Title: R&D engineer
Ahmed ABID, Ph.D., Research engineer at EGM, located in Sophia Antipolis in South of France.
He earned his Ph.D. in Computer Science from the University of Tours, France, in 2017.
Prior to joining Easy Global Market, he was a researcher teaching assistant in computer science at Tours University for two years. He served also as postdoctoral researcher in IRISA at Rennes 1 University, France. His fields of interest span over several areas related to Internet of Things, data engineering, semantics and data modelling. He has published several research papers in these fields.
Currently at EGM, he has been involved several innovative European and Industrial Projects covering various domains such as the Smart Cities, Smart Farming, Internet of Things…
Aquaculture 4.0: technologies and innovations
Digital Twin of Fish Farms
To meet the world fish demands, more intensive and sustainable farming practices needs to be implemented. Integrating the Digital Twin Model of fish farms will enhance understanding the whole fish organism as an adaptive agent, robust, testable biological theory. Information about fish behaviour, physiology and their environmental conditions can be collected in real-time from a variety of fish farms, which builds the “Descriptive Twin” Layer of the Digital Twin Model.
This data is securely transferred to a digital representation of a real fish.
The digital fish will then send a message back to the monitoring system, which can then either inform the farmer, or make automated changes, this builds the “Predictive Twin” Layer of the Digital Twin Model.
Based on this layer the Digital twin Model offers the opportunity to run scenario-based analysis, called ‘what-if’ analysis. This builds the “Design Twin Model”.
Finally, and based on results of what-if analysis scenarios, the “Perspective Twin” layer will provide recommendations based on multi-criteria analysis.
The Digital Twin Model is mainly exploited to predict voluntary behaviour and food intake of the fish.
This digital twin for feeding will enable running various scenarios and predict the response including unplanned, emergent, and stochastic effects. Such a capacity will provide an indispensable tool for decision support and operational optimization and can be run in the AI-controlled precision fish farm data platform environment.