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.

本文介绍了机器学习在化学和聚烯烃制造优化中的应用，从 AI 和 ML 在化学工业的演变、核心 ML 组件和入门资源、各种 ML 方法以及深度学习网络探索，到使用先进 ML 算法进行预测建模的实践工作坊，提倡科学引导的机器学习方法，旨在为化学工程和聚烯烃生产中的 ML 实际应用提供详尽介绍，并为后续学习奠定基础。