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  • 1
    Online-Ressource
    Online-Ressource
    Data Analytics for Process Engineers : Prediction, Control and Optimization (2024)
    Cham :Springer Nature Switzerland :
    Details
    UID:
    almahu_9949744355002882
    Umfang: IX, 145 p. 159 illus., 115 illus. in color. , online resource.
    Ausgabe: 1st ed. 2024.
    ISBN: 9783031468667
    Serie: Synthesis Lectures on Mechanical Engineering,
    Inhalt: This book provides an industry-oriented data analytics approach for process engineers, including data acquisition methods and sources, exploratory data analysis and sensitivity analysis, data-based modelling for prediction, data-based modelling for monitoring and control, and data-based optimization of processes. While many of the current data analytics books target business-related problems, the rationale for this book is a specific need to understand and select applicable data analytics approaches pragmatically to analyze process engineering-related problems; this tailored solution for engineers gets amalgamated with governing equations, and in several cases, with the physical understanding of the phenomenon being analyzed. We also consider this book strategically conceived to help map Education 4.0 with Industry 4.0 since it can support undergraduate and graduate students to gain valuable and applicable data analytics stills that can be further used in their workplace. Moreover, itcan be used as a reference book for professionals, a quick reference to data analytics tools that can facilitate and/or optimize their process engineering tasks.
    Anmerkung: Sources of Data -- Exploratory Data Analysis -- Data-based modelling for prediction -- Data-based modelling for control -- Optimization -- Final remarks.
    In: Springer Nature eBook
    Weitere Ausg.: Printed edition: ISBN 9783031468650
    Weitere Ausg.: Printed edition: ISBN 9783031468674
    Weitere Ausg.: Printed edition: ISBN 9783031468681
    Sprache: Englisch
    DOI: 10.1007/978-3-031-46866-7
    URL: https://doi.org/10.1007/978-3-031-46866-7
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
    Open Access Wunsch
    HU Berlin
  • 2
    Online-Ressource
    Online-Ressource
    Data Analytics for Process Engineers : Prediction, Control and Optimization. (2024)
    Cham :Springer,
    Details
    UID:
    edoccha_9961449885802883
    Umfang: 1 online resource (151 pages)
    Ausgabe: 1st ed.
    ISBN: 3-031-46866-X
    Serie: Synthesis Lectures on Mechanical Engineering Series
    Anmerkung: 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.
    Weitere Ausg.: Print version: Galatro, Daniela Data Analytics for Process Engineers Cham : Springer,c2024 ISBN 9783031468650
    Sprache: Englisch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
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