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Titlebook: Data Analytics for Process Engineers; Prediction, Control Daniela Galatro,Stephen Dawe Textbook 2024 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 20:07:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Data Analytics for Process Engineers
副標(biāo)題Prediction, Control
編輯Daniela Galatro,Stephen Dawe
視頻videohttp://file.papertrans.cn/263/262700/262700.mp4
概述Includes data analytics and machine learning tools to analyze process engineering–related problems pragmatically.Provides a comprehensive review of tools for monitoring, modeling, controlling, and opt
叢書(shū)名稱(chēng)Synthesis Lectures on Mechanical Engineering
圖書(shū)封面Titlebook: Data Analytics for Process Engineers; Prediction, Control  Daniela Galatro,Stephen Dawe Textbook 2024 The Editor(s) (if applicable) and The
描述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.?.
出版日期Textbook 2024
關(guān)鍵詞data-based prediction; data analytics; process control; process engineering; process optimization
版次1
doihttps://doi.org/10.1007/978-3-031-46866-7
isbn_softcover978-3-031-46868-1
isbn_ebook978-3-031-46866-7Series ISSN 2573-3168 Series E-ISSN 2573-3176
issn_series 2573-3168
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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2573-3168 view of tools for monitoring, modeling, controlling, and optThis 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 model
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Sources of Data,i) process simulation data. Acquired data is referred to as .; however, synthetic data, artificially generated from measured or observed data, is a valuable source of data that we explore in this chapter, mostly when the measured data is insufficient or biased.
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Exploratory Data Analysis,sing observations. This chapter explores simple visualization EDA techniques, algorithms to detect and handle outliers and missing values, more advanced tools such as correlograms and clustering, and dimensionality reduction techniques.
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