Data-driven online feedback optimization of solar membrane distillation systems operating in batch mode

Abstract

Membrane distillation is a separation technology that can be powered with low-temperature solar energy and perform brine concentration treatments. However, the operations under changing radiation and salinity conditions make it necessary to use specific control and optimization strategies to maximize the MD system’s performance. In this work, an online feedback optimization technique is proposed to address this issue in real-time. This strategy allows us to intrinsically manage constraints on the input and output of the process while minimizing its thermal energy consumption, which makes progress concerning previous approaches in the literature for MD systems. In addition, the online feedback optimization controller is combined with the least squares technique, which turns the approach into a model-free controller that helps to overcome the difficulty in modeling MD systems. The proposed strategy was tested in a trustworthy simulation scenario, using a validated model and real data from a facility located at Plataforma Solar de Almería (Spain). The results show its superior performance over a previous controller in the literature and non-optimal approaches, allowing us to reduce the thermal energy consumption by 0.5% and up to 6.7%, respectively, which has a direct impact on the cost of water treatment. On the whole, the proposed controller results in an attractive plug-and-play strategy with energy savings achievements for general use in commercial MD modules.