Real-time ammonia estimation in recirculating aquaculture systems: A data assimilation approach

Abstract

Recirculating aquaculture systems offer sustainable fish production but face challenges related to ammonia levels. Ammonia, particularly the un-ionized fraction, NH3, can negatively impact fish growth and health. Traditional ammonia estimation methods, including offline analyses and affordable, yet inaccurate, in-situ measurements, lack the ability to provide reliable real-time insights. Data assimilation combines simulation models and in-situ measurements to provide more accurate estimations. In this study, we demonstrate a novel approach using data assimilation to enhance real-time ammonia estimation in RAS. First, ammonia dynamics are described by forming equations that constitute a simulation model, based on the feeding amounts and the biofilter removal rate parameters. Then, an extended Kalman filter is presented and customized to integrate the simulation model and total ammonia nitrogen measurements for estimating NH3, NH4+, and biofilter parameters. We validated our method through synthetic and laboratory case studies and demonstrated its superior estimation capability as compared to in situ measurements or simulation models. Furthermore, improved ammonia estimation led to improved current and future fish weight estimations, which can be essential for reliable RAS management. The proposed approach facilitates wider adoption of DA in challenging estimations in aquaculture.