Practical ways to apply data analytics and AI for energy management and emission control in the steel industry

Originally published in the August issue of Steel Times International.

Disruptive technologies such as AI, machine learning and advanced data analytics – combined with deep domain knowledge and investment in a digitally literate workforce – are the key to improving sustainability and productivity in energy-intensive process industries, explains ABB’s Sanjit Shewale.  

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The digitalization of the process industries – including the steel industry – continues apace, with innovations such as artificial intelligence (AI), machine learning (ML) and advanced data analytics offering unprecedented visualization and pattern recognition capabilities, recommending optimal actions for every stage of the value chain.

Armed with these granular insights, industrial companies can make informed, data-driven decisions that drive genuine business value, reducing both OPEX and CAPEX, while improving safety, protecting their reputation, boosting environmental, social and governance (ESG) credentials, and complying with evolving regulations.

Given these strong business imperatives, it is surprising therefore that many energy-saving and emission reduction opportunities remain untapped. Plant owners that adopt digital solutions can look forward to better performance at a reduced capital cost, as well as overall standardization of environmental practices, a significant help in reducing and managing their inherent complexity.  

 

Sanjit Shewale

Vice president and global head of Digital, Process Industries Division

Contact an ABB expert

Domain knowledge is important

In addition to the recurrent challenges of optimizing efficiency, and maintaining throughput and quality control during high-volume production, chemical processing companies must now comply with laws aimed at reducing emissions in line with the 2015 Paris Agreement – or face penalties.

The whole energy ecosystem is also evolving – and while this transition is not without its challenges, the enabler is most definitely digital technologies. However, it is important to note at the outset that the idea that simply gathering data in a pool and then applying AI and ML will magically transform industrial operations is flawed. What is often missing is a conceptual understanding of how the data got into that pool – and, as importantly, how it can best be leveraged to add real value to a business.

Many technology/software companies dive into a project without having a clue about the industry. They make false promises. ML requires a lot of data – with data classification and management approach closely aligned to the business strategy. It also requires specialists able to literally teach machines how to structure their computing methods more like human thought, how to solve complex industry-specific tasks by breaking them into simple steps and how to self-monitor for effectiveness. Partnering with a trusted technology provider with deep domain knowledge and expertise is a prerequisite of success, and an incredible opportunity for companies and people who understand how to navigate the digital storm.

In short, the role of people in solving challenges around accuracy of AI techniques and how to explain them must not be underestimated – so that machine recommendations can be trusted. Data cleansing, anomaly removal, analysing the correlation of parameters and proper result interpretation are as important as ever; after all, if you trust the people, you trust the algorithm. I will talk more about investment in human capital later.

Case study 1: Decarbonizing steel production

When we talk about decarbonization potential across industries, it is really the additional ‘horsepower’ provided by advanced data analytics, AI and ML that is the gamechanger. For operators in the process industries, it will be impossible to hit KPIs and tightening sustainability targets by manual setpoint adjustment alone, and not just around reducing emissions, but also energy optimisation and management. As more and more data is there, and with the implementation effort decreasing, we will see more and more people getting involved in AI advisory - driven by corporate decarbonization initiatives.

We engineers know that it is easier to speak about ML than to implement it. We get excited about addressing the untapped opportunities in process industries, especially sustainability. But we also get practical and pragmatic, starting with examples of how data has already been used successfully.

Let’s take a real-life example of a steel plant with annual capacity of up to five million tons of steel. 
Digital energy management can optimize steelmaking processes

Complex distribution networks for electricity, steam, by-product gases and imported fuels account for as much as 20% of production costs. Iron ore reduction is where the vast majority of carbon emissions come from in steelmaking, but the world will continue to rely on iron ore until around 2100. The challenge for the industry in the coming decades must therefore be to transition to alternative, preferably renewable, sources of energy and optimize their use for iron ore reduction.

ABB’s Digital Energy Management solution has a proven track record of short payback times and long-term benefits. It not only optimizes production scheduling, throughput and quality, but also energy-related costs, raw material usage, carbon, greenhouse gases and waste emissions. As mentioned, it relies on decades of experience in the process industries – and steelmaking in particular – captured in rule-based predictive energy management algorithms. 

Designing, deploying and maintaining the optimal site-wide or enterprise-wide energy management and emission control strategy is a large engineering and operational challenge, one that requires a wide span of competencies, engineering tools, architecture approaches and service capabilities in order to identify the most performing and cost-effective solution for each process area and site.

Deploying digital energy management system in a modular approach

Typical steps and modular approach for deploying a digital energy management system are:

Monitor: Monitoring energy usage at plant and process level with real-time visual displays and data

Identify: Identify best performance of process areas and opportunities for improvement

Report: Report energy consumption patterns of process areas and production lines

Analyze: Analyze inefficiencies in plant and process areas

Alarm: Alarm capabilities – enable corrective measures to be taken immediately

Forecast: Forecast energy consumption schedules for process areas based on production plans and measured consumption

Optimize: Solve economic real-time optimization problems consisting of own generation, trading and using of energy in industrial plants and power plants

Waste gas utilization in metals production can be greatly improved by monitoring generation and consumption across plant facilities in real time. Data is collected from multiple systems to compare allocation with actual consumption, provide real-time demand and supply calculations, balancing, benchmark and optimal distribution, as well as forecasts based on production plans and historical data modelling. Root cause analysis is also applied when a gap occurs between supply and demand.

Byproduct gases network optimization

Deploying data with edge computing power

Digital energy optimization is a perfect use case for both AI and ML, with multiple data sources and concrete problems to solve. Complementing these management systems with data contextualization and digital twins allows operators to further optimize their energy consumption using load profiling and balancing, and process parameters; air temperature at the compressor intake, for example.

As another example, a complementary System Anomaly detection app can be deployed to detect unusual pattern, anomalous behavior from process streaming time series data. It uses AI/ML methods to continuously monitor changes in pattern across IT and OT systems from high frequency near real-time data. By triggering alerts, it helps reduce manual efforts needed to identify and rectify energy consumption deviations.

The latest enterprise-grade platforms and suites of AI and ML enabled applications enable companies to deploy and monitor advanced controllers, data analytics and optimization solutions at the edge, to and from an industrial cloud/multi-cloud, or on-premises.

However, this increased complexity requires digital solution providers to have the ability to master very different technologies, industry-specific processes, cybersecurity and offer the needed consultancy and assistance to the end users from the early design stages to system commissioning and maintenance.

System anomaly detection on asset or system energy consumption deviations and alerts

Summary and key benefits

The steelmaking plant mentioned has reported initial results of 10% less flaring of gases and 15% accuracy improvement of electricity procurement forecasts. Site-wide optimization of energy consumption and availability was a key factor in achieving the results as the solution covered steam yield, by-product gases, energy purchase and production, including site power plants and turbines.

In addition, there are further opportunities to scale and enhance installed digital systems based on the latest edge computing, cloud, visualization, AI and ML technologies; for example, by using analytics for adaptive re-modelling and tuning, including additional variables for optimization.

Taking a wider view, visualizing the top steelmakers’ strategic roadmap versus available technologies ripe for analytics and ML demonstrates just how big the potential is for digitally optimizing energy use in the industry. Policymakers are also looking at best practices to set and track adequate targets.

A multitude of research is ongoing in areas such as process-specific data models and simulations, carbon capture, utilization and storage, or making green hydrogen a financially feasible option.

Encouragingly, this will help steelmakers shift away from current production methods towards completely new ways of improving energy efficiency, reducing energy cost and carbon footprint.

Integrated decision support system to drive continuous performance improvements

Investing in human capital

This successful collaboration between humans and machines is fundamental if the steel industry to reap the full benefits of industry 4.0 innovations when it comes to sustainability. 

We, as engineers, are good at domain-specific knowledge, intuition, creativity, empathy. Machines, conversely, are good at large data sets, calculations, learning, automation, pattern recognition. The company that can marry the two successfully will enjoy competitive advantage in the digital future.

Sustainability-related actions mean different things to different people depending on their job function, and they experience varying degrees of difficulty when it comes to locating and analyzing data across their organization. An industrial-grade analytics and AI platform helps you get the most from federated data sources – your existing control systems, IoT devices, MES, ERP, CMMS, engineering and other databases.

Contextual Data Fusion tools automate data integration, letting operators identify previously hidden relationships and performance trends, make timely predictions and accurate forecasts. The business value from taking this holistic view is tailored to specific roles and enables cross-enterprise actions.

However, this can only be achieved if you trust people who are building the optimization models for specific use cases: their deep process expertise, the know-how of your industry. Now, their expertise and ingenuity can be captured and shared on a common platform – augmented with edge, cloud and ML technologies for maximum impact. Industrial analytics helps various functions in all industries to collaborate, augmenting human abilities with AI/ML, and focusing on solving concrete problems.

Earlier, I mentioned investment in people and its importance to the digital transition. In this context, when we talk about technology being ‘disruptive’, we don’t just mean in terms of quantifiable, statistical gains around efficiency and sustainability: we also mean culturally within an organization.

When we talk about the digital transformation, we often do so in terms of futuristic technology, but in fact, if it’s done well, it is more a commitment to continuous improvement and perpetual change – in order to stay relevant. Therefore, I prefer the term DigitalOps. Practicing DigitalOps means continuously discovering, modelling, analyzing, combining, extending, and optimizing business processes in a way that allows companies hit their KPIs around operational excellence, sustainability, process and asset performance, safety and productivity of connected workers. It also means looking differently at cyber security – as an enabler and accelerator of DigitalOps, defending your business in a dynamic, strategic way.

Understandably, many people ask: “Is my job at risk, or will it vanish, as a result of digitalization and automation?” This is the wrong question. Certainly, many personnel in the process industries will see the way in which they work fundamentally transformed, but the real question should be: “How can I best apply and adapt my existing skillset to allow me to be relevant and prosper in the brave new world of digital?”

There is a significant shortage of skilled labour in the process industries. The onus is on companies to demonstrate that they are serious about ESG policies and innovation if they wish to attract the next generation of digitally literate talent to whom issues such as climate change are a genuine concern.

The technology transition must be supported by the transformation of processes and people. How well a specific solution solves a use case is often not as important as how it fits within the overall operational business culture. Unfortunately, successful change management is often overlooked.

Accelerating the use of AI

AI is transforming industry. One way that ABB develops AI capability is through making low-code/ no-code solutions accessible to more people and letting them capture “tribal knowledge” on a common platform. Amongst other activities, the company collaborates with industry consortia, innovation-driven start-ups, conducts the ABB Industrial AI Accelerator program to drive the next level of the industrial revolution via such collaborations.

AI, often dubbed “the next technology frontier” or “the Intelligence Revolution”, is one area where collaboration can make all the difference. AI has the potential to enhance human capabilities in a range of industries. AI is still a growing field, in terms of venture capital investments it is one of the best-funded sectors. Large firms in almost every industry are trying to integrate AI capabilities into their offerings.

It is in the interest of all the process industries to invest in their digital future, particularly when it comes to decarbonization and carbon neutrality. The new EU carbon border tax, for example, will apply to electricity, iron and steel, aluminium, fertilizers and cement. Against this backdrop, an effective, coherent sustainability strategy is no longer a ‘nice to have’, it is a business imperative. 

ABB’s approach for data and analytics and AI in process industries

Summary and conclusion

In conclusion, technological innovations such as AI, ML and advanced data analytics, provided by a specialist technology vendor with proven domain knowledge, operated by a motivated, digitally literate workforce, and backed by meaningful long-term investment have tremendous potential to optimize energy management and emission control in the steel industry, allowing companies to hit environmental targets and contribute to a cleaner, safer, more sustainable world.
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