Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning

Jeon, Pil Rip; Moon, Jong-Ho; Ogunsola, Nafiu Olanrewaju; Lee, See Hoon; Ling, Jester Lih Jie; You, Siming; Park, Young-Kwon

doi:10.1016/j.cej.2023.144503

Cited by 24 publications

(3 citation statements)

References 114 publications

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“…In the ever-evolving landscape of fuel development, the substantial influence of ML is being acknowledged. , A significant surge in efficiency, sustainability, and innovation in fuel development is being observed, owing to the integration of advanced ML algorithms . An exploration into the multifaceted impact of ML on fuel development is explored in this section, underlining its pivotal role in reshaping and enhancing the industry.…”

Section: Energy and Fuelsmentioning

confidence: 99%

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

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This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for their roles in sustainable energy and fuel sectors. These polymers, when integrated with ML techniques, exhibit enhanced functionalities, optimizing renewable energy systems, storage, and conversion. Detailed case studies reveal the potential of biobased polymers in energy applications and the fuel industry, further showcasing how ML bolsters fuel efficiency and innovation. The intersection of biobased polymers and ML also marks advancements in biochemical production, emphasizing innovations in drug delivery and medical device development. This review underscores the imperative of harnessing the convergence of ML and biobased polymers for future global sustainability endeavors in energy, fuels, and biochemicals. The collective evidence presented asserts the immense promise this union holds for steering a sustainable and innovative trajectory.

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Section: Energy and Fuelsmentioning

confidence: 99%

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Bin Abu Sofian,

Sun,

Gupta

et al. 2024

Energy Fuels

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“…Consequently, this might result in the creation of greater output with less amount of land, thus protecting more area for the production of food.  Through the use of machine learning models, one may improve their understanding of the complex biochemical processes that are involved in the synthesis of biofuels [276]. Due to the fact that these models are able to forecast the most efficient processing methods and ideal conditions for converting biomass into biofuels, they are able to reduce the amount of energy that is used and the amount of waste that is produced throughout the manufacturing process [277].…”

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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“…They can make compelling predictions without a precise mathematical relationship between the input and output features. They have been proven to be alternatives to traditional modeling techniques for studying and understanding complex processes [8], which demonstrates significant potential in predicting the physicochemical properties of hydrochar.…”

Section: Introductionmentioning

confidence: 99%

Improved Dujiangyan Irrigation System Optimization (IDISO): A Novel Metaheuristic Algorithm for Hydrochar Characteristics

Shi,

Zhang,

Sui

et al. 2024

Processes

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Hyperparameter tuning is crucial in the development of machine learning models. This study introduces the nonlinear shrinking factor and the Cauchy mutation mechanism to improve the Dujiangyan Irrigation System Optimization (DISO), proposing the improved Dujiangyan Irrigation System Optimization algorithm (IDISO) for hyperparameter tuning in machine learning. The optimization capabilities and convergence performance of IDISO were validated on 87 CEC2017 benchmark functions of varying dimensions and nine real-world engineering problems, demonstrating that it significantly outperforms DISO in terms of convergence speed and accuracy, and ranks first in overall performance among the seventeen advanced metaheuristic algorithms being compared. To construct a robust and generalizable prediction model for hydrochar element characteristics, this study utilized IDISO and DISO algorithms to fine-tune the parameters of the XGBoost model. The experimental results show that the IDISO-XGBoost model achieved an average prediction performance of 0.95, which represents a 4% improvement over the DISO-XGBoost model. These results indicate that the IDISO algorithm has significant potential and value in practical applications.

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Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning

Cited by 24 publications

References 114 publications

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

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

Improved Dujiangyan Irrigation System Optimization (IDISO): A Novel Metaheuristic Algorithm for Hydrochar Characteristics

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