MITIGATING WASTE DISTRESS IN ENERGY-INTENSIVE SECTORS THROUGH ARTIFICIAL INTELLIGENCE
The alarming worldwide and Europe’s current energy market situation is quite clear to everyone. According to a report by Allianz, the invasion of Ukraine will propel Europe’s already high energy prices even higher; indeed, they expect at least a 30% increase in the energy bill for 2022. Moving to environmental aspects, reducing the overall carbon footprint impact is one of the EU’s sustainable goals within 2030. This is the reason why so many companies struggle to find the most suitable solutions to cut costs and reduce CO2 impacts on the Planet.
Here they come sensors, that completely changed how we capture data – offering a proper tool to plan future actions and drive more efficient performances. Indeed, the better the monitoring and measuring data, the better one can understand a company’s data history and potential use cases for reliable future decision-making actions.
For instance, through sensors data, it will be more feasible to determine factories’ total energy use, how much power is consumed by lighting, heating, cooling equipment, and so on. Therefore, keeping data from sensors under control is worthwhile for any manufacturing industry. Disaster detection and Early Warning systems are priceless in such sectors.
They can forecast and detect anomalies that might be harmful, providing time for the response system to prepare for adverse events and lower their impact.
And Artificial intelligence is the most suitable solution here.
Thanks to AI and Machine Learning integration, data analysis has been employed to optimise industrial processes and, as a result, to reduce waste and energy consumption to the bare minimum in energy- intensive sectors such as manufacturing. Hence, energy efficiency can be achieved through regular data stream monitoring.
Here comes Radicalbit’s latest product, Helicon, a platform designed to combine real-time data processing with AI. Employing Artificial Intelligence on industrial devices’ data flow, Helicon can properly provide an Early Warning system to limit the possible negative impacts of irregular production processes.
This method allows saving up to 40% of production energy resources. If the cost-per-unit ratio improves, the process becomes more sustainable. As a result, a company would be less exposed to fluctuations in energy costs. As a concrete example, Helicon has been employed to process IoT data of a chemical factory aiming to detect process anomalies in real-time. The system offers a critical set of information, allowing technical personnel to quickly intervene during the process – mitigating, or even avoiding, any negative impact (energy and raw material waste). Applying AI to real- time IoT data – we could say in a dynamic way – brings prompt advantages: in machines using tools subject to wear Helicon was able to intercept anomalies as they were occurring. This dynamic approach raised the instrument’s life expectancy by 30%, just using tools until the very end of their natural life.
Founded in 2016, Radicalbit is an Italian Deep Tech company, specialised in stream processing and AI solutions.
Radicalbit’s highly innovative product portfolio has been acknowledged by the most relevant international research and consulting companies, such as Garner, Forrester and Zion Research. Considered as a reference point in stream processing technologies R&D (in Academic Education and Business), the Italian company developed products designed to blend Stream Processing and AI.
For more information please visit:
www.radicalbit.io
