The project

The remote control project was created in 2013 to remotely monitor plants and to integrate and rationalise the different remote control systems belonging to a single system. The objective is to unite, by guaranteeing the highest technological levels, the remote alarm, telemetry, supervision and remote management of the aqueduct and treatment facilities. In the event of an alert, the remote alarm system allows making a voice call to the available personnel or sending an SMS or an email to the manager during working hours.

Telemetry has two main objectives:

  • provide and make “instantaneous” data relating to the flow rates, pressures and energy consumption of individual systems, municipalities and aqueducts ready for use;
  • collect monthly production data automatically, to feed the DIM (plant data warehouse), thus reducing the manual entries made by technicians and the number of manual readings carried out by operators.

615

devices

The central system currently directly queries, via GPRS connection, more than 615 devices, including remote aqueduct stations, sewer lift control units, energy meters, flow meters and pressure meters.

585

Acqueducts

To date, 585 aqueduct systems out of a total of 680 are remotely controlled. Future planning foresees the completion of remote control throughout the Metropolitan City of Milan.

147

Water houses

The remote control system detects the operation of as many as 147 of the 175 Water houses controlled by the CAP Group

338

Sewage pumping stations

Out of 359 managed, 338 sewer system stations are detected by our systems.

37

Treatment

37 out of 40 waste water treatment plants are currently remotely controlled.

The electronic detective

In order to prevent the risk of illegal discharges of pollutants, we have introduced “electronic detectives”: early warning control systems that allow the early detection of the presence of pollutants in sewage using the technology developed by Kando.

Detectives electronic is an AI (Artificial Intelligence) system based on a complex database and specific dynamic learning methods of the measured values, able to detect events of possible polluting impact and send an activation signal to automatic samplers positioned on the sewer network.

The project involves the installation of probes able to analyse different parameters along the entire sewer network. The analysis of each parameter identifies a specific pollution index that, through the application’s correlation and self-learning, makes it possible to immediately identify pollution incidents and proceed promptly with any interventions.

The initial phase of the project involved four months of testing and detected more than 20 events of possible polluting impact with subsequent sampling which, once analysed by the accredited laboratory Pero, made it possible to verify the effective intervention capabilities of the AI system.