Free delivery for orders over 69.99 euro

Check order status

Become part of a community of book lovers from all over the world and get access to a whole bunch of benefits. Create an account for free

Free delivery for purchases over 69.99 €

DPD courier 3.99 € DPD point 3.49 € GLS courier 4.99 € GLS point 3.99 € Croatian Post 4.99 € Overseas courier 4.99 € Box Now 4.49 €

Contact

How to shop

Help

My account

▸ Empty :-(

Free delivery for orders over 69.99 euro

Data Analytics for Process Engineers

Name: Data Analytics for Process Engineers
Brand: Springer, Berlin
SKU: 44230444
Price: 40.22 EUR
Availability: InStock
Author: Daniela Galatro
ISBN: 9783031468650

Prediction, Control and Optimization

Daniela Galatro

Stephen Dawe

Language

English

Book Hardback

Libristo code: 44230444

Publishers Springer, Berlin, December 2023

This book provides an industry-oriented data analytics approach for process engineers, including dat... Full description

Libristo code: 44230444

98 b

40.22 €

In stock at our supplier Shipping in 10-13 days

Up to 30 days for returns

Customers also purchased

Tohle jsem měla napsat v přítomném čase Helle Helle

Hardback

8.34 €

Low Tide in Twilight 01 Annabell Führes

Paperback

16.18 €

Deadpool a Wolverine WWIII - Třetí světová Joe Kelly

Paperback

11.96 €

Děcké lékařství Cyril Děckař

Paperback

18.50 €

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, it can 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.