Economically optimal operation of recirculating aquaculture systems under uncertainty

Abstract

This study presents a novel economical operation scheme for recirculating aquaculture systems (RASs). The proposed approach is comprised of a moving horizon estimator and an economic model predictive controller (EMPC), which, respectively, provide the necessary feedback for the EMPC and make economically optimal decisions for the RAS. The scheme is enabled by a mechanistic RAS model, which is coupled with an objective function that jointly optimizes the economics of aquatic biomass growth and utility expenditure. The proposed scheme is tested on a rainbow trout case study, whereby it is compared against the previous state-of-the-art control approach in aerator failure and temperature disturbance scenarios. In both test scenarios, EMPC scheme outperforms the previous state state-of-the-art controller in terms of process economics (up to 41% improvement) as well as shorter batch times (up to 22% shortening). Furthermore, the scheme is shown to be capable of estimating uncertain parameters with little error (generally in the order of 5%) and negligible effect on economic performance when compared to cases with full measurement access and no uncertainty. The proposed scheme provides a new and automated way to ensure RASs operate with maximal productivity, thus further incentivizing sustainability and the uptake of this technology.