- Innovation
- Artificial Intelligence
The AI4ESOPP project develops an AI-based platform for the sustainable management of industrial plants. It collects data from machinery, IoT sensors, and external sources, providing advanced analytics and support for environmental certifications (LEED, ISO 50001). It includes modules to optimize consumption, maintenance, and environmental impact, featuring an LLM interface to simplify usage. The project contributes to the Blue Economy and the development of less advantaged regions, promoting innovation and sustainability.
The Artificial Intelligence for Environment and Sustainability of Production Plants (AI4ESOPP) project aims to develop an artificial intelligence (AI)-based platform dedicated to the management, efficiency improvement, and environmental sustainability of industrial plants.
The platform is distinguished by several innovative features: on the one hand, its ability to collect and organize all data related to one or more industrial plants, whether from modern machines, such as those involved in Industry 4.0 projects or new equipment equipped with Digital Twin technology—or from older machines, thanks to the processing of heterogeneous data streams and their real-time interpretation through AI modules dedicated, for example, to the recognition of objects in transit. This makes it possible to create a dashboard where all plant data (or data from multiple plants, whether on similar or different sites) is easily accessible.
This data will then be enriched with information from external sources, such as weather forecasts or environmental readings, in order to provide a dashboard containing all relevant information.
Data, as we know, is the fuel for AI: this wealth of information—from new and legacy plants, from distributed sensors, and from external sources—will be used to refine AI models for optimization and forecasting. As an example, for a plant equipped with photovoltaic panels, the platform could suggest planning production based on upcoming sunny days, enabling both cost savings and environmental protection.
Environmental impact is one of the platform’s key strengths. It incorporates information related to major environmental certifications, such as LEED, ISO 50001, ISO 14001, and EMAS, and assists management in achieving and maintaining these certifications. This data-driven approach supports the transition toward an ESG (Environmental, Social, Governance) management model for the entire plant. The vast amount of data and information generated can be challenging to handle; to address this, a Large Language Model (LLM), similar to ChatGPT, will be used to help users interpret the data and plan strategies to reduce costs and improve overall environmental performance. Moreover, by integrating information from both next-generation and older machines, the platform will provide clear insights into modernization strategies for individual machines, taking into account both profitability and environmental impact.
Overall, the platform brings together several innovative features: the ability to manage even heterogeneous information about industrial plants; the ability to use this information to power distinct AI modules that support all key phases of Production Plants (IdP); a strong environmental focus, emphasizing its ability to help companies achieve and maintain major environmental impact certifications; and a Generative AI powered by an LLM that assists and guides the user, enabling intuitive interaction with the platform itself. As is often the case, the value of a system made up of multiple components exceeds the sum of the individual parts. The strength and innovativeness of this platform go beyond the sum of innovations found in each of its components.
The platform can be seen as the sum of five conceptually distinct modules:
- A data collection and integration module that gathers, aggregates, and makes accessible all available data: information from the company’s IT systems, data from distributed sensors throughout the plant, data from machinery (Industry 4.0), and data from external sources such as weather forecasts.
- A data analysis module, including real-time dashboards, which allows—depending on the type of production plant—for:
- a. Analysis of the plant’s energy consumption
- b. Analysis of the plant's performance
- c. Analysis of water consumption, leak prevention, reuse, and recycling
- d. Management and valorization of wastewater
- e. Anomaly monitoring
- A module, based on AI, focused on forecasting and planning; AI will be used to identify inefficiencies, critical points, and improvement opportunities within the production cycle. A key component of the project will be the adoption of deep learning techniques and reinforcement learning techniques. The former will be used to analyze large volumes of data (Big Data) from sensors and monitoring systems, enabling a deeper understanding of underlying patterns and dynamics. The latter will be used to develop models that can continuously learn and adapt to the changing dynamics of the production environment. This will allow for dynamic process management, enabling autonomous adaptation to changing production conditions and new information acquired over time. For example, AI will be used for:
- a. Forecasting production costs (for example, using weather forecasts to estimate the performance of photovoltaic panels or to estimate the temperature of the water used for plant cooling), planning, and scheduling
- b. Planning maintenance activities also based on expected or estimated workloads
- c. Tuning machine parameters to optimize production processes
- d. Predictive maintenance
- An advanced reporting module with data export functionalities, aimed at supporting the company in the systematic, objective, and periodic evaluation of its environmental impact for the purposes of: ESG (Environmental, Social, and Governance) assessments, and obtaining and maintaining environmental impact certifications such as LEED, ISO 50001, ISO 14001, and EMAS, by generating data in the formats required by these certifications.
- A module based on an LLM-type AI, designed to support management in querying and managing the platform, enabling dialogue with it and simplifying the user experience. For example, the module can explain the generated reports in natural language, also providing regulatory references and related certifications.