UID:
edoccha_9961449885802883
Format:
1 online resource (151 pages)
Edition:
1st ed.
ISBN:
3-031-46866-X
Series Statement:
Synthesis Lectures on Mechanical Engineering Series
Note:
Intro -- Preface -- Contents -- 1 Sources of Data -- 1.1 Plant Process Data -- 1.2 Pilot Plant and Laboratory Data -- 1.3 Process Simulation Data -- 1.4 Synthetic Data -- 1.5 Summary and Final Remarks -- Data Disclosure -- Problems -- Resources -- Recommended Readings -- References -- 2 Exploratory Data Analysis -- 2.1 Types of Data and Types of Exploratory Data Analysis -- 2.2 Summary Statistics -- 2.3 Simple Visualization -- 2.3.1 Time-Series Plot -- 2.3.2 Scatter Plot -- 2.3.3 Multivariate Scatter Plot -- 2.3.4 Box Plot -- 2.3.5 Histogram -- 2.3.6 Temperature -- 2.3.7 Wind Speed -- 2.4 Outliers and Missing Values -- 2.4.1 Outliers -- 2.4.2 Missing Values -- 2.5 Correlogram -- 2.6 Clustering and Dimensionality Reduction -- 2.6.1 K-means Clustering -- 2.6.2 Principal Component Analysis -- 2.6.3 Variance-Based Sensitivity Analysis -- 2.7 Summary and Final Remarks -- Data Disclosure -- Problems -- Resources -- Recommended Readings -- References -- 3 Data-Based Modelling for Prediction -- 3.1 Regression and Models -- 3.2 Simple Regression Models -- 3.2.1 Simple Linear Regression -- 3.2.2 Multiple Linear Regression and Multivariate Linear Regression -- 3.2.3 Exponential and Logarithmic Regression -- 3.2.4 Polynomial and Response Surface Regressions -- 3.2.5 Splines -- 3.2.6 Multivariate Adaptive Regression Splines -- 3.2.7 Kriging -- 3.3 Non-linear Regression Models -- 3.4 Non-linear Machine Learning Algorithms -- 3.4.1 Neural Networks -- 3.4.2 Random Forest -- 3.4.3 Support Vector Machine -- 3.5 Distribution Models -- 3.5.1 Normal Distribution -- 3.5.2 Weibull Distribution -- 3.5.3 Gamma Distribution -- 3.6 Model Performance and Validation -- 3.7 Correlation and Causality -- 3.8 Summary and Final Remarks -- Data Disclosure -- Problems -- Resources -- Recommended Readings -- References -- 4 Data-Based Modelling for Control.
,
4.1 Modern Control Theory and Data-Based Control -- 4.2 Basic Control Theory: PID Controllers -- 4.3 Model Predictive Control -- 4.4 Summary and Final Remarks -- Data Disclosure -- Problems -- Resources -- Recommended Readings -- References -- 5 Optimization -- 5.1 Basic Optimization Concepts -- 5.2 Grid Search, Random Search, and Gradient Search -- 5.3 Evolutionary Algorithms -- 5.4 Particle Swarm Optimization -- 5.5 Multi-objective Optimization -- 5.6 Bayesian Inference and Optimization -- 5.7 Summary and Final Remarks -- Data Disclosure -- Problems -- Resources -- Recommended Readings -- References -- 6 Final Remarks -- 6.1 R and RStudio: Introduction, Documentation, and Codes -- 6.2 Data Analysis, Data Analytics, and Machine Learning -- 6.3 Open Datasets -- 6.4 Data Analytics and the Physical Meaning of Phenomena -- 6.5 Remarks on Sources of Data -- 6.6 Remarks on Exploratory Data Analysis -- 6.7 Remarks on Data-Based Modelling -- 6.8 Remarks on Data-Based Control -- 6.9 Remarks on Optimization -- 6.10 The Future of Data Analytics in Process Engineering -- References.
Additional Edition:
Print version: Galatro, Daniela Data Analytics for Process Engineers Cham : Springer,c2024 ISBN 9783031468650
Language:
English
