Applications of Machine Learning to Optimizing Polyolefin Manufacturing
Niket Sharma Y. A. Liu
This chapter is a preprint from our book by , focusing on leveraging machine
learning (ML) in chemical and polyolefin manufacturing optimization. It's
crafted for both novices and seasoned professionals keen on the latest ML
applications in chemical processes. We trace the evolution of AI and ML in
chemical industries, delineate core ML components, and provide resources for ML
beginners. A detailed discussion on various ML methods is presented, covering
regression, classification, and unsupervised learning techniques, with
performance metrics and examples. Ensemble methods, deep learning networks,
including MLP, DNNs, RNNs, CNNs, and transformers, are explored for their
growing role in chemical applications. Practical workshops guide readers
through predictive modeling using advanced ML algorithms. The chapter
culminates with insights into science-guided ML, advocating for a hybrid
approach that enhances model accuracy. The extensive bibliography offers
resources for further research and practical implementation. This chapter aims
to be a thorough primer on ML's practical application in chemical engineering,
particularly for polyolefin production, and sets the stage for continued
learning in subsequent chapters. Please cite the original work [169,170] when
referencing.