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CASE HISTORY

Digital Twin and AI for energy efficiency at Acquedotto Pugliese

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Lutech Group, together with the Department of Electrical and Information Engineering at the Polytechnic University of Bari, has signed a two-year research and experimentation agreement with Acquedotto Pugliese as part of the Puglia FESR 2014 – 2020 Operational Program. This agreement aims to create a Digital Twin, developed through process modeling and digitization, for the largest water lifting plant in Europe, the Parco del Marchese, located in the municipality of Laterza.

Due to the highly energy-intensive nature of a lifting plant, strategies aimed at maximizing energy efficiency are essential. The digital twin serves as an analytical, simulation, and planning tool to enhance productivity, reduce innovation risks and production costs, and decrease emissions and carbon footprint.

The Parco del Marchese project will enable more efficient energy management of the facility and further strengthen the plant's security. This facility is the true operational core of the Acquedotto Pugliese network, capable of lifting up to 7,000 liters of water per second and distributing it across the provinces of Brindisi and Taranto, and partially in the Terra di Bari area.

To this end, Lutech is collaborating with the Water Engineering Group for the optimal planning of the plant and its operations, also proceeding with the installation of intelligent IoT sensors to enable the collection and analysis of plant data and to feed Artificial Intelligence models. This allows for the continuous and real-time development of predictive and prescriptive models, enabling a simulation environment.

The project also includes the creation of a Business Intelligence training module to support strategic and operational decision-making related to maintenance scheduling and the reduction of energy consumption in pumping and purification plants within water supply systems.

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The Digital Twin supporting plant management enables three key use cases

Measurement, simulation, and data collection are crucial factors for a project that starts with parameterizing energy performance and will be pivotal for energy optimization of the plant.

Simulation

Modeling and creating the Digital Twin will enable scenario simulations, facilitating the development of models and plans for preventive maintenance, what-if analyses, and customer information. Leveraging Augmented Reality applications, the project allows for digital simulation of the plant, enabling remote visualization and inspection.

Prescriptive Operations

Utilizing collected data, optimal energy configurations are suggested, starting from measuring components and their deterioration.

Predictive Analysis

Field interventions will be simulated to apply anomaly detection techniques to mechanical components, impacting both downtime and energy performance. This recommends efficient and economically sustainable preventive component replacements.

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