Data Driven Modeling and Design of Cellulose Acetate-Polysulfone Blend Ultrafiltration Membranes Based on Artificial Neural Networks

Abstract

This study aimed to develop and validate an Artificial Neural Networks (ANNs) model for the design and optimization of cellulose acetate-polysulfone blend ultrafiltration membranes, produced via the Non-Solvent Induced Phase Separation method. After some data science applications on a comprehensive dataset obtained from literature studies, the ultimate ANNs model exhibited superior predictive capabilities and effectively captured complex nonlinear relationships in the data. The optimum model configuration with a single hidden layer containing six neurons provided reliable predictions by avoiding overfitting and underfitting risks and significantly reducing error metrics. The model analyzed the effects of input variables on outputs, revealing that different stages of the membrane preparation process had varying impacts on performance metrics. This finding emphasized the importance of systematically optimizing the preparation process to enhance overall membrane performance. The model's predictions showed strong agreement with experimental data, further validating its accuracy. The optimum production conditions identified by the model offered significant improvements in membrane performance. Moreover, the model accelerated the membrane development process by reducing the required number of experimental trials and promoting efficient resource utilization. This approach contributed to both economic and environmental sustainability by reducing production costs and energy consumption. This study highlighted the significant potential of machine learning techniques for future innovations and advancements in this field by enabling precise, efficient, and sustainable membrane design and synthesis.