Conclusion

Till now we've seen quite a lot about Aluminium furnances, what are the monetary benefits in knowing the maintenance cycle beforehand, and ultimately the effects of byproducts generated as a result of poor and delayed maintenance. To deal with such a enormmous challenge, we embarked on a journey where machine learning algorithms come to the rescue.

Leveraging the sensor data generated from Aluminium furnaces paved the way for machine learning to show its magic (nothing but mathematics). Using machine learning algorithms, we could predict if a furnace required maintenance for 81% of the times. That is, we can save up to 16000 USD in an hour by employing this machine learning model onto the aluminium furnaces.

Over the course of a year, this translates to potential savings of over 35 million USD. Not only will this have a positive effect on the yield and quality of recycled aluminium, but it will also significantly reduce maintenance costs for aluminium manufacturers, enabling them to reinvest those savings in other areas of their business. By embracing this innovative model, aluminium manufacturers can gain a competitive edge in the industry and increase their profitability.

Improving upon the yield will reduce the dross generated by 30%, given that this extra dross gets generated as a result of poor remelting process caused by the unexpeceted maintenance cycles. Even though 30% seems to be an inconsequential number it can have far reaching effects in a long run. Dross reduction is a huge relief for those workers who compromise with their well being.

Additionally, cutting down dross save the fortune spent by the aluminum companies in logistics, recycling and dumping of dross. Cutting down logistics will automatically reduce the carbon footprint of the organization. Reduction in dross recycling will subside the toxic chemicals generated as a byproduct. This will help organizations by abating the monetary funds invested to ensure proper disposal of these chemicals.

The chain of events triggered by this technique will empower the Aluminium industry to invest upon robust recycling processes, that would have more tangible effects. Hence, this model opens a new gateway to sustainable developement within the Aluminium industry. As a concluding note, this machine learning model will break the chain of exacerbation that aluminium industry leaves behind.