Modeling and optimization of chlorophenol rejection for spiral wound reverse osmosis membrane modules. (April 2021)

• Record Type: Journal Article
• Authors: Sivanantham, V.; Narayana, P.L.; Hyeong, Kwon Jun; Pareddy, Preetham; Sangeetha, V.; Kyoung – Seok, Moon; In, Kim Hong; Sung, Hyo Kyung; Reddy, N.S.
• Is Part Of: Chemosphere. Volume 268(2021)
• Publication Date: 2021-04
• Subjects: Reverse osmosis -- Artificial neural networks -- Chlorophenol removal -- Wastewater; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2020.129345
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: This study shows an artificial neural network (ANN) model of chlorophenol rejection from aqueous solutions and predicting the performance of spiral wound reverse osmosis (SWRO) modules. This type of rejection shows complex non-linear dependencies on feed pressure, feed temperature, concentration, and feed flow rate. It provides a demanding test of the application of ANN model analysis to SWRO modules. The predictions are compared with experimental data obtained with SWRO modules. The overall agreement between the experimental and ANN model predicted was almost 99.9% accuracy for the chlorophenol rejection. The ANN model approach has the advantage of understanding the complex chlorophenol rejection phenomena as a function of SWRO process parameters. Highlights: Reverse Osmosis process modeled to predict Chlorophenol rejection. Impact of inputs on Chlorophenol rejection evaluated by the index of relative importance. Sensitivity analysis helped to identify variables effect on chlorophenol rejection. Process window for maximum Chlorophenol rejection was identified.