Optimization of a polyvinyl butyral synthesis process based on response surface methodology and artificial neural network

Abstract

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 (d_p). 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 (A), the dosage of DES (B) and n-butanal (C), and the aging temperature (D) were selected as process variables, and the comprehensive score (AD, d_p and material and energy consumption) was introduced as the response. The results showed that single-factors B, C, D, and the interactions AB, BC and CD had significant effects on the comprehensive score, and the qualified PVB products (AD > 81%, d_p = 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.