A simulation based water quality parameter control of aquaponic system employing model predictive control strategy incorporation with optimization technique

Abstract

Aquaponics is an integration of aquaculture and hydroponics systems, utilizing recirculating water to connect these two processes. Maintaining optimal water quality parameters is critical for the life of fish and plants and crucial for the optimal production in the aquaponics. However, this is difficult due to the complex dynamics in each system and the recirculations. Atmospheric temperature significantly impacts fish and plant growth by affecting water quality parameters. To address this, a mathematical model for key parameters, such as temperature and dissolved oxygen (DO), is introduced, along with a model predictive controller (MPC) that is designed to maintain these parameters at optimal levels. The ideal operating points for temperature and DO are identified by optimizing the aquaponics dynamics. The MPC's performance is compared to that of a traditional proportional-integral (PI) controller, utilizing two performance indices: relative absolute deviation (RAD) and mean relative deviation (MRD). The MPC demonstrates a reduction in RAD values for both FT and NFT water parameters by 40%–60%, and MRD values by 8%–43%. These results show that the MPC effectively mitigates disturbances and addresses model mismatches, outperforming the PI controller. Implementing the proposed strategies in aquaponic systems enhances overall performance, boosts food production rates, maximizes profit, and reduces labour.