The rise of metal AI how aluminium price hike leads to more investments in AI
Aluminium is the third most abundant metal in Earth’s crust, making up about 8.13% by mass in ore bauxite. And yet, the manufacturing industry faces an imminent risk of depleting aluminium reserves.
In 2021, aluminium prices rose by almost 50% to $3,000 a tonne for the first time in 13 years – which has been alarming for metal producers. This steep rise in prices has been due to a mix of economic fluctuations, rising demand, and logistical challenges caused by the pandemic.
With the help of government stimulus packages and subsidiaries, many countries are seeing an increased demand for metals like aluminium. Analysts see that economic pursuits have grown substantially in Asia, the most significant user for metal, led by China’s expanding appetite. The rise in demand and the lack of efficient and reliable supplies have made it tedious for companies to meet production targets. And with Covid-19 disrupting the global supply chains, recovering from the depleting and costly foundries have been a stagnant process.
The aluminium industry has endured an enormous capacity for several years, but the supply has been further limited due to geopolitical issues as well. The uncertainty of a coup in Guinea, the world’s second-largest ore producer and a vital supplier for China, has played a part in drastically increasing the price of bauxite.
The Chinese connection
Aluminium production has always been an energy-intensive process. Another reason for the global aluminium supply getting disrupted is due to droughts in Cina’s Yunnan province, which has impacted their hydropower supply. Moreover, China’s plans to reduce its carbon footprint has also been a cause for concern since most aluminium producers depend on dedicated coal-fired plants for their electrical supply.
Can Artificial Intelligence solutions mitigate the Aluminium crisis?
Now more than ever, the metallurgical industry is in desperate need to look for cost-effective solutions that can sustain the existing sparse supply of aluminium, improve product quality, decrease energy consumption, and stabilize operations.
While sustainability is a pressing priority for aluminium producers to make them stand out in the market, artificial intelligence enables them to gauge how much electricity they require over a period of time, which can lessen the burden of energy consumption when needed. Artificial intelligence can also automate all the tedious, error-prone, and inaccurate processes to enable efficient data collection and analysis procedures that drive accurate decision-making. With industrial AI, metallurgical industries can take advantage of data for predictive and prescriptive analytics. This helps organizations reduce metal scrap and conserve plant energy while improving product quality and lead time. AI techniques also increase fidelity that amplifies the power of analytics and leads to more accurate models.
How to optimize your aluminium supply with Tvarit
At Tvarit, we have created process-specific algorithms to help manufacturers drastically improve their sustainability using:
- Prescriptive quality to reduce scrap rate
- Prescriptive maintenance to improve machine availability and reduce mean time between failure (MTBF)
- Prescriptive energy to conserve energy and cost
- AI Production planning to optimize changeovers
Tvarit Industrial Artificial Intelligence solutions enable foundries and manufacturers to optimize operations in an ever-evolving environment. By combining data and metallurgical knowledge, our trustworthy hybrid models predict errors long before they occur and prescribe modifications in process parameters to fix quality issues at their root cause. This promises sustainability, reduction of waste metal scrap, and lower energy consumption while elevating product quality and process assessment.
Our AI platform, TIA (Tvarit Industrial Artificial Intelligence), evaluates process deviations based on production and machine data, identifies the root cause, proactively maintains machines and processes, and optimizes the manufacturing unit for faster and cost-effective production. Furthermore, manufacturers can avoid product rejections with prescriptive recommendations for optimal machine and process parameter settings.