Yes / Reverse osmosis (RO) is becoming one of the most promising technologies used in wastewater treatment because it offers high rate of contaminant rejection and lower energy consumption in comparison with other thermal treatment processes. Earlier research by the same authors in respect of a distributed one-dimensional mathematical model for a single spiral-wound RO membrane module based on the solution-diffusion model has been used in this paper to simulate the rejection of NDMA (N-nitrosodimethylamine) from wastewater in a series of seven RO elements full-scale treatment plant. Firstly, the applicability of this model has been evaluated using a simulation study and the results have been compared against experimental data gathered from the literature for a given plant. Secondly, further simulation and analysis studies are carried out to assess the performance of the plant for NDMA rejection and recovery rate under different operating conditions of feed pressure, flow rate, and concentration. For the studied RO configuration, it is concluded that a maximum of 55.1% NDMA rejection can be achieved, which confirms the remaining issue of lower NDMA rejection.

Yes / N-nitrosamine in wastewater treatment processes can contribute to several public health impacts including human carcinogens even at very low concentration. In this work, spiral-wound reverse osmosis (SWRO) process is used to remove N-nitrosamine compounds from wastewater. Effects of operating parameters of the SWRO process on the removal of N-nitrosamine, total water recovery, and specific energy consumption for a SWRO configurations are evaluated via simulation and optimisation. For this purpose, the one-dimensional distributed model developed earlier by the authors is modified by including different mass transfer coefficient correlation, temperature dependent water and solute permeability correlations and energy equations. The model is first validated by estimating a new set of model parameters using eight set of experimental data from the literature and is then used to simulate the process with and without energy recovery device to facilitate deeper insight of the effect of operating conditions on the process performance. The model is then embedded within an optimisation framework and optimisation problems to maximise N-nitrosamine rejections and to minimise specific energy consumption are formulated and solved while the operating conditions are optimized simultaneously.

Yes / The need for desalinated seawater and reclaimed wastewater is increasing rapidly with the rising demands for drinkable water required for the world with continuously growing population. Reverse Osmosis (RO) processes are now among the most promising technologies used to remove chemicals from industrial effluents. N-nitrosamine compounds and especially N-nitrosodimethylamine (NDMA) are human carcinogens and can be found in industrial effluents of many industries. Particularly, NDMA is one of the by-products of disinfection process of secondary-treated wastewater effluent with chloramines, chlorines, and ozone (inhibitors). However, multi-stage RO processes with permeate reprocessing and recycling has not yet been considered for the removal of N-nitrosodimethylamine from wastewater. This research therefore, begins by investigating a number of multi-stage RO processes with permeate-reprocessing to remove N-nitrosodimethylamine (NDMA) from wastewater and finds the best configuration in terms of rejection, recovery and energy consumption via optimisation. For the first time we have applied Species Conserving Genetic Algorithm (SCGA) in optimising RO process conditions for wastewater treatment. Finally, permeate recycling is added to the best configuration and its performance is evaluated as a function of the amount of permeate being recycled via simulation. For this purpose, a mathematical model is developed based on the solution diffusion model, which is used for both optimisation and simulation. A number of model parameters have been estimated using experimental data of Fujioka et al. (Journal of Membrane Science 454 (2014) 212–219), so that the model can be used for simulation and optimisation with high accuracy and confidence.