Research Article
A LSTM-based Model Predictive Control for a Reverse Osmosis Desalination Plant
Divas Karimanzira and Thomas Rauschenbach
Department of surface water and maritime system, Fraunhofer IOSB, Am Vogelherd 50, 98693 Ilmenau.
Submitted: September 16, 2020; Accepted: December 25, 2020
Abstract
Reverse Osmosis (RO) desalination plants are highly nonlinear multi-input-multioutput systems that affected by uncertainties, constraints and some physical phenomenon such as membrane fouling that are mathematically difficult to describe. Such systems require effective control strategies that take these effects into account. Such a control strategy is the nonlinear model predictive (NMPC) controller. However, a NMPC depends very much on the accuracy of the internal model used for prediction in order to maintain feasible operating conditions of the RO desalination plant. Recurrent Neural Networks (RNNs), especially the Long-Short-Term Memory (LSTM) can capture complex nonlinear dynamic behavior and provide long-range predictions even in the presence of disturbances. Therefore, in this paper a NMPC for a RO desalination plant that utilizes a LSTM as the predictive model will be presented. It will be tested to maintain a given permeate flow rate and keep the permeate concentration under a certain limit by manipulating the feed pressure. Results show a good performance of the system.
Keywords
Desalination; Model Predictive Control; Artificial Intelligence; Long Short Term Memory Neural Network; Reverse Osmosis.
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