AI Transforms Chemical Industry’s Sustainability Practices

Addressing Differing Digital Maturities

Stephen Reynolds, global industry principal for chemicals for AVEVA, Chicago, also emphasizes that the chemical industry’s wide spectrum of digital maturity means that solutions offered by vendors must be tailored to each application. 

“We have to find out where a customer is on their digital journey; what they are looking to achieve. It’s always a case of finding the right tool for the right solution,” he said.

A chemical engineer who has used digital solutions for over 20 years, he believes that the key is to use one that can contextualize data and enable analytics within the data historian.

“With AVEVA’s PI System we can group data by plant, by unit, by equipment. We can run reports based on just emissions or maintenance assets or process batches, for example, depending on how we organize our database,” he explained. 

Another is the ability to create alerts and distribute them to the right people.

Morse illustrated the importance of this with the example of a plant he worked at where process temperature was a key variable. Thermocouples would fail but not be addressed for days or sometimes even weeks. 

“We used PI to detect these failures and send an immediate notification to our maintenance system for replacements. So, it would go straight on the books without requiring human intervention. That kind of data integration across different applications is what’s important today, plus its communication, notification and visualization in the same place through the same platform,” he added.

While the company believes that cloud and AI technologies are the future, a host of tools are available prior to that level. 

“To each tool its best purpose, and they build on each other. This is important because not every customer is racing to the cloud or has the necessary data to use AI yet,” he added.

Coming up as a chemical engineer, Reynolds would pull all his data into Microsoft Excel, look for a trend and go and do his job. “Many customers still operate with that level of sophistication. We still talk to customers who have a DCS or HMI SCADA system in their plant, but no data historian and no way of visualizing outside of the control system,” Reynolds noted.  

The first step is moving away from those spreadsheets by automating some of the watchdogs that all companies keep track of — KPIs, for example. “That’s all done with PI, so think of it as foundational condition monitoring — whether it’s process monitoring, equipment monitoring or environmental monitoring, we can get that first line of defense and troubleshoot the issues and report out at that stage.” 

Next is building simulations based on first-principle models of the process, which will show where a plant should be operating rather than where it is operating. 

Then you get into the AI, machine learning (ML) stage where you are combining all this data with non-realtime information — perhaps bringing in lab analytics, weather data, cost and performance information from an ERP system. 

“Now you’re building a data warehouse or data lake out of a big hodgepodge of data, which AI and ML can process quickly. We can interrogate it, flex it a little and come up with that final 20%: the cherry on top of the sundae. That’s the AI bit. Today, we would use it for that last little piece because we’ve exhausted all other tools getting there,” he explained.

For example, Covestro has been using PI System and AVEVA’s Process Simulation tool to investigate process limits and energy targets across multiple facilities as part of a long-term project. As process understanding has improved, Covestro has reduced the energy footprint of these plants by 30%.

Then there’s Solvay’s chemical plant in São Paulo, Brazil, which wanted to install real-time equipment performance monitoring. 

The PI System was the foundation for this, while custom hybrid AI models calculated performance indicators and identified failures in 173 pieces of equipment, including pumps, centrifugal compressors, liquid ring compressors, fans, a cooling tower and a turbo generator.

PI System tools were used to model the equipment variables, build KPIs, configure notifications, notify the maintenance team about potential abnormal conditions and present the information through user-friendly dashboards. The company also noted a better integration between maintenance, operations and engineering teams.

“Solvay also has had some success using PI System as a demonstration of compliance, so it was a key element of their environmental/emission management program and reporting that they submitted to their local environment agency,” said Reynolds.

Another customer is looking to modularize green ammonia plants. 

“Basically, they are trying to put an ammonia plant in a freight container and use our simulation engine to scale down their ammonia process to fit. All these new processes that are coming out of R&D can use this modelling to scale up and move forward from pilot testing to commercial-scale production,” said Reynolds. 

“Our bread and butter continues to be optimization and operability of plants — being able to balance production and cost versus emissions and carbon capture,” he added. 

“I think in the future that’ll start pushing down levels as the AI capability gets better and better. Then, generative AI can start thinking ahead about what we are trying to achieve. As that gets more powerful, some of these other tools might get supplanted. We’re not there yet,” Reynolds said. 

What is nearer at hand, however, is the need for chemical companies to look at data across the boundaries of their whole business — integrating a carbon value chain, for example. 

“The data community is coming together to securely share specific data sets with your customers, your suppliers, your process operators etc. It’s an interesting opportunity, particularly as companies try to tackle Scope 3 emissions. That’s a really difficult, nebulous kind of thing to try and track,” he concluded.

Probing the Product Portfolio

Siemens Digital Industries Software, Plano, Texas, also is incorporating more AI tools in its products. One plan, for example, is to use generative AI as a co-pilot to assist customers throughout the product portfolio.

Toby Hallitt, pre-sales solutions consultant, also urges customers to understand the importance of training in these products. 

Siemens gives all customers access to a self-paced learning platform with bespoke training modules designed to get them quickly up to speed with all the company’s digital solutions. Specialized support is available from dedicated subject matter experts.

“Overall, customers should prioritize solutions which are critical to the company’s business objectives. However, adopting a fully digital approach by incorporating different portfolio elements early on may yield long-term benefits,” he concluded. ⊕