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001 978-3-031-46866-7
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020 _a9783031468667
_9978-3-031-46866-7
024 7 _a10.1007/978-3-031-46866-7
_2doi
050 4 _aTP155-156
072 7 _aTDC
_2bicssc
072 7 _aTEC009010
_2bisacsh
072 7 _aTDCX
_2thema
100 1 _aGalatro, Daniela.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967904
245 1 0 _aData Analytics for Process Engineers
_h[electronic resource] :
_bPrediction, Control and Optimization /
_cby Daniela Galatro, Stephen Dawe.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aIX, 145 p. 159 illus., 115 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Mechanical Engineering,
_x2573-3176
505 0 _aSources of Data -- Exploratory Data Analysis -- Data-based modelling for prediction -- Data-based modelling for control -- Optimization -- Final remarks.
520 _aThis 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.
650 0 _aProduction engineering.
650 0 _aEngineering
_xData processing.
650 0 _aQuantitative research.
650 1 4 _aProcess Engineering.
650 2 4 _aData Engineering.
_966059
650 2 4 _aData Analysis and Big Data.
700 1 _aDawe, Stephen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967905
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031468650
776 0 8 _iPrinted edition:
_z9783031468674
776 0 8 _iPrinted edition:
_z9783031468681
830 0 _aSynthesis Lectures on Mechanical Engineering,
_x2573-3176
_965545
856 4 0 _uhttps://doi.org/10.1007/978-3-031-46866-7
912 _aZDB-2-SXSC
999 _c56729
_d56729