Tidal forecast-driven predictive pump control under water-turnover constraints for energy-efficient operation in flow-through aquaculture

With the emerging food security problem caused by changes in population growth, the associated rapid growth of aquaculture systems has substantially increased their operating costs and energy consumption. To ensure sustainability in aquaculture, there is a growing demand to achieve greater energy efficiency in operations. This study proposes a control strategy to optimize power consumption by utilizing tidal forecast data during pump operation in a flow-through fish farm. In this strategy, energy consumption is reduced by optimizing the flow rate by adjusting the pump speed in response to tidal-induced changes in suction pressure. A pump-performance model was developed based on the Affinity law and part-load efficiency. The optimal pump control schedule was derived using tidal forecast data and the Particle Swarm Optimization algorithm. Although the optimized method generated greater fluctuations in flow rate than the conventional method, it effectively achieved the required standard, reducing energy consumption by ca. 32.56 %, and energy savings increased with the accuracy of the tidal forecast data. This methodology is expected to improve the operational efficiency of the flow-through aquaculture system and to contribute to the sustainable development of aquaculture.