The Viridis platform is extensively based on the concepts of the Internet of Things, Big Data, and Industry 4.0, as it is capable of capturing, storing and processing large volumes of data on the consumption of energy and utilities of any operational unit, whether a specific machine or a production process as a whole. These data, combined with information about the context in which they were recorded (production plans, process variables, operational parameters, among others), makes possible, using the techniques of Machine Learning, the construction of predictive models that are combined to create a digital representation of that asset – a digital twin. The large integration capacity of the platform facilitates the capture of relevant data, while its modeling and visualization capabilities make the use, interpretation, and evolution of digital twins easy, resulting in significant value creation for our clients. Click here to learn more about our solutions.  

The Viridis platform is extensively based on the concepts of the Internet of Things, Big Data, and Industry 4.0, as it is capable of capturing, storing and processing large volumes of data on the consumption of energy and utilities of any operational unit, whether a specific machine or a production process as a whole. These data, combined with information about the context in which they were recorded (production plans, process variables, operational parameters, among others), makes possible, using the techniques of Machine Learning, the construction of predictive models that are combined to create a digital representation of that asset – a digital twin. The large integration capacity of the platform facilitates the capture of relevant data, while its modeling and visualization capabilities make the use, interpretation, and evolution of digital twins easy, resulting in significant value creation for our clients.

The Viridis platform explores the concept of a data lake to combine time series sampled in real time with transactional data from management systems. To deal with large volumes of data, the system has a high-performance data historian with efficient storage and response times, capable of holding thousands of time series for many years. All this measurement data is combined with data from legacy systems such as ledger transactions, production orders, maintenance orders, laboratory data, among others. The system automatically correlates multiple types of data, temporal and transactional, allowing disruptive views on energy performance and operational efficiency.