  Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate
(PET) due to the production of high-quality terephthalic acid (TPA), the PET
monomer. PET hydrolysis depends on various reaction conditions including PET
size, catalyst concentration, reaction temperature, etc. So, modeling PET
hydrolysis by considering the effective factors can provide useful information
for material scientists to specify how to design and run these reactions. It
will save time, energy, and materials by optimizing the hydrolysis conditions.
Machine learning algorithms enable to design models to predict output results.
For the first time, 381 experimental data were gathered to model the aqueous
hydrolysis of PET. Effective reaction conditions on PET hydrolysis were
connected to TPA yield. The logistic regression was applied to rank the
reaction conditions. Two algorithms were proposed, artificial neural network
multilayer perceptron (ANN-MLP) and adaptive network-based fuzzy inference
system (ANFIS). The dataset was divided into training and testing sets to train
and test the models, respectively. The models predicted TPA yield sufficiently
where the ANFIS model outperformed. R-squared (R2) and Root Mean Square Error
(RMSE) loss functions were employed to measure the efficiency of the models and
evaluate their performance.
