Ebook: Machine Learning in Python for Process Systems Engineering: Achieving operational excellence using process data

Perhaps you are reading this book because you too have been inspired by the capabilities
of machine learning and would like to use it to solve problems being faced by your
organization. However, you might be struggling to find a definite guide that can help you
decide which specific methodology to chose among the myriad of available
methodologies. You may have come across a nice research article that showcases an
interesting process systems application of a ML method. However, you might be facing
difficulties trying to understand the intricate details of the algorithm. We won’t be surprised
if you have struggled to find a data-science book that caters to the needs of a process
systems engineer, considers unique characteristics of industrial process systems, and
uses industrial-scale process systems for illustrations. We, the authors, have been in that
phase. A process engineer will arguably find it more relevant and useful to learn principal
component analysis (PCA) by working through a process monitoring application (the most
popular application area of PCA in process industry) and learning how to compute the
monitoring metrics. Similar arguments could be made for several other popular ML
methods. There is a gap in available machine learning resources for industrial
practitioners and this book attempts to cover this gap.

In one sense, we wrote this book for our younger selves; a book that we wish had existed
when we started experimenting with machine learning techniques. Drawing from our
years of experience in developing data-driven industrial solutions, this book has been
written with the focus on de-cluttering the world of machine learning, giving a
comprehensive exposition of ML tools that have proven useful in process industry,
providing step-by-step elucidation of implementation details, cautioning against the`
pitfalls and listing various tips & tricks that we have encountered over the years, and using
dataset from industrial-scale process systems for illustrations. We strongly believe in
‘learning by doing’ and therefore we encourage the readers to work through in-chapter
illustrations as they follow along the text. For reader’s assistance, Jupyter notebooks with
complete code implementations are available for download. We have chosen Python as
the coding language for the book as it convenient to use, has large collection of ML
libraries, and is the de facto standard language for ML. No prior experience with Python
is assumed. The book has been designed to teach machine learning from scratch and
upon completion, the reader will feel comfortable at using ML techniques.