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Metal industries including aluminum industry are the primary growth/regression indicators for any country. Therefore, the outdated metals sector cannot adequately support the growing economy. The disruptive forces in the aluminum industry make it imperative for companies to reinvent themselves to meet social and economic standards changing. Advances in digital technology are disrupting traditional business models and redefining the way they should be done Businesses in order to compete and survive in the future.
Global aluminum producers currently face challenges from a volatile economy, environmental concerns, and stricter safety and carbon regulations. The industry faces volatility due to its energy-intensive nature. While external forces cannot be controlled by industry, the key to improving efficiency and productivity The smelter lies in the intelligent use of the data and information available within the smelter. This can improve cost, quality and customer satisfaction while providing better protection against external factors . By introducing digital technologies and other elements of Industry 4.0 in the factory, the product can record A huge leap in productivity while providing better control over the production process and access to customers. At the same time, this will provide better safety and environmental sustainability. Leveraging digital technology and associated technologies is the only way for producers to maintain their competitive edge in a world The digital manufacturing of the future.
industry elements
4.0 in the aluminum industry
Although the industry is rife with discussions about digitization processes and terms such as Industry 4.0 and advanced control In operations, big data, etc., however, these terms do not always have a clear definition And a distinction in the broader industrial value chain such as aluminum covering alumina refining to aluminum smelting and various processes downstream and recycling. The meaning of digitization and automation is shared in such an environment.
The main elements of Industry 4.0 being deployed in the aluminum sector are:
Digital Production Process and Control With digitization, the entire production process of an aluminum plant is built around a computer system So that the process can be monitored, analyzed and improved effectively. It includes both hardware and software systems to extract data from processes and assets in order to facilitate the smooth running of operations maintenance, improve fault diagnosis and improve performance.
Automation and robotics
Many critical processes in the aluminum smelter, which were previously manually operated in a high-risk area The high temperature is now automated using automation technology and robots and can be carried out without human intervention. Some of these processes pour hot metal into the furnace in the casting house, and take measurements in pots and furnaces, which not only provide safety, but can also provide better maintenance of temperature and working speed and casting quality. Robots are also used to build ingot packages. Automated Guided Vehicles (AGV) are now an integral part of ceramics and casting chambers.
Big data
Big Data is a field that teaches how to extract and analyze information from large and complex data sets. It also includes the search, sharing, transfer, visualization, updating, and privacy of information and data source. Uses predictive analytics, user behavior analytics, or some other advanced data analysis methods on data in Real time derived from aluminum plant. Digital image analysis helps in better control of operations. They are then used in operations management and in managing customers and suppliers.
Machine learning and artificial intelligence
Machine learning and artificial intelligence are two related concepts. Machine learning allows machines to access data and learn on their own to operate independently. Artificial intelligence is a larger concept that allows a computer program to act as intelligently as the human brain. Artificial intelligence is at an early stage while machine learning is being used in the aluminum smelting process. Machine learning can understand the noise pattern of the reduction vessel to control energy consumption. It can also estimate the concentration of alumina in the cell and proactively determine the effects of the anode.