Introduction:
The aquaculture industry is rapidly growing and has become a crucial part of global food production. Land-based recirculating aquaculture systems (RAS) are increasingly important due to their efficient water use, stable conditions, solids removal, and effluent treatment (Spiliotopoulou et al., 2018). Efficient management and optimization of RAS protocols are essential to achieve optimal growth outcomes (Seginer 2016). Optimizing various factors that impact the process, including feeding, heating, and oxidation, is necessary to achieve optimal growth. However, most existing RAS management methods are based on pre-determined solutions that may not be optimal for specific cases. Furthermore, existing solutions are yet to be applicable, as they fail to incorporate a proper description of the processes in the system such as water quality and fish growth processes (Chahid et al., 2021).
Materials and Methods:
In this study, I propose a novel decision support system (DSS) that provides optimal decisions regarding the feeding rate, temperature, and oxygen for maximizing profits in RAS. The proposed method utilizes a simulation-optimization framework that ensures reliable application in real-time (Jamal et al., 2018). A holistic simulation is developed by integrating fish growth simulations as well as water quality simulations. The fish growth simulations are based on bioenergetics models in which the fish growth is estimated by the metabolism rate with time as a function of current weight and the water quality of un-ionized ammonia, temperature, and dissolved oxygen (Ursen 1967). The water quality is simulated to describe the dynamics of the water quality with time while accounting for the impacts of the feeding, temperature, pH and bio- filtration. The relation between these factors and the water quality factors are well-known in the literature (Ebeling and Timmons, 2010). The coupled simulations of fish growth and water quality are used as a simulation for the processes that occur in RAS. The accuracy of the simulation is enhanced through a closed loop based on observations for oxygen, temperature, ammonia and weight . The simulation is coupled with the optimization algorithm (e.g. genetic algorithm) to estimate the value of several decisions (several levels of feeding rate, tempera ture and oxygen) within a simulated forecast period to the future. The decision that comes with the optimal expected profit in the forecast period is the optimal decision which is applied for the current time step. The process is repeated on a daily basis. Overall, this approach provides a reliable and customized solution for RAS management, enabling efficient decision-making to maximize profits. Figure 1 describes the overall framework.
Case Study :
A case study of Tilapia for feeding optimization for maximizing profits was conducted. The oxygen and temperatures were not optimized and remained constant throughout the growing cycle. The optimized results were compared to a conventional feeding schedule based on proportions (between 1% to 5%) of the current weight of the fish. The bioenergetics model parameters were used from Yi (1998).
Details:
Tank volume: 6 cubic meter; Number of fishes: 3000; Initial weight: 7 gr; Temperature: 28 C; Fish price: 1.5 $/kg; Oxygen price: 0.016 $/kg; Oxygen level: 0.6 mg/l; Bio-filter surface a rea: 500 ; pH level: 7.
Results:
Although lower revenue was obtained using the optimization method, the profits (the objective) was higher due to the saving in the feeding.
References:
Spiliotopoulou , A., Rojas-Tirado, P., Chhetri , R. K., Kaarsholm , K. M., Martin, R., Pedersen, P. B., ... & Andersen, H. R. (2018). Ozonation control and effects of ozone on water quality in recirculating aquaculture systems. Water research, 133, 289-298.
Seginer, I. (2016). Growth models of gilthead sea bream (Sparus aurata L.) for aquaculture: A review. Aquacultural Engineering, 70, 15-32.
Chahid , A., N’Doye , I., Majoris , J. E., Berumen , M. L., & Laleg-Kirati, T. M. (2021). Model predictive control paradigms for fish growth reference tracking in precision aquaculture. Journal of Process Control, 105, 160-168.
Jamal, A., Linker, R., & Housh, M. (2018). Comparison of various stochastic approaches for irrigation scheduling using seasonal climate forecasts. Journal of Water Resources Planning and Management, 144 (7), 04018028.
Ursin, E. (1967). A mathematical model of some aspects of fish growth, respiration, and mortality. Journal of the Fisheries Board of Canada, 24(11), 2355-2453.
Ebeling, J. M., & Timmons, M. B. (2010). Recirculating aquaculture . Ithaca, NY, USA: Cayuga Aqua Ventures.
Yi, Y. (1998). A bioenergetics growth model for Nile tilapia (Oreochromis niloticus) based on limiting nutrients and fish standing crop in fertilized ponds. Aquacultural Engineering, 18(3), 157-173.