Phoneme Recognition Using Sparse Random Projections and Ensemble Classifiers

Atsonios, Ioannis

doi:10.1007/978-3-642-11509-7_25

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…On the other hand, the CO 2 specific emission factor was significantly reduced by about 3.24 times (from 267.45 to 82.50 kg/MWh) corresponding to a plant decarbonization rate of 70%. The main reason that the decarbonization rate is not higher than 70% represents the unconverted methane and carbon monoxide in reformer and shift reactors [18]. These chemical compounds are not removed by the chemical scrubbing unit being then combusted in the burner to provide the heat duty required by the reforming reaction.…”

Section: Resultsmentioning

confidence: 99%

Untitled

2020

Studia UBB Chemia

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Hydrogen is foreseen as a promising energy vector for the future on the road to develop a low carbon economy. In this respect, developing energy-efficient improved hydrogen production systems with low CO2 emissions is an important aspect. This study is evaluating the most relevant economic and environmental impact elements for hydrogen production based on catalytic natural gas steam reforming. As an illustrative example, an industrial-relevant hydrogen production plant with an output of 50000 Nm 3 /h was investigated. In addition, a pre-combustion carbon capture feature, based on reactive gas-liquid absorption using alkanolamines (Methyl-DiEthanol-Amine -MDEA), was considered to reduce the CO2 emissions. The overall carbon capture rate was 70%. Similar natural gas reforming plant without decarbonization feature is also discussed to quantify the efficiency and economic penalties for CO2 capture. As assessment methods, computational tools, thermal integration analysis and an in-depth techno-economic and environmental procedure were used. For instance, to quantify the overall environmental impact, Life Cycle Assessment (LCA) was used. Various relevant technical, economic and environmental indicators are calculated and discussed in the present work.

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Section: Resultsmentioning

confidence: 99%

Untitled

2020

Studia UBB Chemia

View full text Add to dashboard Buy / Rent full text

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Phoneme Recognition Using Sparse Random Projections and Ensemble Classifiers

Cited by 2 publications

References 8 publications

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