Steel manufacturing facilities are characterized by being asset intensive, high costs for equipment replacement and inundated with legacy processes. Predictive analytics is the key to improving these facilities efficiency and return on investment. More emphasis is being placed on these plants being digitally transformed and brought to speed with the rest of industry 4.0. SORBA IoT provides business value solutions automating the systems that connect all entities in the steel industry sector allowing them to interact in real-time.
SORBA Historian collects data from sensors and equipment used in the production of energy. SORBA machine learning detects any abnormal changes in consumption that can detect variability ensuring the use of power inefficient equipment is minimized. SORBA produces actionable insight from a sensor at different time intervals. SORBA advanced control can then use this data to control production measures in real-time and eventually reduce operating costs.
SORBA can track and reduce unplanned downtime with all the given steel production data being generated exponentially. Operators can anticipate equipment failures with a high degree of accuracy and schedule a maintenance work order to optimize power generation efficiency.
SORBA machine learning can also monitor asset health using machine learning agents that can track variables such as conditions of the asset, weather, failure frequency etc. SORBOTS (SORBA machine learning agents) utilize analytical techniques such as logistic regression, neural networks etc.
SORBA is an IoT solution that is simple to deploy, complete in its offerings and enterprise in its scale.
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SORBA IoT conducted a pilot for a steel manufacturing plant that produces construction grade rebar. 24 hours a day, 7 days a week, scrap metal is converted into steel. Each year the plant produces 1.1 billion pounds of steel rebar used in concrete. That’s enough rebar to circle the globe. Read more about this use case HERE.