{"id":2358,"date":"2026-01-21T13:22:06","date_gmt":"2026-01-21T12:22:06","guid":{"rendered":"https:\/\/www.pmten.it\/?page_id=2358"},"modified":"2026-02-25T09:19:26","modified_gmt":"2026-02-25T08:19:26","slug":"digitalplant","status":"publish","type":"page","link":"https:\/\/www.pmten.it\/en\/digitalplant\/","title":{"rendered":"DigitalPlant"},"content":{"rendered":"\n
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DigitalPlant<\/h1>\n\n\n\n

An advanced tool for integrated air\u2011quality management<\/strong><\/h2>\n\n\n\n
DigitalPlant<\/strong> is an innovative software platform that implements an integrated methodology combining sensors and numerical models<\/strong> for air\u2011quality assessment and the management of emission sources.\u00a0

DigitalPlant merges environmental monitoring with forecasting models and machine\u2011learning techniques<\/strong> to provide an advanced decision\u2011support system able to identify and prevent environmental issues in real time, both in the short and medium term.\u00a0

Its main application areas include urban environments, industrial facilities, and natural or environmental areas.<\/div>\n<\/div>\n\n\n\n
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The platform enables:<\/div>\n\n\n\n
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Real\u2011time data visualization and statistical analysis of historical series\u00a0<\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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The platform can ingest data from any environmental monitoring system and provides fast, effective access to the most relevant information.<\/div>\n<\/div><\/div><\/div>\n\n\n\n
Configuration of multi\u2011parameter and multi\u2011site alert thresholds with automatic warning generation\u00a0<\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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The platform automatically detects and signals relevant events based on standard and\/or custom configurations.<\/div>\n<\/div><\/div><\/div>\n\n\n\n
Identification of emission sources through back\u2011trajectory models\u00a0<\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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When a critical event is detected, numerical\u2011model modules are immediately activated. The first step involves retrospective simulations that reconstruct air\u2011mass dynamics to trace the likely origin of observed pollutants.<\/div>\n<\/div><\/div><\/div>\n\n\n\n
Assessment of short\u2011 and medium\u2011term potential impacts to enable timely corrective actions\u00a0<\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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Forecasting models, calibrated through machine\u2011learning algorithms, provide broad and reliable information useful for designing and targeting appropriate mitigation measures.<\/div>\n<\/div><\/div><\/div>\n<\/div>\n<\/div>\n <\/div><\/div>\n<\/div>\n <\/div><\/section>\n\n\n\n
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Key Features<\/h2>\n\n\n\n
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Real\u2011time monitoring<\/strong><\/div><\/span><\/span><\/span><\/div>
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DigitalPlant includes a dashboard for real\u2011time data consultation, offering a compact overview that enables a complete and rapid assessment of the context.<\/p>\n<\/div><\/div><\/div>\n\n\n\n

Compatibility with all continuous\u2011measurement sensors and devices<\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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The platform can assimilate data from existing networks composed of different device types. If needed, our team can provide full support in selecting devices and configuring the monitoring network.<\/p>\n<\/div><\/div><\/div>\n\n\n\n

Visualization and analysis of observed data<\/strong><\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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The platform has been developed by leveraging years of experience in environmental consulting. Collected data undergo preliminary statistical processing and are presented to users clearly and effectively.<\/p>\n<\/div><\/div><\/div>\n\n\n\n

Automatic detection of \u201ccritical events\u201d<\/strong><\/strong><\/strong><\/strong><\/div><\/span><\/span><\/span><\/div>
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Collected data are continuously processed to identify unusual or anomalous conditions in the monitored context. DigitalPlant interprets the concept of thresholds through an innovative approach:<\/p>\n\n\n\n