A recirculating aquaculture system with a data-driven control strategy that improves the growth rate of cultured species (ex: shrimp, tilapia), minimizes required feed, reduces water consumption by improving waste removal from recirculating water, and provides robustness against uncertainty and disturbances. The system uses low-cost and readily available sensors to obtain estimates of concentrations of hard to measure target parameters such as ammonia, nitrate, nitrite, chemical oxygen demand, and phosphate). It is controlled in real-time using data-driven (including machine learning) algorithms.
In addition to the main culture tank, the system houses bioreactors for nitrification and denitrification in the waste removal process. The type of reactor varies but may include a mixed bed biofilm reactor (MBBR) and an anaerobic suspend growth reactor or sequencing batch reactor (SBR). These reactors are equipped with sensors and dosing pumps to cultivate a monitored biomass (biofloc). An intelligent control strategy is constructed from measurement data to optimize the waste removal ability of the biomass and to track reference setpoints which can be sized appropriately to feed plants in an aquaponics or hydroponics operation. The purpose of this design is to create a semi-automated method to continuously monitor and control aquaculture systems for maximum food production.
