Predictive modeling and optimization of degasser efficiency in recirculating aquaculture systems using a hybrid ANN-PSO approach

In recirculating aquaculture systems (RASs), degassers maintain optimal water quality by removing dissolved carbon dioxide (CO2). The performance of a degasser is generally evaluated based on its standard stripping efficiency (SSE), which is affected by its operating parameters. The present study aimed to optimize the air flow rate (QA), water flow rate (QW), and packing media height (PMH) to enhance degasser performance. To achieve this, an artificial neural network (ANN) and particle swarm optimization (PSO) were combined for parametric optimization and the predictive modeling of the SSE. The ANN model was trained using experimental data to predict the SSE, and PSO was then employed to optimize the operational parameters to achieve the maximum SSE. The optimal QA, QW, and PMH were found to be 355 m³/h, 35 m³/h, and 0.65 m, respectively, generating a maximum SSE of 0.188 kg CO2/kWh. The hybrid ANN-PSO approach was then validated by comparing experimental and predicted SSE values, with a difference between the two of only ±2.08 %. This confirms that the proposed optimization technique can reliably improve the SSE of degassers in RASs.