Advances in energy research.: Volume 30 /
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...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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New York :
Nova Science Publishers, Inc.,
2018.
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| Zusammenfassung: | 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. |
| Beschreibung: | Includes index. |
| Beschreibung: | 1 online resource |
| ISBN: | 9781536142150 1536142158 |
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| 520 | |a 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. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed September 25, 2018). | |
| 505 | 0 | |a Intro; Contents; Preface; Chapter 1; A Computational Design of Nanocomposite Polymeric Films for Energy Storage; Abstract; Introduction; Effective Permittivity Mixing Rules; Experimental; Charge Injection Mechanism; Charge Transport Mechanism; Multiscale Modeling; Simulation Algorithm; Problem Formulation; Field Solution with the Boundary Integral Equation Method; Time Integration Strategy; Charge Attachment/Detachment; Recombination; Results and Discussion; 1D Continuum Results; 3D Particle Simulation Results; Conclusion; Acknowledgments; References; Chapter 2 | |
| 505 | 8 | |a 4.1.4.2. Battery Discharging4.1.5. Application of the Fuzzy Controller; 4.1.5.1. Fuzzification Phase; 4.1.5.2. Inference Rules; 4.1.5.3. Deffuzification Phase; 4.1.6. Results and Discussions; 4.2. System 2: PV/Wind/Battery Stand-Alone System for a Wide Isolated Region; 4.2.1. Load Demand Acquisition; 4.2.2. Configuration of the Proposed Stand-Alone PV/Wind/ Battery System; 4.2.2.1. PV Panels; 4.2.2.2. Wind Turbine; 4.2.2.3. Batteries; 4.2.3. PV Power Prediction; 4.2.4. WT Power Prediction; 4.2.5. Application of the Fuzzy Controller; 4.2.5.1. Fuzzification Phase; 4.2.5.2. Inference Rules | |
| 505 | 8 | |a 4.2.5.3. Deffuzification Phase4.2.6. Results and Discussions; 4.2.6.1. Empty Batteries; 4.2.6.2. Full Batteries; Conclusion; References; Chapter 4; Proteomics for Bioenergy Production from Corn Stover; Abstract; Introduction; Proteomic Techniques for Enzyme Quantification; Corn Stover Hydrolyzing Enzymes; Biofuel and Value-Added Products from Corn Stover; Summary and Perspective; References; Chapter 5; ANN-DEA for Assessment and Optimization of Energy Consumption; Abstract; 1. Introduction; 2. Proposed Model; 2.1. ANN; 2.1.1. Problem Formulation; 2.2. Linear Regression Analysis; 2.3. DEA | |
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| 880 | 8 | |6 505-00/(S |a Key Trends and Drivers Influencing Energy and Climate PolicyAbstract; Introduction; Κey Drivers for Energy and Climate Policies; Political Drivers; Socio-Economic Drivers; Drivers of the Economic Crisis; Gross Domestic Product Development; Trends in Energy Prices and Energy Consumption; Energy Prices; 2016 Electricity Prices; 2016 Natural Gas Prices; Energy Consumption; Impacts Analysis of Selected Energy and Climate Policies; Trade and Leakage; Renewable Energy Sources; Capacity Increase; Renewables Cost; Actual RES Costs; Future Perspective; Renewable Energy Support Schemes; Conclusion | |
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| contents | Intro; Contents; Preface; Chapter 1; A Computational Design of Nanocomposite Polymeric Films for Energy Storage; Abstract; Introduction; Effective Permittivity Mixing Rules; Experimental; Charge Injection Mechanism; Charge Transport Mechanism; Multiscale Modeling; Simulation Algorithm; Problem Formulation; Field Solution with the Boundary Integral Equation Method; Time Integration Strategy; Charge Attachment/Detachment; Recombination; Results and Discussion; 1D Continuum Results; 3D Particle Simulation Results; Conclusion; Acknowledgments; References; Chapter 2 4.1.4.2. Battery Discharging4.1.5. Application of the Fuzzy Controller; 4.1.5.1. Fuzzification Phase; 4.1.5.2. Inference Rules; 4.1.5.3. Deffuzification Phase; 4.1.6. Results and Discussions; 4.2. System 2: PV/Wind/Battery Stand-Alone System for a Wide Isolated Region; 4.2.1. Load Demand Acquisition; 4.2.2. Configuration of the Proposed Stand-Alone PV/Wind/ Battery System; 4.2.2.1. PV Panels; 4.2.2.2. Wind Turbine; 4.2.2.3. Batteries; 4.2.3. PV Power Prediction; 4.2.4. WT Power Prediction; 4.2.5. Application of the Fuzzy Controller; 4.2.5.1. Fuzzification Phase; 4.2.5.2. Inference Rules 4.2.5.3. Deffuzification Phase4.2.6. Results and Discussions; 4.2.6.1. Empty Batteries; 4.2.6.2. Full Batteries; Conclusion; References; Chapter 4; Proteomics for Bioenergy Production from Corn Stover; Abstract; Introduction; Proteomic Techniques for Enzyme Quantification; Corn Stover Hydrolyzing Enzymes; Biofuel and Value-Added Products from Corn Stover; Summary and Perspective; References; Chapter 5; ANN-DEA for Assessment and Optimization of Energy Consumption; Abstract; 1. Introduction; 2. Proposed Model; 2.1. ANN; 2.1.1. Problem Formulation; 2.2. Linear Regression Analysis; 2.3. DEA |
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| discipline | Energietechnik |
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| id | ZDB-4-EBA-on1054092919 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:09Z |
| institution | BVB |
| isbn | 9781536142150 1536142158 |
| language | English |
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| publisher | Nova Science Publishers, Inc., |
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| spelling | Advances in energy research. Volume 30 / Morena J. Acosta, editor. New York : Nova Science Publishers, Inc., 2018. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes index. 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. Online resource; title from PDF title page (EBSCO, viewed September 25, 2018). Intro; Contents; Preface; Chapter 1; A Computational Design of Nanocomposite Polymeric Films for Energy Storage; Abstract; Introduction; Effective Permittivity Mixing Rules; Experimental; Charge Injection Mechanism; Charge Transport Mechanism; Multiscale Modeling; Simulation Algorithm; Problem Formulation; Field Solution with the Boundary Integral Equation Method; Time Integration Strategy; Charge Attachment/Detachment; Recombination; Results and Discussion; 1D Continuum Results; 3D Particle Simulation Results; Conclusion; Acknowledgments; References; Chapter 2 4.1.4.2. Battery Discharging4.1.5. Application of the Fuzzy Controller; 4.1.5.1. Fuzzification Phase; 4.1.5.2. Inference Rules; 4.1.5.3. Deffuzification Phase; 4.1.6. Results and Discussions; 4.2. System 2: PV/Wind/Battery Stand-Alone System for a Wide Isolated Region; 4.2.1. Load Demand Acquisition; 4.2.2. Configuration of the Proposed Stand-Alone PV/Wind/ Battery System; 4.2.2.1. PV Panels; 4.2.2.2. Wind Turbine; 4.2.2.3. Batteries; 4.2.3. PV Power Prediction; 4.2.4. WT Power Prediction; 4.2.5. Application of the Fuzzy Controller; 4.2.5.1. Fuzzification Phase; 4.2.5.2. Inference Rules 4.2.5.3. Deffuzification Phase4.2.6. Results and Discussions; 4.2.6.1. Empty Batteries; 4.2.6.2. Full Batteries; Conclusion; References; Chapter 4; Proteomics for Bioenergy Production from Corn Stover; Abstract; Introduction; Proteomic Techniques for Enzyme Quantification; Corn Stover Hydrolyzing Enzymes; Biofuel and Value-Added Products from Corn Stover; Summary and Perspective; References; Chapter 5; ANN-DEA for Assessment and Optimization of Energy Consumption; Abstract; 1. Introduction; 2. Proposed Model; 2.1. ANN; 2.1.1. Problem Formulation; 2.2. Linear Regression Analysis; 2.3. DEA Power resources Research. http://id.loc.gov/authorities/subjects/sh85105997 Power resources Research Data processing. Ressources énergétiques Recherche. Ressources énergétiques Recherche Informatique. TECHNOLOGY & ENGINEERING Mechanical. bisacsh Power resources Research fast Power resources Research Data processing fast Acosta, Morena J., editor. has work: Volume 30 Advances in energy research (Work) https://id.oclc.org/worldcat/entity/E39PCXhDdCXymPyHck4T8yX3DC https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1901474 Volltext 505-00/(S Key Trends and Drivers Influencing Energy and Climate PolicyAbstract; Introduction; Κey Drivers for Energy and Climate Policies; Political Drivers; Socio-Economic Drivers; Drivers of the Economic Crisis; Gross Domestic Product Development; Trends in Energy Prices and Energy Consumption; Energy Prices; 2016 Electricity Prices; 2016 Natural Gas Prices; Energy Consumption; Impacts Analysis of Selected Energy and Climate Policies; Trade and Leakage; Renewable Energy Sources; Capacity Increase; Renewables Cost; Actual RES Costs; Future Perspective; Renewable Energy Support Schemes; Conclusion |
| spellingShingle | Advances in energy research. Intro; Contents; Preface; Chapter 1; A Computational Design of Nanocomposite Polymeric Films for Energy Storage; Abstract; Introduction; Effective Permittivity Mixing Rules; Experimental; Charge Injection Mechanism; Charge Transport Mechanism; Multiscale Modeling; Simulation Algorithm; Problem Formulation; Field Solution with the Boundary Integral Equation Method; Time Integration Strategy; Charge Attachment/Detachment; Recombination; Results and Discussion; 1D Continuum Results; 3D Particle Simulation Results; Conclusion; Acknowledgments; References; Chapter 2 4.1.4.2. Battery Discharging4.1.5. Application of the Fuzzy Controller; 4.1.5.1. Fuzzification Phase; 4.1.5.2. Inference Rules; 4.1.5.3. Deffuzification Phase; 4.1.6. Results and Discussions; 4.2. System 2: PV/Wind/Battery Stand-Alone System for a Wide Isolated Region; 4.2.1. Load Demand Acquisition; 4.2.2. Configuration of the Proposed Stand-Alone PV/Wind/ Battery System; 4.2.2.1. PV Panels; 4.2.2.2. Wind Turbine; 4.2.2.3. Batteries; 4.2.3. PV Power Prediction; 4.2.4. WT Power Prediction; 4.2.5. Application of the Fuzzy Controller; 4.2.5.1. Fuzzification Phase; 4.2.5.2. Inference Rules 4.2.5.3. Deffuzification Phase4.2.6. Results and Discussions; 4.2.6.1. Empty Batteries; 4.2.6.2. Full Batteries; Conclusion; References; Chapter 4; Proteomics for Bioenergy Production from Corn Stover; Abstract; Introduction; Proteomic Techniques for Enzyme Quantification; Corn Stover Hydrolyzing Enzymes; Biofuel and Value-Added Products from Corn Stover; Summary and Perspective; References; Chapter 5; ANN-DEA for Assessment and Optimization of Energy Consumption; Abstract; 1. Introduction; 2. Proposed Model; 2.1. ANN; 2.1.1. Problem Formulation; 2.2. Linear Regression Analysis; 2.3. DEA Power resources Research. http://id.loc.gov/authorities/subjects/sh85105997 Power resources Research Data processing. Ressources énergétiques Recherche. Ressources énergétiques Recherche Informatique. TECHNOLOGY & ENGINEERING Mechanical. bisacsh Power resources Research fast Power resources Research Data processing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85105997 |
| title | Advances in energy research. |
| title_auth | Advances in energy research. |
| title_exact_search | Advances in energy research. |
| title_full | Advances in energy research. Volume 30 / Morena J. Acosta, editor. |
| title_fullStr | Advances in energy research. Volume 30 / Morena J. Acosta, editor. |
| title_full_unstemmed | Advances in energy research. Volume 30 / Morena J. Acosta, editor. |
| title_short | Advances in energy research. |
| title_sort | advances in energy research |
| topic | Power resources Research. http://id.loc.gov/authorities/subjects/sh85105997 Power resources Research Data processing. Ressources énergétiques Recherche. Ressources énergétiques Recherche Informatique. TECHNOLOGY & ENGINEERING Mechanical. bisacsh Power resources Research fast Power resources Research Data processing fast |
| topic_facet | Power resources Research. Power resources Research Data processing. Ressources énergétiques Recherche. Ressources énergétiques Recherche Informatique. TECHNOLOGY & ENGINEERING Mechanical. Power resources Research Power resources Research Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1901474 |
| work_keys_str_mv | AT acostamorenaj advancesinenergyresearchvolume30 |