. Fourier transform infrared spectroscopy, thermogravimetric analysis, scanning electron microscopy with energy-dispersive X-ray spectroscopy, and Xray fluorescence were employed in this study for functional group analyses, thermal stability, and structural analyses. The H/C and O/C atomic ratios, fuel ratio, ignitability index, and combustibility index of the biomass samples were evaluated. Groundnut shells, cashew leaves, and lemongrass were identified as promising feedstocks for bio-oil production based on their calorific values (>20 MJ/kg). Sawdust exhibited favorable characteristics for bio-oil production as indicated by its higher volatile matter (79.28%), low ash content (1.53%), low moisture content (6.18%), and high fixed carbon content (13.01%). Also, all samples showed favorable ignition and flammability properties. The low nitrogen (<0.12%) and sulfur (<0.04%) contents in the samples make them environmentally benign fuels as a lower percentage of NO x and SO x will be released during the production of the bio-oil. These results are contributions to the advancement of a sustainable and efficient carbon-neutral energy mix, promoting biomass resource utilization for the generation of energy.

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Section: Resultsmentioning

confidence: 99%

Comprehensive Characterization of Some Selected Biomass for Bioenergy Production

Yahya,

Adeleke,

Nzerem

et al. 2023

ACS Omega

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“…As the principal source of energy, traditional fossil fuels continue to hold a dominant position [199]. Because of the ever-increasing demand and the ever-increasing population of the world, the dwindling stocks of fossil fuels provide a problem for the future supply of these resources [200], [201]. The ever-increasing need for energy may be satisfied by the effective production of biofuel that could be used for transportation means [202], [203].…”

Section: ) Biofuel-based Energy Forecastingmentioning

confidence: 99%

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Nguyen,

Paramasivam,

Dong

et al. 2024

JOIV : Int. J. Inform. Visualization

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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.

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“…Machine learning algorithms can enhance the precision and dependability of predictive maintenance forecasts by integrating feedback loops and supplementary data sources, including personnel feedback, environmental conditions, and operational parameters. Such outcomes may result in enhanced risk mitigation strategies and improved decisionmaking [107], [108].…”

Section: ) Autonomous Navigation and Shippingmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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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.

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Modeling-optimization of performance and emission characteristics of dual-fuel engine powered with pilot diesel and agricultural-food waste-derived biogas

Cited by 37 publications

References 96 publications

Comprehensive Characterization of Some Selected Biomass for Bioenergy Production

Comprehensive Characterization of Some Selected Biomass for Bioenergy Production

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

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