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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Peace Ecology in the Anthropocene for Africasing 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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Citizenship and Political Parties in Ecuadorerformance and robustness of systems. In this Chapter, we illustrate the use of machine learning techniques to enhance tuning and optimization capabilities for both the basic control theory and model predictive control.

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Francesco Strazzari,Ervjola Selenicaassessing our exploratory data analysis, modelling, control, and optimization strategies considering the physical meaning of the phenomena captured in datasets. Finally, we sketch the future of data analytics in process engineering, providing some examples of current research in this fascinating field.

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Data-Based Modelling for Prediction,s, which provide a fundamental understanding of physicochemical phenomena, such as the ones studied in fluid mechanics, heat transfer, and mass transfer. In data-based modelling, quantitative data analysis methods are used to identify and study a set of variables and determine the relationship or co

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Data-Based Modelling for Control,ies on sensor measurements or data from control systems when executing actions. Machine learning can be and is currently used to solve complex control problems, as (i) it provides insights on training, and (ii) it learns from the data. Potentially, machine learning can contribute to optimizing the p

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Final Remarks,s and valuable resources regarding the tools used in this book for data analytics and references to open datasets. We clarified some concepts differentiating data analysis from data analytics and properly defining machine learning. As we reiteratively did in the book, we emphasize the importance of