Title : Smart fish farming: A simulation toolkit for model-based design and optimization
Abstract:
In recent years, the demand for aquatic foods has been growing rapidly, which demands more efficient fish-farming processes to maximize fish production. Along with this fact, sustainability is an important feature to be considered. Therefore, the improvements in the production process should be aligned with the 2023 Agenda for Sustainability Development, where the sustainable development goals (SDGs) are defined. In this way, a well-known technique for maximizing aquatic food production while reducing the ecological impact is the recirculating aquaculture system (RAS), which has proved to be a promising technique in the last decades. This work aims to automate the feeding and monitoring fish-farm processes. The proposal relies on a process-oriented analysis via statistical and predictive tools to obtain insights based on the fish farm characteristics, namely, building, sensors, actuators, etc., to construct a mathematical model. This model considers the more significant variables making a trade-off between complexity and usefulness. Some key variables considered are dissolved oxygen, ammoniac, pH, water temperature, and biomass. The constructed model is employed to design a simulation toolkit in a wellknown numerical platform to analyze the fish farm process for the necessary resource estimation before the facility's development. This model allows the user to obtain the desired production while optimizing costs and reducing the ecological impact of the activities. The toolkit is developed considering a couple of case studies, specifically, the species considered are Tilapia and Salmon. Future work includes the model validation of the constructed toolkit by employing real-time data from the fish farm prototype and control system implementation.
