Energy optimization in large-scale recirculating aquaculture systems: Implementation and performance analysis of a hybrid deep learning approach
Ashwaq M. Alnemari, Wael M. Elmessery, Farahat S. Moghanm, V. Espinosa, Mahmoud Y. Shams, Abdallah Elshawadfy Elwakeel, Omar Saeed, Mohamed Hamdy Eid, Sadeq K. Alhag, Laila A. Al‐Shuraym, Lamya Ahmed Al‐Keridis, A.E. El-Namas
Abstract
Recirculating Aquaculture Systems (RAS) represent an increasingly important solution for sustainable fish production, yet their high energy consumption remains a significant operational challenge. This study extends our previous work on using Deep Deterministic Policy Gradient (DDPG) for optimizing feeding rates in Recirculating Aquaculture Systems (RAS) by developing a hybrid Long Short-Term Memory (LSTM)-DDPG approach for energy optimization in a large-scale commercial RAS facility. The system, comprising 108 tanks with a total water volume of 3,132 m³, was monitored over a complete annual cycle, collecting 8,760 hourly observations of environmental, biological, and operational parameters. The hybrid model achieved high predictive accuracy for energy consumption patterns, with R² values exceeding 0.91 for key components. Implementation resulted in a 15-20% reduction in daily energy consumption while maintaining optimal water quality. Economic analysis revealed a 17% decrease in energy costs per kilogram of fish production. The system's performance was validated under varying fish biomass densities (80-120 kg/m³) and seasonal temperature profiles. These findings demonstrate the effectiveness of integrating deep learning techniques for energy optimization in RAS, offering a scalable solution for enhancing the economic and environmental sustainability of intensive aquaculture operations.