论文标题
在聚对苯二甲酸酯（PET）水解中苯甲酸（TPA）的预测
Prediction of terephthalic acid (TPA) yield in aqueous hydrolysis of polyethylene terephthalate (PET)
论文作者
论文摘要
水解水解用于化学回收的聚丙烯二苯二甲酸酯（PET），这是由于PET单体的高质量第苯甲酸（TPA）的产生。 PET水解取决于各种反应条件，包括宠物大小，催化剂浓度，反应温度等。因此，通过考虑有效因素来对PET水解进行建模可以为材料科学家提供有用的信息，以指定如何设计和运行这些反应。它将通过优化水解条件来节省时间，能量和材料。机器学习算法可以设计模型以预测输出结果。第一次收集了381个实验数据，以建模PET的水解水解。对PET水解的有效反应条件与TPA产量相关。应用逻辑回归来对反应条件进行排名。提出了两种算法，即人工神经网络多层感知（ANN-MLP）和基于自适应网络的模糊推理系统（ANFIS）。该数据集分别分为培训和测试集，分别训练和测试模型。这些模型预测TPA在ANFIS模型表现优于外观的情况下足够产生。 R平方（R2）和根平方误差（RMSE）损耗函数用于测量模型的效率并评估其性能。
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.
