SILENCE

Our Objectives

SILENCE Project investigates the correlation between different steel manufacturing processes and meteorological conditions causing acoustic emissions by installing acoustic sensors coupled with Artificial Intelligence (AI) tools and Machine Learning (ML).

Moreover, the correlation between foamy slag formation of the EAF adopting coal or its substitute, and noise emission, coal consumption and CO2 emissions will be investigated.

The project is expected to contribute to reduce noise emissions impact on steelwork and surrounding communities improving life quality.

Innovative Content

The innovative aspect of the proposal is that the acoustic pollution caused by steel plants is considered in a predictive manner. The impact of noise on surrounding communities is treated in an integrated way and using modern modelling and ML tools to improve co-existence between the steelworks and surrounding communities. The proposal regards:

an integrated approach to improve acoustic emissions monitoring coupling newly installed sensors and available information from process and surrounding environment. Robust sensors will be developed able to store and transmit acoustic data to be integrated with process parameters and meteorological information.
an extensive application of AI and ML-based tools and techniques for data analytics which are applied for the first time to acoustic emissions monitoring and mitigation in the steel field, thanks to The acquisition of a huge volume of data. Because noise perception of workers inside the plant and on population outside can be significantly affected by weather conditions and obstacles that hinders the development of proper countermeasures to significantly reduce the acoustic impact on. AI and ML analyze the causes of acoustic emissions, enables better process monitoring and control, elaborating strategies for the reduction and prevention of acoustic noise,
the development of decision support concepts and tools to be tested on site and dedicated to plant operators and managers ad suitably designed to allow easy and friendly exploitation of the ML-based tools at a practical level.

Methodologies and Tools

The methodologies and tools developed are demonstrated on two industrial use cases, both equipped with an electric arc furnace and immersed in an urban context with different production processes. These plants are selected to be the noisiest in steelmaking production, and to be located near urban contexts.

The core project idea is:

Management of work force and working conditions (RFCS objective)
New, sustainable, and low carbon steelmaking processes (RFCS objective)
Conservation of resources, protection of the environment and circular economy (RFCS objective)
Decarbonisation and modernisation of steel sector (Green Deal)
Protect people and workers most vulnerable to the transition (Green Deal)