A complementary approach of response surface methodology and an artificial neural network for the optimization and prediction of low salinity reverse osmosis performance

Ryan Brooke; Linhua Fan; Mohamed Khayet; Xu Wang

doi:10.1016/j.heliyon.2022.e10692

A complementary approach of response surface methodology and an artificial neural network for the optimization and prediction of low salinity reverse osmosis performance

Heliyon. 2022 Sep 21;8(9):e10692. doi: 10.1016/j.heliyon.2022.e10692. eCollection 2022 Sep.

Authors

Ryan Brooke¹, Linhua Fan¹, Mohamed Khayet², Xu Wang¹

Affiliations

¹ School of Engineering, RMIT University, Australia.
² Department of Structure of Matter, Thermal Physics, and Electronics Faculty of Physics, University Complutense of Madrid, Spain.

Abstract

The treatment of saline water sources by reverse osmosis (RO) is being utilized increasingly to address water shortages around the world. The application of RO is energy-intensive; therefore, plant and process optimization are crucial. The desalination of low salinity water sources with total dissolved solids (TDS) of <5000 mg/L is less energy intensive than the desalination of highly saline seawater and brackish water. A gap exists in optimization studies on lower salinity water (TDS = 500-5000 mg/L). The novelty of the study is the development of a complementary approach using response surface methodology (RSM) and an artificial neural network (ANN) for performance modelling, optimization, and prediction of RO desalination of low salinity water. Feed water salinity, pressure, and temperature were controlled variables to model the performance of the RO system. A performance index incorporating salt rejection efficiency and permeate flux was used as the response target of the system. The optimal parameter combination within their modelled range for the best performance index occurred near the highest pressure input of 150.57 psi, at the temperature of 38.8 °C, and at the lowest feed salt concentration of 577 mg/L. Both the RSM and ANN models demonstrated high validity. The RSM and ANN showed R² values of 0.99 each and with a root mean square error of 2.41 and 5.85 respectively. The RSM showed a small benefit in model accuracy over the ANN, but the ANN has the benefit of not requiring the central composite design before experimentation and being a continuously improving prediction method as more data becomes available. Further applications of the optimization and modelling approach can be applied to RO system optimization considering membrane types and additional feedwater characteristics.

Keywords: Artificial neural network; Low salinity; Response surface methodology; Reverse osmosis.