Industry 4.0 is currently one of the most talked about topics in the aluminium industry, with elements such as automation, robotics, Industrial Internet of Things (IIoT), Machine Learning (ML) and Artificial intelligence (AI) leading the way towards the future of aluminium manufacturing.
Globally the aluminium industry has now started keeping sustainability at the top of the agenda which includes Net Zero, Decarbonisation & Circularity. Based on a study published in the scientific journal Nature Communications in 2020, AI could help achieve as much as 79% of Sustainable Development Goals. It has already proven itself in several other industries and is now becoming an important contributor to solving several issues that are critical for sustainable metal production.
However, in the aluminium industry, which comprises alumina refining to aluminium smelting and various downstream processes and recycling, the concerns and challenges of digitisation and automation are slightly different. Use of Industry 4.0 technologies is not only complementary to, but necessary for achieving sustainable production. Without an Industry 4.0 approach, it will simply not be possible to deliver these long-term goals with existing technology and control practices alone.
Although many companies are either still gathering information on digitisation or in the process of developing a strategy, leading players in the industry are already deploying Industry 4.0 applications in production, R&D, distribution, logistics, and supply chain. In addition to this, state-of-the-art technologies are applied in production control, networking of machines, and production processes. Simultaneously, companies link up with partners on mobile devices and through cloud computing.
Industry 4.0 helps in improving productivity, efficiency, safety, resource utilisation, increasing machines’uptime and reducing breakdowns by using smart sensors and such technologies. Also, it enables self-diagnosis, reducing turnover time, preventing breakdowns, reducing human intervention which will reduce human errors.
The digital production process and control system in an aluminium plant revolves around computer systems so that the whole process can be monitored, analysed, and optimised effectively in real-time. It also includes facilitating the smooth running of operations, maintenance, and better error diagnosis.
Automation and robotics technologies have gone to a level where day-to-day tasks can now be performed in a high-risk, high-temperature zone with zero human intervention. On the one hand, the manufacturing units widely using robotics in constructing ingots bundles; on the other, Automated Guided Vehicles (AGV) are now a part of pot rooms and cast houses, eliminating any potential for human interaction with unsafe material handling, thereby reducing the risk of injury to its operators. Robotic cranes assist in the stacking of the refined metal bundles and applying labels to them.
IIoT uses intelligent sensors and actuators to enhance the overall manufacturing and industrial processes. It comprises of an ecosystem of connected machines, equipment, devices and physical objects that can communicate with each other. Thus, all the data from the shop floor, production units, and so on, can be captured on single location dashboards in real time.
Data Analytics, which is an integral part of Industry 4.0, is also proving handy in enhancing maintenance and operational processes. Historical data to forecast equipment failures and process deviations improves uptime and allows proactive maintenance rather than reactive which in turn enables to achieve a high level of operational production.
Finally, is AI, which allows a computer program to act smartly like a human brain. Still, in an infant stage, AI is all set to transform the aluminium sector by keeping a tab on energy consumption, plan downtime, increase productivity, and drive quality.
In alumina manufacturing through bauxite refining, Industry 4.0 enables debottlenecking the plant capacity and improves equipment availability. The use of state-of-the-art numerical modelling and computer aided simulation are becoming popular tools to resolve issues related to plant capacity enhancement and eliminate existing issues. Bauxite ore is ground in crushers and ball mill through wet grinding. The throughput of ball mill is often restricted by grinding media distribution, size, type of material, liner selection etc. which directly influence the specific impact energy between the particles and grinding media. Modelling tools like Discrete element method (DEM) coupled with computational fluid dynamics (CFD) can be utilised for troubleshooting these type of problem areas. Slurry pump throughput issues, desired particle sizes and pushing the ball mill limit are some of areas of improvement in grinding circuit of refinery.
Use of physics based modelling and suitable computational tools has been proven quite useful for improving efficiency of settling tanks, optimizing the operating parameters of the circuit.
Improving the production of Alumina from green liquor with reduced soda content has been one of the major KPIs of the alumina manufacturing industry. Using Population balance modelling coupled with growth kinetics and CFD simulation can provide insights on the mixing hydrodynamics which in turn helps understanding the existing scenario on residence time distribution, temperature uniformity and many other key parameters in achieving the goal of higher alumina throughput.
Calcination of alumina crystals which reduces the moisture content causes erosion inside the calciner chamber due to non-uniform flow distribution. Formation of soot inside the combustion chamber due to uneven temperature distribution has been an incipient problem for maintaining the refinery product quality. With the transformation in computational capability across the world intensive combustion studies along with particle-laden fluid flow simulation has been used by several companies for tackling these issues and is becoming popular in Industrial R&Ds across the world.
Industry 4.0 is well accepted among the metal manufacturing industries and is being implemented to leverage its essence. Many key players like Rio Tinto, Sohar Aluminium, Alcoa, Vedanta, HINDALCO groups have been at forefront in implementing these technologies at their plant and getting the benefit out of it.
Mr. Saprativ Basu is currently working as Lead Scientist – CAE Solution (Simulation and Modelling) at R&D, Vedanta Ltd based out of Jharsuguda, Odisha. His role includes process intensification of existing plant operations, evaluating new technologies for capacity enhancement in alumina refinery, smelting, anode manufacturing and building ICME framework for New Product Development. Saprativ has completed his M.Tech in Advanced Modelling and Chemical Engineering Simulation (Materials & Processes) from CSIR-NCL, Pune. He has been associated with R&D, Tata Steel Ltd. and Tridiagonal Solutions Pvt. Ltd. before joining R&D, Vedanta Ltd. He has ~13 years of research experience in detailed design and engineering, project management and conducting engineering study and risk-based analysis across cement, FMCG, Pharma, Mining and Metals sector.