Intelligent framework for coagulant dosing optimization in an industrial-scale seawater reverse osmosis desalination plant

Abstract

Efficient and reliable desalination through seawater reverse osmosis (SWRO) mandates optimized pre-treatment strategies to minimize organic and inorganic fouling. Coagulation, the process of agglomerating colloidal particles using chemical coagulants, in combination with media filtration to reduce colloidal fouling on reverse osmosis membranes is commonly used in seawater pretreatment. Due to its inherent complexity and the absence of physical models to quantify the efficiency of coagulation, overdosing of coagulants is ubiquitously observed to maintain filtered water quality. To address this problem, we use Artificial neural networks (ANNs) to optimize coagulant dosing by predicting the SDI after chemical dosing. The model is developed by using large-scale plant data comprising of different seawater physical parameters and plant operational data including pH, SDI, turbidity, coagulant dosing rate, and flocculant dosing rate. By using feature engineering, selection, and our domain knowledge, new input parameters are derived, irrelevant parameters are eliminated, and these are used as inputs to train the model. The developed ANNs model achieved a prediction accuracy of 95% also outperforms other machine learning methods, and upon industrialization it reduced annual coagulant consumption by 11.7% when implemented in a commercial SWRO plant producing 216,000 m3/day of desalinated water.