Optimizing alkalinity control in Recirculating Aquaculture Systems (RAS): a dynamic modelling approach

Abstract

Maintaining good water quality is essential for successful fish production in land-based recirculating aquaculture systems (RAS). Numerous interdependent factors influence water quality parameters, making it difficult to evaluate which operational strategies are most favorable. Mathematical models and model simulations have proven to be powerful tools to evaluate how RAS design and operation are linked to RAS dynamics, but these models rarely implement pH and carbonate species as dynamic variables. Here, we present a dynamic model for RAS (dynRAS) that combines rates of TAN (total ammonia nitrogen) removal, fish growth, and CO2 and TAN excretion, with a reaction model for pH and the carbonate system formulated based on the law of mass action. A novel aspect of our approach is the incorporation of a dosage system modelled by a Hill-function, enabling the exploration of diverse dosing strategies for pH and alkalinity management. The model was validated based on empirical data from a pilot scale RAS system operated with a feeding regime involving 12 hours of feeding per day. We found that model simulations could be used to accurately predict diurnal cycling patterns in RAS water quality parameters. Furthermore, we made use of simulations to assess how diurnal cycling varies with changing pH and alkalinity levels. Model results emphasize the complexity of pH and alkalinity control in RAS in relation to overall water quality management. Based on our simulations, we argue that what should be considered as optimal pH and alkalinity in RAS depends on the state of the system. Accordingly, optimal pH and alkalinity thresholds may vary between different RAS units and between different time points of a rearing cycle. More generally, we demonstrate how modelling and model simulations can be an effective way of getting insights into the dynamics of complex RAS interactions and provide a valuable tool to efficiently explore effects of different operational strategies on water quality parameters.