View in EDS PDF Full Text

Advances in Energy Research. Volume 30

Saved in:
Bibliographic Details
Title: Advances in Energy Research. Volume 30
Description: In this compilation, the authors first present a study in which computational design is performed, using empirical data, to fit physical models to extract transport and material parameters (which are then used in 1D continuum and 3D particle models of charge transport) to validate against empirical measurement and each other prior to use in extrapolation studies. Next, the book aims to discuss and illustrate the key trends behind the current international and European Union energy and climate policy. The authors provide insights into current dynamics, enabling a better understanding of future developments and indicating that unless a global effort to reduce greenhouse gas emissions is made, emissions will continue to rise. The authors also present a computer algorithm based on type-1 fuzzy logic control strategies to manage the flow of energy in stand-alone PV/Wind/Battery hybrid systems. The solar and wind energies were combined together to increase system's efficiency and batteries were used to ensure the availability of power on demand and improve the dynamic behavior of the system. Both traditional and state-of-the-art proteomics techniques used for quantification of corn stover hydrolyzing enzymes are presented in the following chapter. The quantitative expression of cellulolytic and hemicellulolytic enzymes secreted by different microbes during corn stover hydrolysis is discussed, and an attempt is made to link the substrate complexity and quantitative composition of lignocellulolytic enzymes produced by microbes. Later, an algorithm based on artificial neural network (ANN) and data envelopment analysis (DEA) is proposed for analyzing and assessing industrial sectors for energy potential. For illustrative purposes, energy use in the South African industrial sector between 1993 and 2025 was presented as a case study. The closing study reviews the merits of these Artificial Neural Network (ANN) and Data Envelopment Analysis to develop a new hybrid model to determine how much energy could be conserved in the residential sector. The model was applied to the United States residential sector from 1984 to 2010 and it was discovered that 7.5% of energy consumed could be conserved.
Authors: Acosta, Morena J.
Resource Type: eBook.
Subjects: Power resources--Research--Data processing, Power resources--Research
Categories: SCIENCE / Energy
Database: eBook Collection (EBSCOhost)
FullText Links:
  – Type: ebook-pdf
Text:
  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 1901474
RelevancyScore: 1084
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1083.55249023438
IllustrationInfo
ImageInfo – Size: thumb
  Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$1901474$PDF&s=r
– Size: medium
  Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$1901474$PDF&s=d
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Advances in Energy Research. Volume 30
– Name: Abstract
  Label: Description
  Group: Ab
  Data: In this compilation, the authors first present a study in which computational design is performed, using empirical data, to fit physical models to extract transport and material parameters (which are then used in 1D continuum and 3D particle models of charge transport) to validate against empirical measurement and each other prior to use in extrapolation studies. Next, the book aims to discuss and illustrate the key trends behind the current international and European Union energy and climate policy. The authors provide insights into current dynamics, enabling a better understanding of future developments and indicating that unless a global effort to reduce greenhouse gas emissions is made, emissions will continue to rise. The authors also present a computer algorithm based on type-1 fuzzy logic control strategies to manage the flow of energy in stand-alone PV/Wind/Battery hybrid systems. The solar and wind energies were combined together to increase system's efficiency and batteries were used to ensure the availability of power on demand and improve the dynamic behavior of the system. Both traditional and state-of-the-art proteomics techniques used for quantification of corn stover hydrolyzing enzymes are presented in the following chapter. The quantitative expression of cellulolytic and hemicellulolytic enzymes secreted by different microbes during corn stover hydrolysis is discussed, and an attempt is made to link the substrate complexity and quantitative composition of lignocellulolytic enzymes produced by microbes. Later, an algorithm based on artificial neural network (ANN) and data envelopment analysis (DEA) is proposed for analyzing and assessing industrial sectors for energy potential. For illustrative purposes, energy use in the South African industrial sector between 1993 and 2025 was presented as a case study. The closing study reviews the merits of these Artificial Neural Network (ANN) and Data Envelopment Analysis to develop a new hybrid model to determine how much energy could be conserved in the residential sector. The model was applied to the United States residential sector from 1984 to 2010 and it was discovered that 7.5% of energy consumed could be conserved.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Acosta%2C+Morena+J%2E%22">Acosta, Morena J.</searchLink>
– Name: TypePub
  Label: Resource Type
  Group: TypPub
  Data: eBook.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Power+resources--Research--Data+processing%22">Power resources--Research--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Power+resources--Research%22">Power resources--Research</searchLink>
– Name: SubjectBISAC
  Label: Categories
  Group: Su
  Data: <searchLink fieldCode="ZK" term="%22SCIENCE+%2F+Energy%22">SCIENCE / Energy</searchLink>
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1901474
RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 621.042
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Power resources--Research--Data processing
        Type: general
      – SubjectFull: Power resources--Research
        Type: general
    Titles:
      – TitleFull: Advances in Energy Research. Volume 30
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Acosta, Morena J.
      – PersonEntity:
          Name:
            NameFull: Acosta, Morena J.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2018
            – D: 26
              M: 09
              Type: profile
              Y: 2018
          Identifiers:
            – Type: isbn-print
              Value: 9781536142143
            – Type: isbn-electronic
              Value: 9781536142150
          Numbering:
            – Type: volume
              Value: 00030
          Titles:
            – TitleFull: Advances in Energy Research. Volume 30
              Type: main
ResultId 1