Artificial Intelligence: An impactful way to enhance operational parameters in the primary aluminium industry

Introduction

In my last article, I deliberated on how aluminium and its alloys can dominantly augment the growth engine of hardware architecture associated with AI, like Data Centres, Heat Dissipation and Chip mounting on a global scale. AI is fast becoming a widespread transformative tool for various technology areas like satellite, aerospace, development of critical biological solutions and development of critical metallurgical products. The other side of this story is that the primary aluminium industry itself can be a large beneficiary of the deployment of AI tools, where a shorter time cycle for the solution of complex process problems and abstract correlation between operational conditions can be an impactful factor for the entire industry. However, let us remember, an AI software tool is not a Black Box wherein you dump things and expect a solution in seconds, minutes or hours. Most of these programs are very complex and may not always be adequate to suggest a meaningful solution. Also, people have a wrong notion that the solution can be obtained without deep knowledge of the process. Subject-related knowledge is the core of the solution and therefore, competent human beings will be essential for this effective tool.

Driving operational efficiency: From refinery optimisation to recycling quality

Let us discuss what could be an effective process to contribute to the alumina refinery, enhance quality, optimise energy, optimise the smelter and product development. Recycled aluminium industries globally are using AI-based tools to identify the right quality parameters and improve aberrations in quality in the finished product.

AI for ore quality management and data complexity

Since the availability of high-grade bauxite ores is day by day depleting, I would like to mention that the adoption of effective AI tools can do a magnificent task for sorting the right ores. Now, the question arises: what data will be valid inputs to compute the AI solution? Here comes the complexity and application of Domain knowledge.

Cognitive distinction by an AI tool is based upon data and their correlation and therefore massive computation. It is very much pertinent to build detailed ore characteristics like chemical analysis, mineralogical analysis, non-destructive analytical features and maybe more data will be important inputs.

Enabling real-time decisions and process quality optimisation

Imagine a control room monitor, wherein operators can see a ready solution for rejecting below-grade ores and accept the right raw materials instantaneously. Nothing could be more useful and quicker than this. But this is not simple; this will require a massive integration of various analytical techniques and support of reliable hardware.

Another example could be the concentration of trapped soda in alumina, which is a critical quality parameter in metallurgical and other grades of alumina. A soda concentration of approximately 0.3% is desirable in alumina, which is used in aluminium smelting in the Pot Line.

Also read: Aluminium: A differentiated material in the expansion of the AI industry

Factors influencing the soda in aluminium trihydrate are measurable digestion parameters and precipitation characteristics like temperature, aluminate concentration, rate of precipitation, seed characteristics and organics in liquor.

One has to evaluate the relative influence of the above or more parameters by using a cognitive tool which is under discussion. Let us also remember that every operation needs to be backed by measurable variables, whether independent or dependent.

Expanding AI horizons: Potline efficiency and alloy development

Two more areas that are likely to be strong beneficiaries of the AI tool are the potline operation and the new alloy development. Aluminium Electro-Smelting is the main energy-intensive process. There are variables in the potline operation which are not easy to measure; therefore, there needs to be developed sensors and scientific principles to render everything measurable here. The effectiveness of data-based cognitive judgment to minimise specific energy consumption will then be highly valuable.  Both areas stated above offer complex problems in establishing correlating parameters, and these areas are under intense investigation. However, as mentioned, building quantifiable parameters which may not even appear very relevant may also be found useful when the AI tools explore them. New Alloy Development may offer challenges of reliable experimental data; we have to use intrinsic thermodynamic parameters, which will provide the base for reasoning for the suggested solution.

Conclusion

In a nutshell, AI is an emerging tool which will demand its own support peripherals like structured, well-measured databases. This will itself push newer measurement techniques and the deployment of measurement hardware. With the combination and echo system of all the above that I discussed, I can foresee the industry is going to produce in a much more predictable way, with energy and asset optimisation and efficient resource utilisation. Let us continually work towards that to make the world a winner.

Also read: EGA leading the aluminium industry with AI-powered transformation