Abstract
Operating reverse osmosis (RO) systems with renewable energy (RE) can contribute greatly to water security. However, the stochastic and intermittent nature of renewables means that most large-scale RO relies on fossil fuels via a grid connection. Modular operation by connecting and disconnecting RO units is promising to power multi-unit RO entirely from RE. Nevertheless, it may lead to excessive start-ups/shutdowns, especially when using wind energy. This paper proposes using neural networks for wind speed prediction and scheduling to improve the modular operation of wind-powered RO. A modular operation technique was developed for a three-unit RO system with variable water output. To estimate the number of operating units, a neural network was designed to predict wind speed 24 hrs ahead, giving a correlation (R = 0.64) and a RMSE of 1.54 m/s against real data. Two approaches, high- and low-output scheduling, were defined to either maximise production or minimise unplanned shutdowns during modular operation. The high- and low-output scheduling reduced the number of start-up/shutdown cycles by 37.5 % and 75 % compared to unscheduled operation, leading to a 1.9 % and 2.3 % improvement in specific energy consumption, respectively. Overall, scheduled RO operation minimised unplanned shutdowns and delivered stable performance while following recommended operating procedures.
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