Artificial neural networks for modeling the reverse osmosis unit in a wastewater pilot treatment plant

This paper presents experimental and modeling data from a membrane-based wastewater treatment (WWT) pilot plant. The effluents from various upstream steps of a can-manufacturing plant were combined and subjected to a pretreatment process, which consisted of coalescing filters, coagulation and gravity settling, and sand activated carbon and polishing filtration, and a pressure-driven membrane process, such as reverse osmosis (RO). The performance of the RO membrane was evaluated and experiments were conducted using continuous wastewater flow. The complete membrane separation scheme was validated with a closed loop cell through several experiments, in which the concentration of the antiscaling agent and the pH were varied to determine the optimal operational conditions. Detailed parametric studies for these continuous flow experiments were conducted, and the permeate flow rates in the RO membrane system were experimentally measured. The experimental flow data were correlated and analyzed using an artificial neural network (ANN). A four-layer feed-forward network with a back-propagation algorithm was used to train the ANN models. After the training process was completed, the experimental flow data was used to assess the prediction capabilities of the networks based on the RO permeate water flow rate. This research showed that the RO unit results in the acceptable removal of 96.1% of the total dissolved solids and a maximum effluent recovery close to 72%. The predicted and experimental flow data were well correlated, and a determination coefficient between 0.97 and 0.99 was achieved.