Industries in the Primary Metal Manufacturing subsector smelt and/or refine ferrous and nonferrous metals from ore, pig or scrap, using electrometallurgical and other process metallurgical techniques. Establishments in this subsector also manufacture metal alloys and superalloys by introducing other chemical elements to pure metals. The output of smelting and refining, usually in ingot form, is used in rolling, drawing, and extruding operations to make sheet, strip, bar, rod, or wire, and in molten form to make castings and other basic metal products.
Manufacturing Process Innovations: A “Bessemer Moment” For Titanium?
I had called Taso to talk about their process innovation for making titanium. It is a new method that uses hydrogen instead of carbon: hydrogen assisted metallothermic reduction (HAMR). HAMR promises to be both environmentally friendly as well as much lower cost, what Arima calls titanium’s “Bessemer moment.” The process was developed by metallurgist and Professor of Metallurgical Engineering at the University of Utah, Dr. Z. Zak Fang, under the sponsorship of the U.S. Department of Energy’s ARPA-E program, their version of DARPA. The HAMR process uses half the energy, cuts emissions by more than 30% (and to potentially zero if using renewable energy) to power the furnaces. It substantially reduces the cost of producing titanium. The majority of savings come from eliminating both the chlorination step and the vacuum distillation.
The business of sustainability in steelmaking
These upgrades at the Train 2 plant allowed ArcelorMittal to save 15-20% on installation, reduce downtime by 5-10%, save 170 equivalent metric tons of CO2, and prevent reprocessing 26 tons of materials. Sensor-based equipment condition monitoring also let the steelmaker’s staff track energy use and identify potential faults before they cause downtime. These improvements also increase the facility’s installation reliability, energy efficiency, personnel safety and equipment life with predictive maintenance.
We Recycle More Steel Than Plastic. Why Does It Still Pollute So Much?
AI-based operational excellence in steel manufacturing
Modern steelmaking is heavily instrumented with several process parameters being monitored, yet there are limited operational insights available in real-time. Take, for instance, the continuous casting process − a facility producing 150 tonnes per hour can generate over US$5 million per day in production revenue, assuming current steel prices. Conversely, a single day of lost production is equivalent to US$5 million worth of losses. Therefore, a manufacturer can unlock tremendous value by eliminating these unscheduled production downtimes.
Casting molten steel, unsurprisingly, is hard on heavy equipment. Components wear under harsh conditions leading to failures or adverse product quality. Early detection of such conditions could warn the maintenance and production managers to schedule repairs before failures occur. Applying advanced analytics to machine and process data can help in predicting such unwanted events. Data-science projects are often designed for specific use cases thereby limiting the scope and interoperability of the model. The approach faces challenges in terms of model sustenance in production and scalability across use-cases or plants.
Condition monitoring in steel mills: 3 fault detections
Thermal Process Modeling to Save Energy
The thermal schedule for heating workpieces is often determined by simple rules and experience in industrial production. Thus, a finite element method (FEM) based simulation of heating ingots in heat treatment furnaces is of great importance to thermal optimization. FEM modeling allows for the prediction and control of temperature uniformity — and ultimately microstructure, residual stresses, workpiece properties, and reducing energy consumption.
Optimizing manufacturing processing and quality management with digital twins, IIoT
The application of IIoT and digital twin technologies in production process and quality management in steel production processes with the following characteristics:
- Integrate process design data, quality specification data, equipment operational real time data, quality measurement data into a holistic end-to-end closed-loop system, enabling comprehensive online monitoring and analytics of production process and supporting product quality traceability.
- Combine digital twin and Industrial Internet technology seamlessly into a holistic platform to support such an application.
- Enable digital twin for both equipment and product alike, dynamically bind product digital twins with equipment digital twins to enabling product process and quality online tracking, monitoring and traceability.
- Combine online data and analytic technologies with Lean management and Six Sigma concepts and best practice for production process and quality management, creating a digital Lean capability.