Summary of Applications Of Machine Learning to Optimizing Polyolefin Manufacturing, by Niket Sharma and Y.a. Liu
Applications of Machine Learning to Optimizing Polyolefin Manufacturing
by Niket Sharma, Y.A. Liu
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This preprint chapter from our book explores leveraging machine learning (ML) in chemical and polyolefin manufacturing optimization. It provides a comprehensive overview of ML applications in chemical processes, tracing its evolution and core components. The chapter delves into various ML methods, including regression, classification, and unsupervised learning techniques, with performance metrics and examples. Ensemble methods, deep learning networks (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 concludes by advocating for a hybrid approach that enhances model accuracy, setting the stage for continued learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This chapter explains how machine learning (ML) can be used to make better chemical products. It’s written for both beginners and experts who want to learn about ML in chemistry. We show how ML has evolved over time and what it is capable of doing. The chapter covers different types of ML, including regression, classification, and unsupervised learning. We also explore special kinds of ML called ensemble methods and deep learning networks. Practical exercises help readers apply these techniques to real-world problems. |
Keywords
* Artificial intelligence * Classification * Deep learning * Machine learning * Optimization * Regression * Unsupervised
