How can AI in manufacturing help in aluminium production optimization?
The company sought to reduce energy cost by optimizing smelter operating parameters.
One of India’s largest integrated aluminium players was running its smelting operations on a smelter control technology, which was more than 40 years old. The age of the legacy control system and disconnected nature of the various Operating Technology (OT) and Information Technology (IT) platforms prevented a 360-degree visibility into existing processes and with desired granularity.
The decision making when it came to operations was largely driven by empirical rules and was not data driven. These empirical rules were not revised regularly.
The above resulted in large variations in energy consumption, production efficiency and process stability both within and across production lines. There was inadequate root cause analysis of events and the focus was primarily on post-facto analysis compared timely preventive and corrective action.
In order to arrest these inefficiencies, the company wanted to leverage data and analytics to transform their smelter operations. One of the specific areas in which it was looking to improve using data driven decision making was specific energy consumption of the primary aluminium production process.
The primary aluminium production is a closed loop electrolytic process (Hall-Heroult) and energy constitutes 60% of the overall production cost. The company wanted to explore analytics driven way of dynamically determining smelter operating parameters to maximize energy efficiency without sacrificing quality or productivity.