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Industrial optimization lies on the crossroads between mathematics,
computer science, engineering and management. This book presents
these fields in interdependence as a conversation between
theoretical aspects of mathematics and computer science and the
mathematical field of optimization theory at a practical level. The
19 case studies that were conducted by the author in real
enterprises in cooperation and co-authorship with some of the
leading industrial enterprises, including RWE, Vattenfall, EDF,
PetroChina, Vestolit, Sasol, and Hella, illustrate the results that
may be reasonably expected from an optimization project in a
commercial enterprise. The book is aimed at persons working in
industrial facilities as managers or engineers; it is also suitable
for university students and their professors as an illustration of
how the academic material may be used in real life. It will not
make its reader a mathematician but it will help its reader in
improving his plant.
Machine Learning and Data Science in the Oil and Gas Industry
explains how machine learning can be specifically tailored to oil
and gas use cases. Petroleum engineers will learn when to use
machine learning, how it is already used in oil and gas operations,
and how to manage the data stream moving forward. Practical in its
approach, the book explains all aspects of a data science or
machine learning project, including the managerial parts of it that
are so often the cause for failure. Several real-life case studies
round out the book with topics such as predictive maintenance, soft
sensing, and forecasting. Viewed as a guide book, this manual will
lead a practitioner through the journey of a data science project
in the oil and gas industry circumventing the pitfalls and
articulating the business value.
Machine Learning and Data Science in the Power Generation Industry
explores current best practices and quantifies the value-add in
developing data-oriented computational programs in the power
industry, with a particular focus on thoughtfully chosen real-world
case studies. It provides a set of realistic pathways for
organizations seeking to develop machine learning methods, with a
discussion on data selection and curation as well as organizational
implementation in terms of staffing and continuing
operationalization. It articulates a body of case study-driven best
practices, including renewable energy sources, the smart grid, and
the finances around spot markets, and forecasting.
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