Integrated Experimental and Analytical Investigation of Reverse Osmosis Desalination Systems

Abstract

Water scarcity is a growing global issue, necessitating innovative and sustainable solutions for freshwater generation. Among the available technologies, reverse osmosis (RO) has become the primary method for seawater desalination due to its effective salt rejection and high energy efficiency. This study presents an integrated experimental and analytical investigation of a full-scale seawater reverse osmosis (SWRO) plant with a 2 MLD capacity at the Victoria and Alfred (V&A) Waterfront in Cape Town, South Africa. Operational data were collected over six months, including feedwater temperature (13.66 –16.78 °C), pressure (50–60 bar), total dissolved solids (32,883–38,387 mg/L), and pH (6.19–7.89). The plant consistently produced high-quality permeate with TDS around 500 mg/L, achieving a 31% recovery rate at an average energy consumption of 3 kWh/m³. Machine learning models, specifically multiple linear regression and decision trees, were used to predict RO performance and to explore the relationships between operational parameters. Results show that higher feed pressure improves permeate flux but raises energy use, increased feedwater temperature boosts flux and slightly reduces energy consumption, while deviations from near-neutral pH negatively impact product quality and efficiency. The novelty of this work lies in combining real plant operational data with predictive analytics to establish parameter-based performance relationships and identify optimal operating ranges (e.g., feed pressure ~52–55 bar, pH ~7). These insights provide a strong foundation for optimizing desalination processes, improving membrane efficiency, and guiding the design and operation of future RO desalination projects.