GE Develops Aluminium Smelter’s Digital Twins
GE Digital has announced the availability of its newest Process Analytics solution, the Digital Smelter. The software creates a Digital Twin of the
GE Digital has announced the availability of its newest Process Analytics solution, the Digital Smelter. The software creates a Digital Twin of the aluminium smelting process to deliver insights and prescriptive guidance to safely maximize production, reduce raw material costs and optimise energy consumption. This supports the digital transformation strategy of aluminum smelters in the Middle East region, which accounts for 8 per cent of aluminum production globally. The new Digital Smelter can support the region’s aluminum industry to gain higher levels of productivity as well as preventive insights to enhance the uptime of the plants. The solution strengthens GE Digital’s footprint in the metals and mining sector.
Digital Smelter delivers a complete analysis of the components within a pot as well as each pot line, providing a Digital Twin to help increase the efficiency of the smelting process. The software also helps operators predict potential process and equipment anomalies and prescribe the most effective decisions. These capabilities help to keep operations safe and production optimal while reducing operator error.
The flagship advantage of Digital Smelter is thorough prediction of pot leaks, which leads to the reduction of unplanned stoppages and optimizes pot health. Providing alerts for anomalies such as pot instability and undesirable events such as anode spikes help optimize energy consumption and keep the process reliable. Digital Smelter can also improve the quality of aluminium and reduce raw material consumption by modelling the proper amount of aluminium fluoride to use in each batch.
Executives and management can easily access dashboards showing the productivity, efficiency, and health of each potline or all lines across the organization. With the ability to benchmark performance across potlines, poor performing lines can be quickly identified and operators can review heat maps, performance trends, and pot health indicators to make decisions to restore the line’s performance and create more value for the business. And, the Digital Twin can then prescribe the best action to help ensure the plant doesn’t shut down over costly equipment failures or EHS events, which could lead to fines and additional regulatory requirements.
Digital Twins are learning, living models that combine domain knowledge and physics with industrial AI. Companies use this technology to detect, prevent, and predict critical issues in order to uncover insights and actions that drive business value.