Search Results - India~

Principles of water treatment by Howe, Kerry J.

Table of Contents: “…Machine generated contents note: PrefaceAcknowledgmentsChapter 1 Introduction1-1 The Importance of Principles1-2 The Importance of SustainabilityReferencesChapter 2 Water Quality and Public Health2-1 Relationship between Water Quality and Public Health2-2 Source Waters for Municipal Drinking Water Systems2-3 Regulations of Water Treatment in the United States2-4 Evolving Trends and Challenges in Drinking Water Treatment2-5 Summary and Study GuideReferencesChapter 3 Process Selection3-1 Process Selection Based on Contaminant Properties3-2 Other Considerations in Process Selection3-4 Design and Selection of Process Trains3-5 Summary and Study GuideHomework ProblemsReferencesChapter 4 Fundamental Principles of Environmental Engineering4-1 Units of Expression for Chemical Concentrations4-2 Chemical Equilibrium4-3 Chemical Kinetics4-4 Reactions Used in Water Treatment4-5 Mass Balance Analysis4-6 Introduction to Reactors and Reactor Analysis4-7 Reactions in Batch Reactors4-8 Hydraulic Characteristics of Ideal Flow Reactors4-9 Reactions in Ideal Flow Reactors4-10 Measuring the Hydraulic Characteristics of Flow Reactors with Tracer Tests4-11 Describing the Hydraulic Performance of Real Flow Reactors4-12 Reactions in Real Flow Reactors4-13 Introduction to Mass Transfer4-14 Molecular Diffusion4-15 Diffusion Coefficients4-16 Models and Correlations for Mass Transfer at an Interface4-17 Evaluating the Concentration Gradient with Operating Diagrams4-18 Summary and Study GuideHomework ProblemsReferencesChapter 5 Coagulation and Flocculation5-1 Role of Coagulation and Flocculation in Water Treatment5-2 Stability of Particles in Water5-3 Theory of Coagulation5-4 Coagulation Practice5-5 Principles of Mixing for Coagulation and Flocculation5-6 Rapid Mix Practice5-7 Theory of Flocculation5-8 Flocculation Practice5-9 Energy and Sustainability Considerations5-10 Summary and Study GuideHomework ProblemsReferencesChapter 6 Sedimentation6-1 Principles of Discrete (Type I) Particle Settling6-2 Discrete Settling in Ideal Sedimentation Basins6-3 Principles of Flocculant (Type II) Particle Settling6-4 Principles of Hindered (Type III) Settling6-5 Conventional Sedimentation Basin Design6-6 Alternative Sedimentation Processes6-7 Physical Factors Affecting Sedimentation6-8 Energy and Sustainability Considerations6-9 Summary and Study GuideHomework ProblemsReferencesChapter 7 Rapid Granular Filtration7-1 Physical Description of a Rapid Granular Filter7-2 Process Description of Rapid Filtration7-3 Particle Capture in Granular Filtration7-4 Head Loss through a Clean Filter Bed7-5 Modeling of Performance and Optimization7-6 Backwash Hydraulics7-7 Energy and Sustainability Considerations7-8 Summary and Study GuideHomework ProblemsReferencesChapter 8 Membrane Filtration8-1 Classification of Membrane Processes8-2 Comparison to Rapid Granular Filtration8-3 Principal Features of Membrane Filtration Equipment8-4 Process Description of Membrane Filtration8-5 Particle Capture in Membrane Filtration8-6 Hydraulics of Flow through Membrane Filters8-7 Membrane Fouling8-8 Sizing of Membrane Skids8-9 Energy and Sustainability Considerations8-10 Summary and Study GuideHomework ProblemsReferencesChapter 9 Reverse Osmosis9-1 Principal Features of a Reverse Osmosis Facility9-2 Osmotic Pressure and Reverse Osmosis9-3 Mass Transfer of Water and Solutes through RO Membranes9-4 Performance Dependence on Temperature and Pressure9-5 Concentration Polarization9-6 Fouling and Scaling9-7 Element Selection and Membrane Array Design9-8 Energy and Sustainability Considerations9-9 Summary and Study GuideHomework ProblemsReferencesChapter 10 Adsorption and Ion Exchange10-1 Introduction to the Adsorption Process10-2 Adsorption Equilibrium10-3 Adsorption Kinetics10-4 Introduction to the Ion Exchange Process10-5 Ion Exchange Equilibrium10-6 Ion Exchange Kinetics10-7 Fixed Bed Contactors10-8 Suspended Media Reactors10-9 Energy and Sustainability Considerations10-10 Summary and Learning Objectives10-11 Homework ProblemsReferencesChapter 11 Air stripping and aeration11-1 Types of Air Stripping and Aeration Contactors11-2 Gas-Liquid Equilibrium11-3 Fundamentals of Packed Tower Air Stripping11-4 Design and Analysis of Packed Tower Air Stripping11-5 Energy and Sustainability Considerations11-6 Summary and Study GuideHomework ProblemsReferencesChapter 12 Advanced Oxidation12-1 Introduction to Advanced Oxidation12-2 Ozonation as an Advanced Oxidation Process12-3 Hydrogen Peroxide/Ozone Process12-4 Hydrogen Peroxide/UV Light Process12-5 Energy and Sustainability Considerations12-6 Summary and Study GuideHomework ProblemsReferencesChapter 13 Disinfection13-1 Disinfection Agents and Systems13-2 Disinfection with Free and Combined Chlorine13-3 Disinfection with Chlorine Dioxide13-4 Disinfection with Ozone13-5 Disinfection with Ultraviolet Light13-6 Disinfection Kinetics13-7 Disinfection Kinetics in Real Flow Reactors13-8 Design of Disinfection Contactors With Low Dispersion13-9 Disinfection Byproducts13-10 Residual Maintenance13-11 Energy and Sustainability Considerations13-12 Summary and Study GuideHomework ProblemsReferences14 Residuals Management14-1 Defining the Problem14-2 Physical, Chemical, and Biological Properties of Residuals14-3 Alum and Iron Coagulation Sludge14-4 Liquid Wastes From Granular Media Filters14-5 Management of Residual Liquid Streams14-6 Management of Residual Sludge14-7 Ultimate Reuse and Disposal of Semisolid Residuals14-8 Summary and Study GuideHomework ProblemsReferencesAppendix A Conversion FactorsAppendix B Physical Properties of Selected Gases and Composition of AirB-1 Density of Air at Other TemperaturesB-2 Change in Atmospheric Pressure with ElevationAppendix C Physical Properties of WaterAppendix D Periodic TableAppendix E Electronic Resources available on the John Wiley and Sons website for this TextbookIndex.…”
An electronic book accessible through the World Wide Web; click to view

Evaluation of enzyme inhibitors in drug discovery a guide for medicinal chemists and pharmacologists / by Copeland, Robert Allen

Table of Contents: “…Serial Dilution Schemes.Index.…”
An electronic book accessible through the World Wide Web; click to view

Mixed models theory and applications with R / by Demidenko, Eugene, 1948-

Table of Contents: “…331.16 Software and books361.17 Summary points 372 MLE for LME Model 412.1 Example: Weight versus height 422.2 The model and log-likelihood functions 452.3 Balanced random-coefficient model 602.4 LME model with random intercepts 642.5 Criterion for the MLE existence 722.6 Criterion for positive definiteness of matrix D742.7 Preestimation bounds for variance parameters 772.8 Maximization algorithms792.9 Derivatives of the log-likelihood function 812.10 Newton--Raphson algorithm 832.11 Fisher scoring algorithm852.12 EM algorithm 882.13 Starting point 932.14 Algorithms for restricted MLE 962.15 Optimization on nonnegative definite matrices 972.16 lmeFS and lme in R 1082.17 Appendix: Proof of the MLE existence 1122.18 Summary points 1153 Statistical Properties of the LME Model 1193.1 Introduction 1193.2 Identifiability of the LMEmodel 1193.3 Information matrix for variance parameters 1223.4 Profile-likelihood confidence intervals 1333.5 Statistical testing of the presence of random effects 1353.6 Statistical properties of MLE 1393.7 Estimation of random effects 1483.8 Hypothesis and membership testing 1533.9 Ignoring random effects 1573.10 MINQUE for variance parameters 1603.11 Method of moments 1693.12 Variance least squares estimator 1733.13 Projection on D+ space 1783.14 Comparison of the variance parameter estimation 1783.15 Asymptotically efficient estimation for [beta] 1823.16 Summary points 1834 Growth Curve Model and Generalizations 1874.1 Linear growth curve model 1874.2 General linear growth curve model 2034.3 Linear model with linear covariance structure 2214.4 Robust linear mixed effects model 2354.5 Appendix: Derivation of the MM estimator 2434.6 Summary points 2445 Meta-analysis Model 2475.1 Simple meta-analysis model 2485.2 Meta-analysis model with covariates 2755.3 Multivariate meta-analysis model 2805.4 Summary points 2916 Nonlinear Marginal Model 2936.1 Fixed matrix of random effects 2946.2 Varied matrix of random effects 3076.3 Three types of nonlinear marginal models 3186.4 Total generalized estimating equations approach 3236.5 Summary points 3307 Generalized Linear Mixed Models 3337.1 Regression models for binary data 3347.2 Binary model with subject-specific intercept 3577.3 Logistic regression with random intercept 3647.4 Probit model with random intercept 3847.5 Poisson model with random intercept 3887.6 Random intercept model: overview 4037.7 Mixed models with multiple random effects 4047.8 GLMM and simulation methods 4137.9 GEE for clustered marginal GLM 4187.10 Criteria for MLE existence for binary model 4267.11 Summary points 4318 Nonlinear Mixed Effects Model 4358.1 Introduction 4358.2 The model 4368.3 Example: Height of girls and boys 4398.4 Maximum likelihood estimation 4418.5 Two-stage estimator 4448.6 First-order approximation 4508.7 Lindstrom--Bates estimator 4528.8 Likelihood approximations 4578.9 One-parameter exponential model 4608.10 Asymptotic equivalence of the TS and LB estimators 4678.11 Bias-corrected two-stage estimator 4698.12 Distribution misspecification 4718.13 Partially nonlinear marginal mixed model 4748.14 Fixed sample likelihood approach4758.15 Estimation of random effects and hypothesis testing 4788.16 Example (continued) 4798.17 Practical recommendations 4818.18 Appendix: Proof of theorem on equivalence 4828.19 Summary points 4859 Diagnostics and Influence Analysis 4899.1 Introduction 4899.2 Influence analysis for linear regression 4909.3 The idea of infinitesimal influence 4939.4 Linear regression model 4959.5 Nonlinear regression model 5129.6 Logistic regression for binary outcome 5179.7 Influence of correlation structure 5269.8 Influence of measurement error 5279.9 Influence analysis for the LME model 5309.10 Appendix: MLE derivative with respect to σ2 5369.11 Summary points 53710 Tumor Regrowth Curves 54110.1 Survival curves 54310.2 Double--exponential regrowth curve 54510.3 Exponential growth with fixed regrowth time 55910.4 General regrowth curve 56510.5 Double--exponential transient regrowth curve 56610.6 Gompertz transient regrowth curve 57310.7 Summary points 57611 Statistical Analysis of Shape 57911.1 Introduction 57911.2 Statistical analysis of random triangles 58111.3 Face recognition 58411.4 Scale-irrelevant shape model 58511.5 Gorilla vertebrae analysis 58911.6 Procrustes estimation of the mean shape 59111.7 Fourier descriptor analysis 59811.8 Summary points 60712 Statistical Image Analysis 60912.1 Introduction 60912.2 Testing for uniform lighting 61212.3 Kolmogorov--Smirnov image comparison 61612.4 Multinomial statistical model for images 62012.5 Image entropy 62312.6 Ensemble of unstructured images 62712.7 Image alignment and registration 64012.8 Ensemble of structured images 65212.9 Modeling spatial correlation 65412.10 Summary points 66013 Appendix: Useful Facts and Formulas 66313.1 Basic facts of asymptotic theory 66313.2 Some formulas of matrix algebra 67013.3 Basic facts of optimization theory 674References 683Index 713.…”
An electronic book accessible through the World Wide Web; click to view

Rupert Gray : a tale in black and white / by Cobham, Stephen N.

An electronic book accessible through the World Wide Web; click to view

Adolphus, a tale /

An electronic book accessible through the World Wide Web; click to view

Rupert Gray : a tale in black and white / by Cobham, Stephen N.

An electronic book accessible through the World Wide Web; click to view

Adolphus, a tale /

An electronic book accessible through the World Wide Web; click to view

Understanding and managing model risk a practical guide for quants, traders and validators / by Morini, Massimo

An electronic book accessible through the World Wide Web; click to view

Understanding and managing model risk a practical guide for quants, traders and validators / by Morini, Massimo

An electronic book accessible through the World Wide Web; click to view

The Best of Texas Folk and Folklore : 1916-1954

Full text available:

The Best of Texas Folk and Folklore : 1916-1954

Full text available:

Echoes of the Haitian Revolution 1804-2004 /

An electronic book accessible through the World Wide Web; click to view

Echoes of the Haitian Revolution 1804-2004 /

An electronic book accessible through the World Wide Web; click to view

Statistical monitoring of complex multivariate processes with applications in industrial process control / by Krüger, Uwe, Dr

Table of Contents: “…Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index.…”
An electronic book accessible through the World Wide Web; click to view

Electron flow in organic chemistry a decision-based guide to organic mechanisms / by Scudder, Paul H.

Table of Contents: “…THEORY & PERICYCLIC REACTIONS 343 12.1 Review Of Orbitals As Standing Waves 344 12.2 Molecular Orbital Theory For Linear Pi Systems 344 12.3 Molecular Orbital Theory For Cyclic Conjugated PI Systems 348 12.4 Perturbation Of The HOMO And LUMO 351 12.5 Delocalization Of Sigma Electrons (Supplemental) 352 12.6 Concerted Pericyclic Cycloaddition Reactions 353 12.7 Concerted Pericyclic Electrocyclic Reactions 357 12.8 Concerted Pericyclic Sigmatropic Rearrangements 359 12.9 Pericyclic Reactions Summary 361 APPENDIX (A COLLECTION OF IMPORTANT TOOLS) 364 General Bibliography 364 Abbreviations Used in This Text 365 Functional Group Glossary 366 Composite pKa Chart 369 Bond Strength Table 372 Generic Classification Guide 373 Flow Charts for the Classification of Electron Sources and Sinks 375 Pathway Summary 375 Trends Guide 380 Major Routes Summary 384 Major Decisions Guide 388 Thermodynamics and Kinetics 390 Generation of Alternate Paths, Reaction Cubes 390 Organic Structure Elucidation Strategies 393 Notes on Nomenclature 399 HINTS TO PROBLEMS FROM CHAPTERS 8, 9, AND 10 404 INDEX 407.…”
An electronic book accessible through the World Wide Web; click to view

Statistical monitoring of complex multivariate processes with applications in industrial process control / by Krüger, Uwe, Dr

Table of Contents: “…Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index.…”
An electronic book accessible through the World Wide Web; click to view

Electron flow in organic chemistry a decision-based guide to organic mechanisms / by Scudder, Paul H.

Table of Contents: “…THEORY & PERICYCLIC REACTIONS 343 12.1 Review Of Orbitals As Standing Waves 344 12.2 Molecular Orbital Theory For Linear Pi Systems 344 12.3 Molecular Orbital Theory For Cyclic Conjugated PI Systems 348 12.4 Perturbation Of The HOMO And LUMO 351 12.5 Delocalization Of Sigma Electrons (Supplemental) 352 12.6 Concerted Pericyclic Cycloaddition Reactions 353 12.7 Concerted Pericyclic Electrocyclic Reactions 357 12.8 Concerted Pericyclic Sigmatropic Rearrangements 359 12.9 Pericyclic Reactions Summary 361 APPENDIX (A COLLECTION OF IMPORTANT TOOLS) 364 General Bibliography 364 Abbreviations Used in This Text 365 Functional Group Glossary 366 Composite pKa Chart 369 Bond Strength Table 372 Generic Classification Guide 373 Flow Charts for the Classification of Electron Sources and Sinks 375 Pathway Summary 375 Trends Guide 380 Major Routes Summary 384 Major Decisions Guide 388 Thermodynamics and Kinetics 390 Generation of Alternate Paths, Reaction Cubes 390 Organic Structure Elucidation Strategies 393 Notes on Nomenclature 399 HINTS TO PROBLEMS FROM CHAPTERS 8, 9, AND 10 404 INDEX 407.…”
An electronic book accessible through the World Wide Web; click to view

The path to more sustainable energy systems how do we get there from here? / by Ebenhack, Ben W.

Table of Contents: “…Next steps -- 8.1 Entering a new age -- 8.1.1 The transition that brought us here -- 8.2 Petroleum's role in the next transition -- 8.2.1 Petroleum's response to the shortage -- 8.2.2 The time factor -- 8.2.3 Higher prices -- 8.3 Energy poverty's role in the transition -- 8.3.1 The need for an energy labor force -- 8.4 A brief note on climate change's role in the transition -- 8.5 Energy dreams -- 8.5.1 Easy energy transitions -- 8.5.2 Solar -- 8.5.3 Unproven technologies -- 8.5.4 Ridiculous technologies -- 8.6 Comparing the options -- 8.7 New lifestyles around sustainable energy -- 8.8 Optimized energy mixes for space and time -- 8.8.1 Using everything, as we always have -- 8.8.2 Context-based solutions -- 8.8.3 Local, decentralized energy development -- 8.8.4 Conservation -- 8.8.5 Evolving energy mixes -- 8.9 Brief summary of agency and industry forecasts -- 8.10 So, what is the path forward? -- Index.…”
An electronic book accessible through the World Wide Web; click to view