Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux

Abstract

Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered neural network model is developed to predict the permeate flux in forward osmosis. The developed model is tested for its generalization capability by including lab-scale experimental data from several published studies. Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and the draw solution and temperature of the feed solution and the draw solution. The development of optimum network architecture is supported by studying the impact of the number of neurons and hidden layers on the neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of the model is tested against untrained published data. The performance of the ANN model is compared with a transport-based model in the literature. A simple machine learning technique such as a multiple linear regression (MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its ability to form a complex relationship between inputs and output better than MLR.