Efnisyfirlit

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• List of Figures
• List of Tables
• Foreword
• Preface
• Acknowledgement
• Authors
• 1 Introduction to Geothermal Energy Resources
• 1.1 Basic Concepts and Classification of Geothermal Resources
• 1.2 Thermal Properties of Rocks
• 1.3 Geothermal Technology of the World
• 1.3.1 Geothermal Technology
• 1.3.1.1 Geothermal Electricity
• 1.3.1.2 Geothermal Heating and Cooling
• 1.3.2 Global Direct Use of Geothermal Energy
• 1.3.3 Stages of Construction and Cost of Setting up a Geothermal Power Plant
• 1.4 Geothermal Energy Potential of India
• 1.4.1 History
• 1.4.2 The Major Geothermal Manifestations of India
• 1.4.2.1 Puga Geothermal Field, Leh District, Ladakh, J&K
• 1.4.2.2 Chumathang Geothermal Field, Leh District, Ladakh, J&K
• 1.4.2.3 Tatapani Geothermal Field, Balrampur District, Chhattisgarh
• 1.4.3 Potential Geothermal Resources and Their Prioritization
• 1.5 Steps to Promote Geothermal Energy in India
• 1.5.1 Monetary Policies
• 1.5.2 Decreasing Investment Funding Costs/Risk Prevention
• 1.5.2.1 Challenges
• 1.6 Zoning of Geothermal Provinces in India
• 1.7 Some Financial Models of Countries Exploiting Geothermal Energy
• 1.7.1 United States: Financing of Miravalles III Project
• 1.7.2 Philippines (Asian Development Bank, 2018): Setting up of Plant by PNOC
• 1.7.3 Kenya: Setting up Olkaria III Project
• References
• 2 Enhanced Geothermal Systems
• 2.1 Introduction to EGS
• 2.1.1 Overview
• 2.1.2 Thermal Gradient
• 2.2 Issues and Challenges for Subsurface Designing of EGS Systems
• 2.3 Geothermal Drilling Technology for EGS
• 2.3.1 Advanced Drilling Technologies
• 2.3.2 Evolutionary Oil and Gas Well-drilling Technologies
• 2.3.2.1 Expandable Tubulars
• 2.3.2.2 Under-reamers
• 2.3.2.3 Low-clearance Casing Design
• 2.3.2.4 Drilling-with-Casing
• 2.3.2.5 Well Design Variations
• 2.3.2.6 Multilateral Completions/Stimulating through Sidetracks and Laterals
• 2.3.3 Revolutionary Technologies
• 2.4 Power Production and Hydraulic Stimulation for EGS
• 2.4.1 Using Dense Fluid for Hydraulic Stimulation
• 2.4.2 Using Carbon Dioxide (CO2) as a Working Fluid
• 2.4.3 Software Packages Used for EGS Investigation
• 2.5 Influence of Micro-seismic Events in EGS
• 2.5.1 Micro-seismic Events and Stimulation of Enhanced Geothermal System
• References
• 3 Direct Applications:: Application of Geothermal Water for Sericulture, Aquaculture, and Honey Processing
• 3.1 Direct Utilization of Geothermal Energy around the World
• 3.2 Application of Geothermal Water for Sericulture
• 3.2.1 Steps of Processing through Geothermal Water
• 3.3 Application of Geothermal Water for Aquaculture
• 3.3.1 Stage 1: Estimation of Heat Transfer between the Water Channel and the Walls
• 3.3.2 Stage 2: Heat Transfer Estimation of Water in Raceway
• 3.3.3 Stage 3: The Heat Flow from the Water Surface to the Atmosphere Is Calculated Using Radiation
• 3.3.4 Stage 4: Heat Resistance and Total Heat Dissipation Estimation Each Month with Only Water Condition
• 3.3.5 Step 5: Calculation of the Time Taken to Cool or Heat the Device without the Use of Biomass
• 3.3.6 Step 6: Estimation of Heat Flow from the Biomass by Conduction and Displacement
• 3.4 Application of Geothermal Water for Honey Processing
• 3.4.1 Energy and Materials Balance
• 3.4.2 Required Heating Medium Quantity
• 3.4.3 Heat Flow Coefficient Calculation
• 3.4.4 Heat Flow Surface Area Calculation
• 3.4.5 Calculating the Necessary Heating Time
• 3.4.6 Processing
• 3.4.6.1 Uncapping
• 3.4.6.2 Extractor
• 3.4.6.3 Processing
• 3.5 Case Studies
• 3.5.1 Puga
• 3.5.1.1 Methods
• 3.5.1.2 Societal Benefits
• 3.5.2 Manikaran
• 3.5.2.1 Methods Adopted
• 3.5.2.2 Societal Benefits
• References
• 4 Indirect Applications:: Application of Geothermal Water for Milk Pasteurization, Cloth and Food Drying, Desalination, and Space Heating and Cooling
• 4.1 Indirect Applications of Geothermal Water around the World
• 4.2 Application of Geothermal Water for Milk Pasteurization
• 4.3 Application of Geothermal Water for Cloth and Food Drying
• 4.3.1 Food Drying
• 4.3.2 Cloth Dryer
• 4.4 Application of Geothermal Water for Desalination
• 4.4.1 Multi-stage Flash Distillation
• 4.4.2 Multi-effect Distillation
• 4.4.3 Vapour-compression Evaporation
• 4.4.4 Cogeneration System for Power and Water Desalination
• 4.5 Application of Geothermal Water for Space Heating and Cooling
• 4.5.1 Ground Source Heat Pump
• 4.5.2 Geothermal Space Cooling
• 4.5.3 Cooling Source
• 4.5.4 Absorption Chillers
• 4.5.5 Distribution System
• 4.5.6 Energy Transfer Station (ETS)
• 4.5.7 Geothermal Heating
• 4.5.8 Heat Exchangers
• 4.5.9 Control Unit
• 4.5.10 Energy Meters
• 4.6 Case Studies
• 4.6.1 Bakreswar
• 4.6.1.1 Methods
• 4.6.1.2 Societal Benefits
• 4.6.2 Tatapani
• 4.6.2.1 Methods
• 4.6.2.2 Social Benefits
• 4.6.3 Iceland Deep Drilling Project
• 4.6.3.1 Method
• References
• 5 Machine Learning Techniques for Geothermal Energy Sector
• 5.1 Introduction to Machine Learning Techniques
• 5.2 Application of Machine Learning Technique in Estimation of Geothermal Temperature Distribution
• 5.3 Subsurface Characterization Using Machine Learning Technique
• 5.4 Application of Machine Learning Technique in Geothermal Drilling
• 5.5 Application of Machine Learning Technique in Reservoir Modelling
• 5.6 Application of Machine Learning Technique in Geothermal Power Generation
• 5.6.1 Prediction of Geographical Suitability of Geothermal Power Plants
• 5.7 Challenges and Issues in Adapting Machine Learning for the Geothermal Energy Sector
• 5.8 Case Studies
• 5.8.1 Case Study 1: Drilling
• 5.8.2 Case Study 2: Fault Detection
• References
• 6 Financing in Geothermal Energy Exploration and Exploitation
• 6.1 Introduction
• 6.1.1 Early-stage Financing
• 6.1.2 Late Development Financing
• 6.1.3 Construction and Operating Financing
• 6.2 Sources of Funding for Geothermal Projects in India
• 6.2.1 Business and Accounting Model
• 6.3 Economic Drivers of a Geothermal Project
• 6.3.1 Equity Funding
• 6.3.2 Debt Funding
• 6.4 Basic Structure of a Financial Model
• 6.5 Financial Funding in Various Countries
• 6.5.1 Indonesia
• 6.5.2 The Philippines
• 6.5.3 Iceland
• References
• 7 Impact of Geothermal Energy on Climate Change and Tackling It with Machine Learning
• 7.1 Introduction to Climate Change
• 7.1.1 Goal 13: Climate Change
• 7.2 Mitigation and Geothermal Energy
• 7.3 Factors Influencing Adaptation and Mitigation
• 7.4 Application of Machine Learning in Climate Change Mitigation
• 7.4.1 Gradient Descent Method
• 7.4.2 Gaussian Processes
• 7.4.3 Non-linear, Non-Gaussian Inferences
• 7.4.4 Deep Learning Approaches
• References
• Index