Proud to report that the SMART module will be released to the market (the result of the latest R&D project that we started in October 2020 at AMV), an aid in the characterization of scrap metal using Machine Learning for all types of foundries, steel mills and recyclers.
The SMART module (BA-Master) from Beyond ALEA comes to cover the difficulties in controlling the variability of scrap that make smelting plants adopt conservative positions in the calculation of their recipes, abusing high quality scrap and minimizing the use of more variable and lower cost scrap, even in favor of virgin material (primary aluminum, pig iron, alloys…).
Considering that foundry raw materials represent, in most cases, the main manufacturing cost center (more than 80% in aluminum recycling, and 60% in the case of steel), the negative impact on profitability and competitiveness of the plant derived from these conservative positions is evident.
In fact, studies carried out by some steel mills, shared with AMV, estimated a potential reduction in materials costs of around 12 – 15 euros per ton, which would represent savings of around 10 million euros at the end of each year.
The application of this module will have the following benefits:
I. Allow the use of more economical materials, minimizing the risk of casting out of specification.
II. Reduce the number of chemical adjustments necessary to achieve the desired chemistry, reducing Tap-to-Tap times and energy costs.
III. Characterize raw materials according to quality and variability. In particular, objectively classify suppliers by quality/cost ratio, discarding those that do not meet the minimum requirements.
IV. Build a knowledge base in which all the parameters involved in the performance and quality of castings are considered.
V. Depending on the client’s system structure, the system can act as the brain of an automatic optimization and decision-making system.
Thank you for the subsidy from the 2020 Call on technological development and other digital enabling technologies (C007/20-ED), promoted by Red.es, dependent on the Ministry of Economic Affairs and Digital Transformation, co-financed with FEDER Funds, in its campaign “a way of making Europe”, which will be a boost to our work and consequently to economic growth and job creation.
Procedure: C007/20-ED ARTIFICIAL INTELLIGENCE
Reference: 2020/0720/00099414
