Primary Metal
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.
Assembly Line
Special steel plant in Kapfenberg: workplace of the future
Procedures, working methods, systems, processes, control, collaboration: a lot is new at the Kapfenberg special steel plant. In the old plant, work was spread out over several stations, but now many people can work together in one room: melters, crane operators, plant operators. Technology and control are centralized in one place. The use of robots and manipulators means less physical effort, but digitalization comes with other challenges.
UO researchers use electrochemistry to decarbonize iron production
Using electrochemistry, University of Oregon researchers have developed a way to make iron metal for steel production without burning fossil fuels. The series of chemical reactions turns saltwater and iron oxide — cheap and abundant ingredients — into pure iron metal.
If scaled up, the process could help decarbonize one of the largest and most emissions-intensive industries worldwide. It might someday replace the carbon-spewing industrial blast furnaces currently used to produce the iron that feeds steel manufacturing. Importantly, the byproducts of the chemical reaction can all be repurposed. The sodium hydroxide that’s generated can go back into the reactor or be collected and used in carbon-capture technology. And chlorine is valuable in other industrial processes.
The trillion-dollar quest to make green steel
But the main explanation for steel’s giant carbon footprint is that, globally, most steel is still made by heating fossil fuels to turn raw iron ore into finished metal — a process that generates 90 percent of CO2 emissions from steel, along with a toxic soup of heavy metals and air pollution. While recycled steel can displace some of the demand for “primary” steel, it doesn’t diminish the need to clean up or replace coal-fueled furnaces.
Most likely, that shift will include using hydrogen to process iron ore for steelmaking. Only one facility in the world is currently doing this at any meaningful scale: the $180 million Hybrit project in Sweden. However, dozens of projects involving hydrogen are in various stages of development worldwide. Sweden’s H2 Green Steel recently raised $1.6 billion to build the world’s first large-scale, hydrogen-fueled plant, while Chinese steelmaker HBIS Group said it produced its first batch of hydrogen-infused iron.
Undoubtedly, the steel industry’s transformation will require countries to build significantly more renewable energy capacity, both to power electricity-driven furnaces and to produce “green” hydrogen, of which very little is available today worldwide. Down the line, next-generation technologies developed by startups such as Electra and Boston Metal could make it cheaper and easier to produce green steel. All told, decarbonizing iron and steel is expected to require $1.4 trillion of investment by midcentury.
In 2021, three years after construction began, the Hybrit plant successfully produced the world’s first steel reduced by 100 percent fossil-free hydrogen,” which it delivered to Swedish automaker Volvo Group. To date, Pei said the facility has produced about 2,000 metric tons of DRI, also known as “sponge iron.” For comparison, that’s roughly the average amount of steel needed to make over 2,200 cars.
🗜️ Can OES Provide Inclusion Analysis during Steel Production?
Non-metallic inclusions are of considerable importance for the steel industry due to the dramatic influence that even small amounts of them may have on properties, mechanical and otherwise, of the metal or on the production process itself. Inclusions can have positive effects and may increase the value of the steel, but most often inclusions signify quality problems and reduced value.
The modern reference for inclusion analysis is the SEM/EDX (scanning electron microscope coupled with energy dispersive X-ray fluorescence spectroscopy). This analytical process takes typically several hours, including sample preparation and interpretation, which is far too long to be applicable to production control.
Within recent years we’ve seen the development of extremely fast and economical OES (optical emission spectrometry) methods which are able to provide inclusion information even during the steel production process. The method uses the principle of Single Spark Acquisition (SSA), where signals from the individual “single sparks” are not summed as in conventional OES acquisition, but processed with special algorithms.
Lift: Advanced, automated metal forming, controls, training, optimization
Advanced manufacturing techniques are advancing and on display at Lift in Detroit. Lift is operated by the American Lightweight Materials Manufacturing Innovation Institute (ALMMII). Lift is a public-private partnership among the U.S. Department of Defense, industry and academia, and it is part of the national network of manufacturing innovation institutes. Major participants in the Lift facility include Hexagon, Kearney, Siemens, U.S. Department of Defense and Department of Commerce.
Others, such as ABB, Fanuc and Festo, have supplied equipment to the advanced manufacturing demonstration and training facility. Siemens experts Tom Hoffman, Drew Whitney, Ed Chenhalls, Isaac Sislo, Matt Sislo and Alec Hopkins provided the Lift tour and information on April 13, during the Manufacturing in America event, in Detroit. The Siemens area at Lift headquarters can hold about 50 people for workshops on topics such as digital threads, digital twins, simulation, automation, controls, design, maintenance and industrial machinery.
HBIS is producing DRI by using more than 60% of hydrogen
Chinese HBZX High Tech, part of Hebei Iron & Steel Group – HBIS, is the first worldwide steelmaker producing DRI using more than 60% Hydrogen in the feed gas mix, on industrial basis. This happened at the HBZX plant, in Xuan Hua, Zhangjiakou, Hebei province, where a new, 600,000 tpy, Zero Reformer, ENERGIRON® direct reduction plant has been supplied and achieved continuous, stable, and safely production with outstanding quality.
This is an outstanding achievement, since the plant is the first hydrogen-enriched gas-powered DRI industrial production facility in the world and represents a significant accomplishment for the Chinese steel industry, being also the first green gas-based DRI plant in the country, paving the way to the transition from the carbon-based BF route to gas-based DRI technology and electric steelmaking.
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.
Advanced analytics of sinter plant operations to minimise particulate emissions
Advanced Analytics of Sinter Plant Operationsto Minimise Particulate Emissions(PM). Use of data analysis techniques to improve understanding of the process and correlate process parameters and raw materialsto PM emissions and to identify areas of opportunity to decrease PM to the local community. Understand the effects of chemistry upon performance, focusing on reduced chlorides by treatment of reverts, effects on sinter quality and design/implementing new sinter process filter system technology on a laboratory scale to capture and measure PM emissions.
U.S. Steel Gains AI Know-How in Big River Deal
By investing in Big River, which began operating in 2017, U.S. Steel gains access to the technology and know-how for producing sheet steel from melting scrap in an electric furnace. The deal is expected to make U.S. Steel more cost-competitive with rivals, including Nucor Corp. and Steel Dynamics Inc., that use electric furnaces to turn scrap metal into steel.
The plant’s AI system, designed by San Francisco technology firm Noodle Analytics Inc., uses deep learning and neural networks. It was designed to continually train algorithms on data captured by thousands of sensors.
The data can be useful in a number of ways, from spotting problems with production and quality to helping sequence the production of various grades and sizes of steel in the most efficient manner. The system can also help conserve energy consumption beyond what the plant gets per its utility contract, maximizing the amount of surplus power it can sell.