AI-Assisted Kinetic Modeling of PLA Hydrolysis Under Subcritical Water Conditions for Sustainable Polymer Recycling

Year : 2026 | Volume : 17 | Issue : 02 | Page : 65 74
By

Sanika Dahiwal,

Vaibhav Godase,

  1. UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Korti, Maharashtra, India
  2. Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Korti, Maharashtra, India

Abstract

The accumulation of poly (lactic acid) (PLA) in terrestrial and marine ecosystems has intensified the demand for closed-loop, green recycling technologies. Subcritical water (SCW) hydrolysis offers a promising, catalyst-free pathway for the rapid depolymerization of PLA into its constituent monomer, lactic acid. However, the complex, highly non-linear kinetics governing macro-molecular degradation under variable hydrothermal conditions limit real-time process optimization and industrial scalability. This study develops a novel artificial intelligence (AI)-assisted kinetic modeling framework to predict and optimize PLA hydrolysis within a subcritical water environment. Experimentally, PLA depolymerization was evaluated across a temperature range of 160°C to 240°C, pressures from 5 MPa to 15 MPa, and reaction times spanning 5 to 60 minutes. Characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and gas chromatography-mass spectrometry (GC-MS), verified structural evolution and monomer purity. To map these intricate multi-variable relationships, we trained and compared artificial neural network (ANN) and extreme gradient boosting (XGBoost) architectures using an experimental dataset containing 180 distinct data points. The XGBoost model demonstrated superior predictive capability, capturing the empirical monomer yield with high precision and successfully mapping the transition from oligomeric cleavage to thermal monomer degradation. This machine learning approach successfully bypassed the limitations of traditional oversimplified first-order kinetic equations. The integration of SCW with predictive AI analytics establishes a high-efficiency, low-carbon framework for poly (lactic acid) chemical recycling, advancing the economic viability of the plastics circular economy.

Keywords: Poly (lactic acid), subcritical water hydrolysis, artificial intelligence, kinetic modeling, chemical recycling, circular economy.

[This article belongs to Journal of Modern Chemistry & Chemical Technology ]

How to cite this article: Sanika Dahiwal, Vaibhav Godase. AI-Assisted Kinetic Modeling of PLA Hydrolysis Under Subcritical Water Conditions for Sustainable Polymer Recycling. Journal of Modern Chemistry & Chemical Technology. 2026; 17(02):65-74.
How to cite this URL: Sanika Dahiwal, Vaibhav Godase. AI-Assisted Kinetic Modeling of PLA Hydrolysis Under Subcritical Water Conditions for Sustainable Polymer Recycling. Journal of Modern Chemistry & Chemical Technology. 2026; 17(02):65-74. Available from: https://journals.stmjournals.com/jomcct/article=2026/view=256962

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Regular Issue Subscription Original Research
Volume 17
Issue 02
Received 17/07/2026
Accepted 18/07/2026
Published 27/08/2026
Publication Time 41 Days

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