5 Smart digital ways to boost sustainability in metals manufacturing

“Digital starts with easy accessing of data,” says Tarun Mathur, Global Portfolio and Sales Manager Digital for the metals industry, Process Industries, ABB. And that data becomes informative, when it’s linked to real world factors such as time frames captured down to the millisecond, temperatures, inputs (different, scrap metal grades) and output quality. Analysis and algorithms can then be applied to build an understanding of past operations, which allow operators to positively impact the future.

“Data is the backbone of any analysis or control,” says Mathur. “Whether you have to prove or to improve, you have to use data to get there.”

Here are five ways to think about transitioning your metals operation to greater sustainability by harnessing data:

When assessing the digital maturity of any factory or industry, Mathur asks, “How do you use your data across different aspects of the business?”

For a couple of decades, he says, operations managers have been using their data to improve operational efficiency; and sales people have used their data to improve their approaches to customers. But Industry 4.0 is about automating decision making using data aggregated across silos.

Logging data on a central platform such as ABB Ability^TM Data Analytics Platform for metals helps manufacturers collect and store data in the right format for making connections and fully informed decisions. The platform makes data gathered from different processes and organizational departments relatable: providing an integrated view of operations via powerful, high-resolution dashboards; and enabling application of new technologies such as machine learning to optimize plant-wide operations or specific process areas.

“You have to take data from your ERP systems, from your sales, from your operations, from your supply chain and so on,” says Mathur. Then you can, for example, investigate what kinds of equipment give better efficiency over a long period of time; or what kind of scrap results in fewer emissions — both of which help you procure from suppliers that contribute to your efficiency and sustainability goals. “Then tapping into the data of your suppliers about their transport emissions, et cetera, helps you understand their Scope 2 and 3 figures, which form part of your own carbon accounting,” explains Mathur.

Where ABB Ability™ Data Analytics Platform for metals is edge-based and integrates data from different operations technology (OT) systems, ABB Ability™ Genix Industrial Analytics and AI Suite is a cloud-based platform that helps metals manufacturers unlock actionable insights from massive amounts of industrial data generated with every melt. It connects industrial artificial intelligence (AI) and analytics with the Industrial Internet of Things (IIoT) in order to optimize production processes, predict maintenance needs and enable faster decision-making in a way that fosters innovation and improves sustainability.

Contextualizing or “logging” data at the point of collection is key to both platforms. When setting up a data logging system, it helps to have outcomes in mind, says Mathur.

ABB has been working, for example, with a large global stainless steel manufacturer, which was able to define 80-100 data-led investigations or projects it wanted to initiate. These included predictive maintenance and optimizing resources to minimize waste. “They made investments upfront in infrastructure, and they gave small startups and big companies such as ABB and Microsoft access to the data, to allow them to develop solutions,” says Mathur, who adds, “They got a lot of great results from what I think was an impressive and balanced strategy.”

Collecting contextualized data over time allows you to benchmark performance. For example, ABB Ability™ Data Analytics for cold rolling mills is a core digital technology solution that analyzes plant and process data. With, say, one year of data in the system, manufacturers can identify the best performance during that period and ascertain optimum rolling set points that achieve world class strip quality and uniformity at the highest rolling speeds.

 “On any given day, your dashboard can also provide insights on how far you are from the benchmark, and which parameters are contributing to that deviation,” says Mathur. You may sometimes be operating under different constraints; understanding the causes and effects of varying inputs and conditions can help managers make more informed decisions about which settings they may be able to adjust to come closer to KPIs in any circumstances.

One measure of success is minimizing waste. “Rolling mills generally have to discard a portion of metal at either end of a sheet,” explains Mathur. “If the mill isn’t running properly, more of what they call the head and tail of a sheet needs to be cut and that then needs to be reprocessed.” Producing to a consistent quality reduces waste and recycling, which in turn minimizes energy use, costs and emissions.

Electrical equipment such as motors and drives play a large part in the milling of metals and consume a significant amount of energy. ABB’s motors have been developed and improved over many decades, to consume less energy than others while delivering the same outcomes. “Combined with ABB automation and digital solutions, these motors help our customers achieve the most sustainable operations,” says Mathur.

Another digital way to optimize operations for efficiency and sustainability is to use a broad range of contextualized data and algorithms to mimic a process or a series of interconnected processes. The resulting virtual model of processes is known as a digital twin and allows operators to answer, “What if …”, says Mathur, “without risking poor outcomes in actual production.”

You can test, for example, the effect of different scrap grades on energy use or final product quality and characteristics. You can also test different modes of operation to discover which are most sustainable. Says Mathur, “A digital twin helps you identify the optimal trajectory for your mill or for your plant to produce consistent outcomes in a more sustainable way.

“Making decisions without a data led model,” he continues, “you might be able to achieve optimal operations once or twice. But without the number-crunching power of digital technology it's impossible for a human being to take into account millions of data points that change every second.”

A digital twin can be used as an operator assistant, recommending different set points for a process as factors such as electricity costs or sources change, or feedstock quality varies. It can analyze all possible scenarios to come up with the most efficient option. If required, it can also automate such adjustments. 

ABB Ability™ Expert Optimizer, ABB's Advance Process Control (APC) solution for metals currently offers optimizing applications for:

• Pellet plant indurating machine burners
• Pellet plant dryer
• Pellet balling disc
• Raw material grinding mills

Using Model Predictive Control (MPC) to create digital twins of these steel production processes, ABB’s APC solution processes data to predict behavior over time. The solution then drives the process towards more profitable operation, reducing the need for operators to monitor and modify processes around the clock.

In one instance, says Mathur, “We installed cameras for measuring green pellet size produced by a balling disc. This feedback allows APC to change process setpoints such as disc speed and moisture addition, which in this case reduced the reject by almost 50%. Specific energy consumption of the process is improved as a result. Further, APC on a pellet indurating machine was found to increase productivity of the pellet plant, as well as reduce power consumption of the fans.”

Remote access support and KPI tracking by ABB experts can ensure that sustainability and operational excellence provided by digital applications such as APC are delivered over the life cycle of the plant.

Sometimes, additional data is required to really transform a process. ABB Ability™ Smart Melt Shop was developed in response to customer JSW Steel’s requirement to more efficiently manage crane and ladle movements and optimize temperature control in its melt shop. Development of the solution required constant engagement and collaboration between ABB and JSW Steel in Maharashtra, India, as ABB integrated new sensors, including visual tracking, into its system modeling.

In a melt shop it is important to run the right process temperatures, says Mathur. The melt is carried by crane from one station to another through a sequence of processes, and it is very difficult to manually maintain precise temperatures during required cooling and reheating. Operators typically err on the side of overheating, because underheating carries the risk of rejection and wasting 100-200 tons of material. Overheating, on the other hand, incurs a great deal of energy wastage, which impacts the melt shop’s emissions profile.

Smart Melt Shop captures all ladle movements using cameras and positioning systems, radically reducing delays, which cause loss of heat, and only reheats to precise temperatures as required by each process. “We bring data on all the movement of cranes, equipment and materials into one place, and then our machine learning algorithms optimize the operation — including which crane should be assigned to which jobs in order to produce more within a timeframe,” says Mathur.

Applying this digital solution resulted in improved decision making that led to numerous benefits at JSW, including: 4-5% higher casting speeds, 5°C less arcing for each heating of the ladle furnace; less material rejection due to incorrect temperature at destination; and an overall increase in productivity that equates to 24,000 extra tons of steel a year.

Building on digital applications such as Smart Melt Shop, which help to advance predictable scheduling of machinery and processes, there is additional potential to forecast energy demand and optimize the flow of energy and fuel gases in integrated steel plants. Such forecasting also helps steelmakers to synchronize peak production times with off-peak electricity prices, or at least to access the cheapest or most sustainable energy available from the grid at any given time.

“ABB’s energy management solution for metals manufacturers can now incorporate demand forecasting,” says Mathur, “which means we utilize data from a plant to tell operators how much electrical power they will require over the next few hours.” An optimization feature then allows them to choose which form of power – such as renewable energy, fossil fuels, or stored energy from combined sources – to purchase.

ABB has tested industrial Demand Side Management (iDSM) in collaboration with a European steelmaker. The production scheduler was successfully integrated with forecast energy prices to make use of the most favorably priced electricity in the local energy market while maintaining output and using the least possible amount of energy.

In another use case, applying AI to accurately forecast energy demand, led to more efficient energy use and improved electricity procurement forecasts by 15%.

When combined with options for the purchase of cleaner energy, iDSM offers procurement departments two pathways to improving the sustainability of their metals manufacturing.