首页|New Data from Aston University Illuminate Findings in Machine Learning (Machine Learning Facilitated the Modeling of Plastics Hydrothermal Pretreatment Toward C onstructing an On-ship Marine Litter-to-methanol Plant)
New Data from Aston University Illuminate Findings in Machine Learning (Machine Learning Facilitated the Modeling of Plastics Hydrothermal Pretreatment Toward C onstructing an On-ship Marine Litter-to-methanol Plant)
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Current study results on Machine Learn ing have been published. According to news reporting from Birmingham, United Kin gdom, by NewsRx journalists, research stated, "An onboard facility shows promise in efficiently converting floating plastics into valuable products, such as met hanol, negating the need for regional transport and land-based treatment. Gasifi cation presents an effective means of processing plastics, requiring their trans formation into gasification-compatible feedstock, such as hydrochar." Financial support for this research came from Marie Sklstrok;odowska Curie Actio ns Fellowships by The European Research Executive Agency, Belguim. The news correspondents obtained a quote from the research from Aston University , "This study explores hydrochar composition modeling, utilizing advanced algori thms and rigorous analyses to unravel the intricacies of elemental composition r atios, identify influential factors, and optimize hydrochar production processes . The investigation begins with decision tree modeling, which successfully captu res relationships but encounters overfitting challenges. Nevertheless, the decis ion tree vote analysis, particularly for the H/C ratio, yielding an impressive R 2 of 0.9376. Moreover, the research delves into the economic feasibility of the marine plastics-to-methanol process. Varying payback periods, driven by fluctuat ing methanol prices observed over a decade (ranging from 3.3 to 7 yr for hydroch ar production plants), are revealed. Onboard factories emerge as resilient solut ions, capitalizing on marine natural gas resources while striving for near-net-z ero emissions."
BirminghamUnited KingdomEuropeAlco holsCyborgsEmerging TechnologiesMachine LearningMethanolAston Universi ty
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