Our projects

AI models to capture CO2 #Oil&Gas

Optimizing the testing phase of CO2 capture solutions by speeding up simulation and improving performance, using hybrid AI models.

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The development of CO2 capture solutions involves complex experimentation that is often left to the manual activity and experience of an operator. To make this step more effective, hybrid AI models can be applied to speed up the process.

To improve the calibration of the physical model, an integration is done with a custom model in python (hybrid model) that allows generating representative synthetic datasets that can be used to train surrogate models.

Results

– 40% Simulation time
– 12% Calibration errors

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Optimizing wastewater treatment plants #multiutility

Wastewater treatment: we built a controller based on AI algorithms that improves the management of pollutants in water and reduces plant energy consumption.

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Most wastewater treatment plants in Italy lack systems for aeration management, often characterized by feedback or fixed set-point controls. Thus, spikes in pollutants can hardly be prevented, resulting in fines and penalties.

Through Model Predictive Control (MPC), it is possible to control the system by predicting its evolution and adjust oxygen in advance, depending on the expected load.

Results

– 33,7% Ammonia output compared to the previous controller
– 8% Total nitrogen compared with the previous controller
– 16% Energy savings achieved

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