Indexed In Scopus
  Scopus ID: 21100926589

Comparative Analysis of Prediction Models for Melt Flow Rate of C2 and C3 Polymers Synthesized using Nanocatalysts

Devanshi Ledwani, Ishika Thakur and Vaibhav Bhatnagar

Review Article

Analysis of product quality is extremely important in any industry. The Polymer industry is gaining significant importance in recent years. C2 and C3 are polymers that are made up of monomeric units with two and three carbon atoms respectively. Nano Technology plays an important role in polymerization reactions which subsequently improves the quality and quantity of polymer products. The importance of measuring product quality in online polymerization industries poses significant difficulties. There are no online tools for measuring resin features that represent polymer quality, such as flow melting indicator MFI and congestion. MFI should invariably be tested in time-consuming and laborintensive lab analysis. In many processing units, MFI is measured occasionally rather than regularly during a day using manual analysis. This paper presents models to predict melt flow rate using models of Linear regression, Support Vector Regression, and Adaboost Regressor. Adaboost regressor fittest with the accuracy of 69.2%. The primary objective of this research is to identify the best machine learning algorithm that can accurately predict the melt flow rate.

Published on: October 29, 2022
doi: 10.17756/nwj.2022-s1-022
Citation: Ledwani D, Thakur I, Bhatnagar V. 2022. Comparative Analysis of Prediction Models for Melt Flow Rate of C2 and C3 Polymers Synthesized using Nanocatalysts. NanoWorld J 8(S1): S123-S127.

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