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.

翻译：本章是本书的预印本，聚焦于利用机器学习优化化学及聚烯烃制造工艺。内容兼顾新手与资深从业者，旨在帮助读者了解机器学习在化学过程中的最新应用。我们梳理了人工智能与机器学习在化学工业中的发展历程，阐述了机器学习核心组件，并为初学者提供了学习资源。文中详细讨论了各类机器学习方法，涵盖回归、分类及无监督学习技术，并附有性能指标与实例分析。针对集成方法、深度学习网络（包括MLP、DNNs、RNNs、CNNs及Transformer等）在化学应用中日益增长的作用进行了深入探讨。实践环节通过引导读者运用先进机器学习算法进行预测建模，提升实操能力。本章最终以科学引导型机器学习的洞察收尾，倡导采用混合方法以提升模型精度。详尽的参考文献为后续研究与实践应用提供了资源支撑。本章旨在成为化学工程（尤其是聚烯烃生产）中机器学习实际应用的全面入门指南，并为后续章节的持续学习奠定基础。引用时请注明原始文献[169,170]。