Development of an artificial neural network EMMS drag model for the simulation of fluidized beds in chemical looping combustion

Stamatopoulos, P.; Stefanitsis, D.; Zeneli, M.; Nikolopoulos, N.

doi:10.1016/j.ces.2023.119286

Cited by 6 publications

(3 citation statements)

References 50 publications

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“…ML has been applied to the chemical looping and related fields with positive results. Supervised learning is the primary method used in this field, with representative models including k -nearest neighbors (KNN), , support vector regression (SVR), , decision tree (DT), , and artificial neural network (ANN). − Table summarizes the predictive performance of these models in chemical looping. The prediction accuracy of the model will vary significantly with different sample sizes and feature dimensions.…”

Section: Methodologiesmentioning

confidence: 99%

Machine Learning for Chemical Looping: Recent Advances and Prospects

Song,

Teng,

Fang

et al. 2024

Energy Fuels

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Section: Methodologiesmentioning

confidence: 99%

Machine Learning for Chemical Looping: Recent Advances and Prospects

Song,

Teng,

Fang

et al. 2024

Energy Fuels

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“…This approach optimizes the integration of energy and mass flows, identifies key process parameters, and determines optimal operational strategies. 162 This reduces costs and aids in the development of BCLHP technology. Berrouk et al 163 developed models for three distinct processes based on biomass gasification for the co-production of H 2 and power, and they conducted techno-economic assessments to evaluate the capital and operating costs, as well as the levelized cost of energy.…”

Section: Development Of the Reactorsmentioning

confidence: 99%

“…Simulation studies are seen as effective for addressing challenges in CLHP. This approach optimizes the integration of energy and mass flows, identifies key process parameters, and determines optimal operational strategies . This reduces costs and aids in the development of BCLHP technology.…”

Section: Progress In the Bclhp Technologymentioning

confidence: 99%

Hydrogen Production from Biomass-Based Chemical Looping: A Critical Review and Perspectives

Qiu,

Zeng,

Xiao

2024

Energy Fuels

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Biomass-based chemical looping (BCL) emerges as a highly prospective technique for generating high-purity H 2 , distinguished by its minimal energy penalties and potential for negative carbon emissions. However, its industrial implementation faces significant challenges owing to the intricate interactions in solid−gas reactions and the heat and mass management within the reactors. This work comprehensively reviews the current advancements in BCL H 2 production (BCLHP), emphasizing oxygen carrier selection, reactor design, system integration, and technoeconomic assessments. It highlights that BCLHP surpasses traditional biomass gasification methods in efficiency, resulting in reduced costs and net negative CO 2 emissions (−17.00 kg of CO 2 / kg of H 2 ). Fe-based oxides are deemed the most appropriate oxygen carriers; however, their utilization is constrained by high fabrication costs and the absence of scalable synthesis methods. Additionally, the uneven and intricate heat and mass transfer present substantial obstacles to scaling up the redox reactors. These issues collectively elevate construction costs and limit the application of BCLHP to laboratory or pilot scales. Significant technological challenges and research gaps in experimental and modeling studies undermine the reliability of simulation outcomes. Therefore, addressing these challenges necessitates developing suitable oxygen carriers and their scalable production methods, stable redox reactors, and overall system scalability. In conclusion, while BCLHP holds considerable promise for high-purity H 2 production and negative emissions, advancing it to practical applications will require innovative advancements in both the experimental and modeling domains.

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Numerical evaluation and optimization of volatiles distributors with different configurations for biomass chemical looping combustion

Wang,

Li,

et al. 2024

Chemical Engineering Journal

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Development of an artificial neural network EMMS drag model for the simulation of fluidized beds in chemical looping combustion

Cited by 6 publications

References 50 publications

Machine Learning for Chemical Looping: Recent Advances and Prospects

Machine Learning for Chemical Looping: Recent Advances and Prospects

Hydrogen Production from Biomass-Based Chemical Looping: A Critical Review and Perspectives

Numerical evaluation and optimization of volatiles distributors with different configurations for biomass chemical looping combustion

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