REWAS 2022: Automation and Digitalization for Advanced Manufacturing: Advanced Process Simulation, Visualization and Measurement Techniques
Sponsored by: TMS Extraction and Processing Division, TMS: Recycling and Environmental Technologies Committee, TMS: Process Technology and Modeling Committee
Program Organizers: Elsa Olivetti, Massachusetts Institute of Technology; Alexandra Anderson, Gopher Resource; Mertol Gokelma, Izmir Institute of Technology; Camille Fleuriault, Eramet Norway; Kaka Ma, Colorado State University
Wednesday 2:00 PM
March 2, 2022
Room: 211B
Location: Anaheim Convention Center
Session Chair: Alexandra Anderson, Gopher Resource
2:00 PM Introductory Comments
2:05 PM Invited
Digitalization for Advanced Manufacturing through Simulation, Visualization and Machine Learning: Chenn Zhou1; John Moreland1; Armin Silaen1; Tyamo Okosun1; Nicholas Walla1; Kyle Toth1; 1Purdue University Northwest
Computer simulation, visualization and machine learning are increasingly playing key roles in the Digitalization for advanced manufacturing. These technologies can be used to create cutting-edge physics-based and data-driven tools for real-time decision making to address critical issues related to energy efficiency, carbon-footprint and other pollutant emissions, productivity, quality, operation efficiency, maintenance, and more. These technologies can provide fundamental understating and practical guidance for process design, troubleshooting, and optimization, new process development and scale up, as well as workforce development. The Center for Innovation and Visualization through Simulation (CIVS) at Purdue University Northwest has used these technologies to develop and implement digitalization for Advanced Manufacturing in partnerships with steel and other industries. Methodologies and project examples will be presented in this paper.
2:35 PM Invited
Computational Methodology to Simulate Pyrometallurgical Processes in a Secondary Lead Furnace: Vivek Rao1; Vineet Kumar1; Alexandra Anderson2; Joseph Grogan2; 1Oak Ridge National Laboratory; 2Gopher Resource
Lead is the most recycled metal and our goal in this work is to improve energy efficiencies in the smelting processes used for recycling. In that effort, Oak Ridge National Laboratory is partnering with Gopher Resource, the second largest independent lead recycling company in the United States, to develop a high-fidelity Computational Fluid Dynamics (CFD) model of a direct-fired secondary lead furnace. These high-performance simulations are being developed using first principles modeling with pilot and operational empirical validation. A three staged process is used to tackle the problem. Firstly, a CFD model of an experimental furnace is constructed to validate the combustion model. Secondly, a solid phase furnace feed is added using a Discrete Element Model. Thirdly, and lastly, the full furnace operation is simulated which includes the combustion flow field, particle loading, smelting and melting processes, and formation of slag and lead.
3:05 PM
Determining the Bubble Dynamics of a Top Submerged Lance Smelter: Avinash Kandalam1; Markus Reinmöller1; Michael Stelter1; Markus Reuter1; Alexandros Charitos1; 1TU Bergakademie Freiberg
Top Submerged Lance (TSL) smelter is widely used in non-ferrous metallurgy to extract various primary and secondary materials. The technology has found wide application with regard to copper, lead and zinc, nickel tin, while applications concerning iron and municipal solid waste treatment have been reported. As of 2019, there are about 66 operating TSL plants globally. By controlling the air/fuel ratio (i.e. by lambda value), the TSL can be operated under oxidizing/reducing/inert conditions. The bubble dynamics play a crucial role in determining the reaction kinetics, splashing, turbulence, sloshing as well as the refractory and lance wear. This paper shows the efforts to determine the bubble dynamics in a cold TSL-model using acoustic measurements, lance motion system, and high-speed photography. The results show a correlation between bubble dynamics with respect to varying lance submersion depths, bath properties (varying viscosities and densities, i.e. glycerol/water mixtures), and lance flow rates (i.e. airflow).