Predicting forward osmosis performance with synthesized polyamide-based membrane: An integrated machine learning (MATLAB and ANN) and economic analysis framework

Abstract

Forward Osmosis (FO) has emerged as a highly promising membrane-based water treatment process owing to its intrinsic advantages, such as low energy requirements and the potential for high water recovery. The lab-prepared highly efficient thin film composite (TFC) FO membranes that were used in this research work. The resultant polyamide-based FO membrane showed exceptional performance, notably achieving a high-water flux of 60.94 ± 3.5 L/m2.h and a constrained solute flux of 1.52 ± 0.08 g/m2.h. Machine learning methodologies are strategically employed in this study to construct predictive models for FO membrane performance. Notably, the accuracy of the van't Hoff equation's linear assumption is significantly enhanced by introducing an osmotic pressure calculation based on water activity, considering the nuanced effects of external and internal concentration polarization. The research employs MATLAB software alongside an artificial neural network (ANN) to develop predictive models. MATLAB facilitates the creation of a comprehensive theoretical model, offering insights into forecasting water flux through FO membranes by systematically analyzing diverse membrane parameters and their impact on performance. Concurrently, the ANN model was employed to predict the permeate flux for the laboratory-scale FO membrane system, utilizing the input parameters within the network's training range (R2 > 0.91). An economic assessment is also conducted to estimate the projected membrane cost, a pivotal determinant of system viability. Collectively, these investigations yield valuable insights into the intricacies of FO membrane design and optimization, elucidating their performance dynamics in water treatment and allied applications. This research significantly contributes to the ongoing quest to develop more efficient and precise membrane processes.