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A Web-Based Platform for Efficient and Robust Simulation of Aquaculture Systems using Integrated Intelligent Agents


We propose a web-based platform of integrated intelligent agents that incorporates multiple Functional Mock-up Units (FMUs) and surrogate modelling techniques. In this platform, each FMU envelops a stand-alone simulation component that represents an aquaculture system, such as the fish growth model, water quality model, and fish behavior model. Some FMUs may be computationally expensive to simulate or have different time step intervals, making integration with other FMUs difficult. To address these challenges, we employed surrogate models to substitute the more computationally expensive models. In this work, surrogate models are trained using simulation data and selected based on robustness analysis to ensure the overall system input-output reliability.
The platform also includes a Chatbot component utilizing natural language processing and decision-making techniques to interpret user requests and provide tailored FMU configurations, enhancing the user simulation experience. Overall, the proposed platform provides a comprehensive and efficient approach to modelling and simulating complex systems such as fish farms. Robustness analysis ensures the platform’s accuracy and reliability, while the user-friendly interface enables easy tailored experimentation. By providing a framework for exploring the potential of aquaculture as a key source of food and income, the proposed platform represents a valuable interactive simulation tool for researchers, policymakers, and industry professionals seeking to improve the sustainability, efficiency, and economic viability of the aquaculture industry.


Academic article


  • EC/H2020 / 871108




  • SINTEF Ocean / Aquaculture



Published in

Procedia Computer Science






4560 - 4569

View this publication at Cristin