Using Artificial Intelligence (AI) to Reduce Scrap and Energy Usage in the Casting Process

Greenhouse gas (GHG) emission reduction requirements are found at the state and country level in most developed countries. For example, in 2020, China announced its goals to strive for carbon dioxide peaking before 2030 and achieve carbon neutrality before 2060.1 As major industrial emitters face increased scrutiny to reduce emissions, the focus on regulatory compliance will also extend downward to encompass large- and medium-sized enterprises.

The use of castings is central to a significant portion of the industrial valve industry. Depending on the valve design/type, castings can be found in pressure boundary parts (body, bonnet, body end), wedges, and other key components. Foundries are major energy consumers. Studies2 have shown typical steel arc metal casting, typical for valve and pump manufacturing, consumes approximately 20.7 million BTU/ton. Reducing scrap and improving yield, in general, would represent notable energy consumption reduction, and therefore GHG emissions, where renewable energy is not being used in the steel-making process.

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