A review of the application of response surface methodology in nanofiltration: Insights into process modeling, parametric analysis, and optimization - Elgharbi, Sarra; Ounifi, Ibtissem; Boubakri, Ali; Abdedayem, Asma (Separation science and technology, 2025)
Nanofiltration (NF) is a promising membrane technology for water treatment, desalination, and various industrial applications. To optimize NF performance, it is essential to thoroughly understand the interactions between operating parameters, membrane characteristics, and overall system efficiency. Response Surface Methodology (RSM) has become a valuable statistical tool for modeling and optimizing NF processes by systematically evaluating the effects of different parameters and predicting optimal conditions. This review provides an in-depth assessment of RSM applications in NF, focusing on key aspects such as permeate flux, contaminant rejection, energy efficiency, fouling mitigation, and membrane design. Special focus is placed on how RSM enhances energy efficiency in NF hybrid systems, improves membrane longevity, and advances process sustainability. Furthermore, RSM has played a crucial role in developing predictive models that assist in decision-making regarding NF system optimization. Future research should investigate the integration of RSM with emerging computational techniques, including machine learning, digital twins, and real-time monitoring, to create intelligent, self-adaptive NF systems. Additionally, incorporating sustainability metrics, such as life cycle assessment and techno-economic analysis, into RSM tools will aid in developing cost-effective and environmentally sustainable NF processes. By combining statistical modeling with modern computational approaches, RSM continues to drive advancements in NF technology, leading to more efficient and sustainable solutions for water treatment.