Prediction, Control and Optimization Galatro, Daniela. creator author. aut http://id.loc.gov/vocabulary/relators/aut Dawe, Stephen. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access IX, 145 p. 159 illus., 115 illus. in color. online resource. 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. Sources of Data -- Exploratory Data Analysis -- Data-based modelling for prediction -- Data-based modelling for control -- Optimization -- Final remarks. by Daniela Galatro, Stephen Dawe. Production engineering Engineering Data processing Quantitative research Process Engineering Data Engineering Data Analysis and Big Data TP155-156 9783031468667 https://doi.org/10.1007/978-3-031-46866-7 https://doi.org/10.1007/978-3-031-46866-7 231202 20251030110847.0 978-3-031-46866-7