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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Cast Shop Technology: Energy Joint Session
Presentation Title Artificial Intelligence to Optimize Remelting Processes: An Approach Combining Data Acquisition and Modeling
Author(s) Amin Rostamian, Marc Bertherat, Michel Rappaz
On-Site Speaker (Planned) Amin Rostamian
Abstract Scope Recycling and remelting of Al alloys involve large amounts of energy and CO2 release. In order to minimize energy consumption and environment impact, a novel approach has been developed and tested for this industrial sector, but it can be extended to other processes and materials. The approach is based on on-line data acquisition and efficient numerical modeling of heat exchanges within a remelting furnace. The fast and efficient numerical calculations, which includes the physical mechanisms of combustion, convection and radiation, have a few adjustable parameters which are calibrated on-line by a few data acquisition values. A friendly user-interface allows furnace operators to monitor the remelting process and optimize mass loading, door opening, heating sequences, etc. The main features of the product SmartMelt® will be presented.
Proceedings Inclusion? Planned: Light Metals


Aluminum Melting Furnace Pressure Control
Case Study of Air Cooled Electromagnetic Stirred Melting Furnace at Hydro Henderson
Efficiency of the Casting Process Starts in the Melt Shop
Gas Fired Holding Furnace Modeling for Efficient Operation
Praxair’s OPTIVIEWTM Image Analysis System for Enhanced Combustion Control on Aluminum Tilting Rotary Furnace
Productivity and Energy Efficiency Improvements at Two Reverberatory Furnaces at Alcoa, Norway
Resource Efficiency Analysis of High Pressure Die Casting Process
The Application of ALTEK Stirring Technology to a 90MT Melting Furnace at ALCOA Moesjen, Norway

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