Prediction of terephthalic acid (TPA) yield in aqueous hydrolysis of polyethylene terephthalate (PET)

• URL: http://arxiv.org/abs/2201.12657v1
• Date: Sat, 29 Jan 2022 20:51:36 GMT
• Title: Prediction of terephthalic acid (TPA) yield in aqueous hydrolysis of polyethylene terephthalate (PET)
• Authors: Hossein Abedsoltan, Zeinab Zoghi, Amir H. Mohammadi
• Abstract summary: Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) Modeling PET hydrolysis by considering the effective factors can provide useful information for material scientists. For the first time, 381 experimental data were gathered to model the aqueous hydrolysis of PET.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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