03340nam a22005295i 4500 978-3-031-46866-7 DE-He213 20251030110847.0 cr nn 008mamaa 231202s2024 sz | s |||| 0|eng d 9783031468667 978-3-031-46866-7 10.1007/978-3-031-46866-7 doi TP155-156 TDC bicssc TEC009010 bisacsh TDCX thema Galatro, Daniela. author. aut http://id.loc.gov/vocabulary/relators/aut 67904 Data Analytics for Process Engineers [electronic resource] : Prediction, Control and Optimization / by Daniela Galatro, Stephen Dawe. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. IX, 145 p. 159 illus., 115 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Synthesis Lectures on Mechanical Engineering, 2573-3176 Sources of Data -- Exploratory Data Analysis -- Data-based modelling for prediction -- Data-based modelling for control -- Optimization -- Final remarks. 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. Production engineering. Engineering Data processing. Quantitative research. Process Engineering. Data Engineering. 66059 Data Analysis and Big Data. Dawe, Stephen. author. aut http://id.loc.gov/vocabulary/relators/aut 67905 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031468650 Printed edition: 9783031468674 Printed edition: 9783031468681 Synthesis Lectures on Mechanical Engineering, 2573-3176 65545 https://doi.org/10.1007/978-3-031-46866-7 ZDB-2-SXSC 56729 56729