Yes / Reverse Osmosis (RO) process can be considered as one of the intensively used pioneering equipment for reusing wastewater of several applications. The recent study presented the development of an accurate model for predicting the dimethylphenol removal from wastewater via RO process. The Response Surface Methodology (RSM) was applied to carry out this challenge based on actual experimental data collected from the literature. The independent variables considered are the inlet pressure (5.83-13.58) atm, inlet temperature (29.5-32) ° C, inlet feed flow rate (2.166-2.583) × 10-4 m3/s, and inlet concentration (0.854-8.049) × 10-3 kmol/m3 and the dimethylphenol removal is considered as the response variable. The analysis of variance showed that the inlet temperature and feed flow rate have a negative influence on dimethylphenol removal from wastewater while the inlet pressure and concentration show a positive influence. In this regard, F-value of 240.38 indicates a considerable contribution of the predicted variables of pressure and concentration against the process dimethylphenol rejection. Also, the predicted R2 value of 0.9772 shows the high accuracy of the model. An overall assessment of simulating the performance of RO process against the operating parameters has been systematically demonstrated using the proposed RSM model.

Yes / The successful use of Reverse Osmosis (RO) process has increased significantly in water desalination, water treatment and food processing applications. In this work, the economic feasibility of a multi-stage RO process including both retentate and permeate reprocessing for the removal of chlorophenol from wastewater is explored using simulation and optimisation studies. Firstly, a mathematical model of the process is developed based on the solution diffusion model, which was validated using experimental chlorophenol removal from the literature, is combined with several appropriate cost functions to form a full model package. Secondly, for a better understanding of the interactions between the different parameters on the economic performance of the process, a detailed process simulation is carried out. Finally, a multi-objective optimisation framework based on Non-Linear Programming (NLP) problem is developed for minimising the product unit cost, the total annualised cost, the specific energy consumption together with optimising the feed pressure and feed flow rate for an acceptable level of chlorophenol rejection and total water recovery rate. The results clearly show that the removal of chlorophenol can reach 98.8% at a cost of approximately 0.21 $/m³.