The project, which includes machine learning and is framed by the initiatives of Industry 4.0, is focused on sustainability by being able to predict the number of emissions into the environment.
“This project is focused on operational sustainability with the objective of operating with a better environmental efficiency, maximizing productivity.” This is how the Automation and Control Manager from Ternium Brazil, Flavio Silva, describes the Machine Learning project, which is focused on promoting the emission reduction in the sintering smokestack.
This effort falls within Industry 4.0 and works by predicting, 30 minutes before, the result of the hourly average of particle emissions in the smokestack of primary sintering.
This way, the operator can anticipate and receive guidelines to reduce or increase the production to guarantee an appropriate environmental control of the emissions. According to the environmental license of Ternium Brazil, emissions of particulate matter cannot be above the 50mg/Nm³ limit. For Cristiane Galiazzi, Sintering Operational Coordinator, “the guidelines being implemented in this model help the operator make decisions in pursuit of a better operational and environmental efficiency.”
The Sintering Emission Prevision Model is based on machine learning algorithms for the prevision of temporary series, Big Data technologies and automation tools developed by Ternium. The project was implemented in October of 2020 and involves the work of the automation teams in Brazil, Mexico, and Argentina.
Fernando Monti, New Automation Technologies Manager in Ternium Mexico, who participated in the process, explained the project: “It’s like an additional instrument that says: ‘Look, the next 30 minutes, your emissions will be at this level, if the level is higher than the limit, you may reduce the production. If the level is lower than the limit, you may produce more."
Monti considers this project is a complement of the Sintering Electrostatic Precipitator, which contributes to the emission control even more. “This project is multi-disciplinary, multi-national, and multi-company since it was developed with Ternium personnel as well as from other companies. The diversity of the team enriched the development of the project,” states Alejandro Zambrano, New Automation Technologies Data Science Coordinator.
Operators in the Indicator Control Room.
Right into the Control Room
The data is directly sent to the Control Room. Leandro Bento is one of the operators who control the emission prevision system data. He understands the importance of the project for the environment: “Everyone here knows the importance of operating our plant within the limits established by the environmental license. Anything that represents an improvement of the results and an emission reduction is always welcome.”
“The aim of this project is the environment, but it also influences productivity.”
This project also involved Giuleano Gonçalves, Leticia Piccin, and Americo Almeida from the Automation Department in Brazil; Rafael Okamota and Juan Yannaduoni from the Environmental Department; and Gustavo Rosa, from Sintering Plants Operation.
How Does it Work?
The project foresees in 30 minutes the hourly average of the emissions, which cannot be above 50mg/Nm³, according to the established regulation in the Environmental License of Ternium.
For example, at 16:30, the model predicts an hourly average of emissions by 17:00. If the average is above the limit, the operator receives the necessary guidelines to apply the reduction measures immediately. If the average emission is within the limit, the operator evaluates the possibility of increasing the production.
For Pamela Reis, Environmental Manager from Ternium Brazil, this is a pioneering and revolutionary project that is here to contribute even more to the environmental control of the company operations.
“I’m so proud of what all the teams are building, we are changing the culture and improving environmental efficiency at an incredible speed. With this project, we are demonstrating that the value of the environmental care is what guarantees a safe and sustainable operation”, she concluded.