Luan Wenwen, Sun Li, Zeng Zuoxiang, Xue Weilan
School of Chemical Engineering, East China University of Science and Technology 200237 Shanghai China
RSC Adv. 2023 Mar 8;13(11):7682-7693. doi: 10.1039/d2ra08099k. eCollection 2023 Mar 1.
High quality polyvinyl butyral (PVB) can be used as the intermediate film of automobile and building glass and the packaging film of photovoltaic cells. Therefore, it is necessary to optimize its synthesis process to obtain suitable products with a high acetalization degree (AD) and small particle size ( ). In this work, a deep eutectic solvent (DES) was selected as the catalyst, and response surface methodology (RSM) and artificial neural network (ANN) were utilized to optimize the synthesis process of PVB. The concentration of polyvinyl alcohol (), the dosage of DES () and -butanal (), and the aging temperature () were selected as process variables, and the comprehensive score (AD, and material and energy consumption) was introduced as the response. The results showed that single-factors , , , and the interactions , and had significant effects on the comprehensive score, and the qualified PVB products (AD > 81%, = 3-3.5 μm) were obtained under the optimal conditions obtained by RSM and ANN models. ANN is a better and more precise optimization tool than RSM. Also, DES played a dual role in catalysis and dispersion in the synthesis of PVB and showed good reusability, so it has great application potential in PVB industrial production.
高质量的聚乙烯醇缩丁醛（PVB）可作为汽车和建筑玻璃的中间膜以及光伏电池的封装膜。因此，有必要优化其合成工艺，以获得具有高缩醛化度（AD）和小粒径（ ）的合适产品。在这项工作中，选择了一种深共晶溶剂（DES）作为催化剂，并利用响应面法（RSM）和人工神经网络（ANN）来优化PVB的合成工艺。选择聚乙烯醇的浓度（ ）、DES的用量（ ）和正丁醛的用量（ ）以及老化温度（ ）作为工艺变量，并引入综合评分（AD、 以及材料和能源消耗）作为响应。结果表明，单因素 、 、 以及相互作用 、 和 对综合评分有显著影响，在RSM和ANN模型获得的最佳条件下得到了合格的PVB产品（AD>81%， =3-3.5μm）。与RSM相比，ANN是一种更好、更精确的优化工具。此外，DES在PVB的合成中起到了催化和分散的双重作用，并且具有良好的可重复使用性，因此在PVB工业生产中具有很大的应用潜力。
