Machine Learning for Process Industry Series: Books 1 and 2
Machine Learning for Process Industry Series: Books 1 and 2
About the Bundle
In the 21st century, data science has become an integral part of the work culture at every manufacturing industry and process industry is no exception to this modern phenomenon. From predictive maintenance to process monitoring, fault diagnosis to advanced process control, machine learning-based solutions are being used to achieve higher process reliability and efficiency. However, few books are available that adequately cater to the needs of budding process data scientists. The scant available resources include: 1) generic data science books that fail to account for the specific characteristics and needs of process plants 2) process domain-specific books with rigorous and verbose treatment of underlying mathematical details that become too theoretical for industrial practitioners. Understandably, this leaves a lot to be desired. Books are sought that have process systems in the backdrop, stress application aspects, and provide a guided tour of ML techniques that have proven useful in process industry. This series ‘Machine Learning for Process Industry’ addresses this gap to reduce the barrier-to-entry for those new to process data science.
The first book of the series ‘Machine Learning in Python for Process Systems Engineering’ covers the basic foundations of machine learning and provides an overview of broad spectrum of ML methods primarily suited for static systems. Step-by-step guidance on building ML solutions for process monitoring, soft sensing, predictive maintenance, etc. are provided using real process datasets. Aspects relevant to process systems such as modeling correlated variables via PCA/PLS, handling outliers in noisy multidimensional dataset, controlling processes using reinforcement learning, etc. are covered. The second book of the series ‘Machine Learning in Python for Dynamic Process Systems’ is focused on dynamic systems and provides a guided tour along the wide range of available dynamic modeling choices. Emphasis is paid to both the classical methods (ARX, CVA, ARMAX, OE, NARX, etc.) and modern neural network methods. Applications on time series analysis, noise modeling, system identification, and fault detection & diagnosis are illustrated with examples. Future books of the series will continue to focus on other aspects and needs of process industry. It is hoped that these books can help process data scientists find innovative ML solutions to the real-world problems faced by the process industry.
Books of the series will be useful to practicing process engineers looking to ‘pick up’ machine learning as well as data scientists looking to understand the needs and characteristics of process systems. With the focus on practical guidelines and real industrial case studies, we hope that these books lead to wider spread of process data science in the industry.
About the Books
Machine Learning in Python for Process Systems Engineering
Achieve Operational Excellence Using Process Data
This book provides an application-focused exposition of modern ML tools that have proven useful in process industry and hands-on illustrations on how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, inferential modeling, dimensionality reduction, and process control. This book considers unique characteristics of industrial process data and uses real data from industrial systems for illustrations. With the focus on practical implementation and minimal programming or ML prerequisites, the book covers the gap in available ML resources for industrial practitioners. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning. The readers will find all the resources they need to deal with high-dimensional, correlated, noisy, corrupted, multimode, and nonlinear process data.
The book has been divided into four parts. Part 1 provides a perspective on the importance of ML in process systems engineering and lays down the basic foundations of ML. Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the various characteristics of industrial process systems. Part 3 is focused on artificial neural networks and deep learning. Part 4 covers the important topic of deploying ML solutions over web and shows how to build a production-ready process monitoring web application.
Broadly, the book covers the following:
- Varied applications of ML in process industry
- Fundamentals of machine learning workflow
- Practical methodologies for pre-processing industrial data
- Classical ML methods and their application for process monitoring, fault diagnosis, and soft sensing
- Deep learning and its application for predictive maintenance
- Reinforcement learning and its application for process control
- Deployment of ML solution over web
If you do not have a PayPal account, you can purchase the book at Google Play.
Machine Learning in Python for Dynamic Process Systems
A practitioner’s guide for building process modeling, predictive, and monitoring solutions using dynamic data
This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning for dynamic process modeling. Applications on time series analysis, process disturbance modeling, system identification, and process fault detection are illustrated with examples. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems.
This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques (such as ARX, FIR, OE, ARMAX, ARIMAX, CVA, NARX, etc.) and has been written keeping in mind the different modeling requirements and process characteristics that determine a model’s suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning.
The following topics are broadly covered:
· Exploratory analysis of dynamic dataset
· Best practices for dynamic modeling
· Linear and discrete-time classical parametric and non-parametric models
· State-space models for MIMO systems
· Nonlinear system identification and closed-loop identification
· Neural networks-based dynamic process modeling
If you do not have a PayPal account, you can purchase the book at Google Play.
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