Estimation of pressure drop in a demister for multi-stage flash desalination process based on stacked long short-term memory (LSTM) neural networks

Abstract

The thermal desalination process such as a multi-stage flash desalination process (MSF) uses demisters as the separator between the flashed off vapor and the brine droplets in the flashing stage. The performance of the MSF plant depends on the quantity of fresh water produced. The separation efficiency depends on the pressure drops in the demister that influences the plant performance. This study proposes the application of Long Short-Term Memory (LSTM) neural networks for estimating pressure drop across demisters. The stacked LSTM algorithm is effective in estimating the pressure drop for experiment and real plant data. The superiority of stacked LSTM algorithm above reference benchmarks is evident. The Root Mean Square Error (RMSE) of the estimation from stacked LSTM algorithm is less than the estimation from the work of Al-Fulaij et al CFD model Al-Fulaij H, et al 2016, Desalination, 385: 148–157 by 40%.