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

Area of Science:

• Polymer Chemistry
• Materials Science
• Chemical Engineering

Background:

• High-quality polyvinyl butyral (PVB) is crucial for automotive glass, building glass, and photovoltaic cell packaging.
• Optimizing PVB synthesis is necessary to achieve high acetalization degree (AD) and small particle size (d _p).

Purpose of the Study:

• To optimize the synthesis process of PVB using deep eutectic solvent (DES) as a catalyst.
• To compare the efficacy of response surface methodology (RSM) and artificial neural network (ANN) for process optimization.

Main Methods:

• Utilized DES as a catalyst for PVB synthesis.
• Employed response surface methodology (RSM) and artificial neural network (ANN) to optimize process variables: polyvinyl alcohol concentration (A), DES dosage (B), n-butanal dosage (C), and aging temperature (D).
• Introduced a comprehensive score including AD, d _p, and material/energy consumption as the response variable.

Main Results:

• Identified significant effects of variables B, C, D, and interactions AB, BC, CD on the comprehensive score.
• Achieved qualified PVB products with AD > 81% and d _p = 3-3.5 μm under optimal conditions determined by both RSM and ANN models.
• Demonstrated that ANN is a more precise optimization tool than RSM.

Conclusions:

• The optimized PVB synthesis process yields high-quality products suitable for industrial applications.
• Deep eutectic solvents (DES) exhibit a dual role in catalysis and dispersion, with good reusability, indicating significant potential for industrial PVB production.
• Artificial neural networks offer superior performance for optimizing complex chemical synthesis processes compared to traditional methods like RSM.