Issue 6, 2024

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

Abstract

Polypropylene in-reactor alloys present a complex structure influenced by diverse polymerization process parameters, posing challenges for traditional analysis methods in establishing a quantitative relationship between process conditions, alloy structures and mechanical properties. To address this issue, a series of polypropylene/poly(ethylene-co-propylene) alloys with varied structures were synthesized by gas-phase polymerization. Machine learning methods were employed to develop regression models for predicting flexural strength (FS), impact strength (IS) and rubber phase content. The importance of structure and process condition descriptors was further analysed to reveal the process–structure–property relationship. The FS and IS prediction models utilizing Extreme Gradient Boosting (XGB) algorithms achieved impressive R2 scores of 0.9846 and 0.9841, respectively. Notably, the significant contribution of the rubber phase content to FS and IS prediction was observed in the structure descriptors. Furthermore, process condition descriptors (flowrate and initial pressure) played crucial roles in rubber synthesis, thereby exerting a substantial impact on FS and IS. In light of the feature importance analysis, new experimental runs were designed to synthesize alloys with enhanced IS. The experimental results closely aligned with the model predictions (RMSE = 4.4751 for IS). This research provides a new approach to establish process–structure–property relationships for in-reactor alloys, providing a convenient method for designing experiments to attain desired material properties.

Graphical abstract: Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

Supplementary files

Article information

Article type
Paper
Submitted
26 sen 2023
Accepted
07 fev 2024
First published
08 fev 2024
This article is Open Access
Creative Commons BY-NC license

React. Chem. Eng., 2024,9, 1354-1363

Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys

S. Zheng, X. Huang, J. Hu and Z. Yao, React. Chem. Eng., 2024, 9, 1354 DOI: 10.1039/D3RE00504F

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements