TY  - BOOK
AU  - Galatro,Daniela
AU  - Dawe,Stephen
ED  - SpringerLink (Online service)
TI  - Data Analytics for Process Engineers: Prediction, Control and Optimization 
T2  - Synthesis Lectures on Mechanical Engineering,
SN  - 9783031468667
AV  - TP155-156 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Production engineering
KW  - Engineering
KW  - Data processing
KW  - Quantitative research
KW  - Process Engineering
KW  - Data Engineering
KW  - Data Analysis and Big Data
N1  - Sources of Data -- Exploratory Data Analysis -- Data-based modelling for prediction -- Data-based modelling for control -- Optimization -- Final remarks
N2  - 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
UR  - https://doi.org/10.1007/978-3-031-46866-7
ER  -