The Master's thesis of researcher Zaman Emad Nazal was discussed at the College of Engineering, University of Basra, Department of Chemical Engineering, under the supervision of Professor Dr. Alaa Abdul-Razzaq Jassim, entitled Modeling and Evaluation of the Performance of Large Basra Water Project Under Different Operation Conditions
‏The reverse osmosis (RO) unit of the Large Basra Water Project (LBWP) is a primary desalination station for potable water production, with a daily capacity of 200,000 cubic meters across two separate production lines. The efficiency of these facilities depends on the integration of primary treatment stages with the RO membrane system design and operating conditions, including pressure, incoming salt concentration, flow rate, and temperature, as these parameters directly impact energy consumption, membrane lifespan, and produced water quality. Experimental operational data were collected from the LBWP over a full year (December 2023–November 2024) through the SCADA system. The accuracy of the mathematical prediction model was verified by comparing it with the IMS Design Software used in the plant's design and with the operational values measured during plant operation. 
‏   The study investigated the influence of key operating parameters on the performance of the Reverse Osmosis (RO) system. An increase in applied pressure was found to enhance both the recovery and salt rejection rates, while simultaneously decreasing the water permeability coefficient and increasing the salt permeability coefficient. Conversely, increasing the feedwater salinity resulted in a lower recovery rate and water permeability coefficient, but improved the salt rejection rate and increased the salt permeability coefficient. Furthermore, a higher feed flow rate led to an improvement in both recovery and salt rejection, along with an increase in both water and salt permeability coefficients. Lastly, an elevation in temperature improved the recovery rate and water permeability coefficient, but led to a decrease in both the salt rejection rate and the salt permeability coefficient.
‏     A neural network model utilizing the Bayesian backpropagation algorithm was developed to predict permeate flow rate (Qp) and permeate concentration (Cp) based on six operating parameters: pressure, temperature, feed flow rate, feed concentration, turbidity, and pH . The model demonstrated excellent predictive performance for Qp (R = 0.998, RMSE = 27.53) and acceptable performance for Cp (R = 0.95, RMSE = 6.90). Sensitivity analysis revealed that feedwater flow rate (42.94%) and feedwater salinity (28.23%) were the most influential parameters, followed by pressure (12.94%), temperature (7%), turbidity (6.72%), and pH (2.17%). Additionally, multiple linear regression models were developed using IBM SPSS Statistics 23 to correlate system performance with operating conditions, providing complementary predictive tools for optimizing RO system efficiency.