Open Systems, Smarter Chemistry: The Power of Collaborative Automation

Key Takeaways

Open Automation Enhances Chemical Recycling: GR3N’s partnership with Schneider Electric enables a modular, software-defined automation system that adapts to variable plastic waste streams, improving efficiency and eliminating vendor lock-in.
Data Integration Accelerates Digital Transformation: Nova Chemicals and Dow are leveraging AI, digital twins, and predictive maintenance to break down data silos, optimize operations and improve sustainability.
Automation is Evolving Toward Autonomy: Companies are moving toward autonomous operations, using AI, robots, and drones to enhance safety, efficiency, and scalability in industrial processes.

Plastics waste is a mixed bag. The types of plastics in the average recycling bin may contain bottles, packaging and food containers. This poses a challenge for plastics recyclers, like Italian technology company GR3N (pronounced “green”), which must sort and process these materials efficiently. Unlike other chemical processes that rely on fossil-fuel feedstocks, waste plastics introduce significant variability in plant inputs. GR3N has adopted a modular approach to its technology, which the company plans to license to chemical recycling facilities.

The company’s demonstration plant in Chieti, Italy, has three parallel depolymerization lines, two of which process packaging waste and another that handles textile byproducts. Each line includes one of the company’s proprietary microwave-assisted depolymerization, or MADE, reactors that operate based on a specific recipe, said Franco Cavadini, GR3N’s chief technical officer.

Individual production lines and their associated reactors are further fine-tuned through advanced real-time control systems that adapt to feedstock variability and help the company optimize recipes, Cavadini said.

The unpredictability and complexity of handling plastic waste streams present a major technology challenge for the company.

“This pushes the boundaries of automation,” Cavadini explained.

In September, the company partnered with Schneider Electric to create an open, software-defined automation system for the advanced plastic recycling industry that can work across architecture layers, regardless of manufacturer. The system is managed by Universal Automation Organization (UAO), an association overseeing the implementation of a shared source runtime execution engine based on the IEC 61499 distributed control standard.

GR3N's efforts related to an open automation platform for the plastics recycling business is an example of increasing collaboration across the chemical industry to maximize automation potential and interoperability. The partnerships are becoming increasingly important as the industry continues to grapple with decades-old problems that have hindered digital adoption compared to other sectors.

“There's a tendency of a slow adoption to the digital transformation primarily due to the fact that there are a lot of legacy assets in the chemical industry,” said Robert Swim, chemical industry manager for Rockwell Automation’s North American business. “Chemical customers have been embracing automation for the last 20-, 25-plus years. However, a lot of that has reached a point of obsolescence or maturity today.”

OPAF Updates from the ARC Show

In February, the Open Process Automation Forum (OPAF) reported several updates to its goal of developing a standards-based, open process control architecture during the annual ARC Advisory Group in Orlando. The following are some of the notable OPAF achievements highlighted during the conference, as reported by Control Global Executive Editor Jim Montague:

OPAF launched an O-PAS, Version 2.1 certification compliance program in 2024. Early program offerings include the availability of OPC UA connectivity and global discovery services. OPAF has plans to begin offering recognition of verification labs, certification of physical platforms and the integration of network capabilities.

OPAF plans to add several sections to Version 2.1 this year. Among the additions are role-based access and managing certificates, collaboration with OPC Foundation to add OPC UA Field eXchange field-level extensions and the integration of updates to Automation Markup Language to enable software portability and digital engineering.

Read the entire article, “OPAF keeps on plugging, too,” including additional OPAF updates, on the Control Global website.

Software-Defined Freedom: GR3N's Modular Approach

Cavadini worked with Schneider Electric on developing the system from the early planning stages. He acknowledges that building an automation system from the ground up is more challenging for large petrochemical companies like ExxonMobil or Shell. But in the nascent world of chemical recycling, GR3N had the advantage of building a software-defined automation system that would free the company from vendor lock-in, opening more opportunities to fully leverage AI.

“It’s automation, functionally speaking, that is independent from the hardware it’s running on,” Cavadini said. “For us, it means that I don't need to have internal developers specialized on multiple languages. I don't need to keep every new version of the software specialized for different platforms.”

Cavadini noted Schneider Electric’s support of universal automation and the IEC 61499 standard as a key driving factor in the company’s decision to partner with the technology provider. IEC 61499 is a system design language for distributed information and control systems designed to enable interoperability of different types of vendor software.

“It is object oriented; it is easily distributable,” Cavadini said. “So, it's much more powerful as a type of software, which means that I can be more effective in developing our control software with less effort and less problems the moment I go in the field and I do commissioning of a startup.”

For GR3N, the decentralized automation engineering approach reduced engineering costs by 30% and cut human error at the development stage by 40%. The platform's independence from specific hardware eliminates the need for specialized developers across multiple programming languages and platforms, streamlining operations while maintaining complete functionality, Cavadini said.

“Through software-defined automation and hardware independence, we have been able to effectively de-risk our operations and push the boundaries of our technology,” said Fabio Silvestri, GR3N’s head of marketing and business development, when the company announced its collaboration with Schneider Electric and UAO. “We’ve been able to reconfigure our systems quickly when we see opportunities to improve efficiency, while avoiding supply chain issues due the hardware agnostic nature of the system. This is what is needed to make advanced plastic recycling at reality at scale.”

Additional Automation Insights: Past, Present and Future

Billy Bardin, Dow’s global climate transition director, on the impact that automation is having on the company’s sustainability efforts:

“Dow has a long history of deploying advanced automation, advanced control and optimization (AC&O) and real time optimization (RTO) to improve operations in areas such as raw material or energy efficiency applications. Through AC&O/RTO, Dow can improve the performance of our large asset base, pushing the process to constraints that enable maximum performance.

Process models (or process digital twins) are used to identify areas of improvement. Dow has deployed machine learning and AI to help identify new molecules that provide more sustainable solutions to our customers. For example, Dow partnered with CAS to identify more sustainable molecules as candidates to replace incumbents in the pulp and paper industry.

As another example, Dow has developed the Dow Polyurethanes Predictive Intelligence capability as part of digitalization efforts, which supports customers’ material solutions needs.

Machine vision AI has been deployed to help detect fugitive emission leaks in operations, reducing emissions as part of our sustainability efforts. Our automation technologies and approaches are a key foundational element to reliable plant operations that help prevent unplanned events and improve our process safety, which is good for Dow, our communities and our stakeholders.”

Ahmed Musa, Nova Chemical’s digital transformation leader, on the company’s next wave of automation:

“We want to move into autonomous operation for certain aspects of our operation.” Musa added that autonomous operation is intended to automate safety-critical operations, mundane tasks and inspections of critical operating equipment, possibly using a robot or drone to collect the information.

Franco Cavadini, GR3N’s chief technical officer, on what he expects in the next two years as the company moves toward commercialization of its technology:

Using advanced data analytics and artificial intelligence in combination with traditional control systems, the company expects to more effectively manage the inherent variability that comes with plastics waste. He cited, as an example, the ability to compute more advanced recipes optimized for specific waste streams using AI-enabled insights.

Unlocking the Industrial “Google”

Nova Chemicals faced a familiar challenge for process manufacturers when trying to deliver consistent information from various data sources. The Calgary-based polyethylene producer partnered with Cognite, a company that provides data modeling solutions through its Cognite Data Fusion platform. Nova was looking to enable predictive maintenance but faced a common problem seen in many process environments: data silos.

However, as Jason Schern, Cognite’s field chief technology officer explained, making this information useful isn’t as simple as consolidating it.

“If you look at discrete manufacturing or finance or retail, there are pieces of data that are consistent across all data systems that allow you to SQL-join that stuff up in the data lake, but the asset reference and the asset hierarchy doesn't match the asset reference in the work order, which doesn't match the asset reference in the P&ID (piping and instrumentation document). So that's where things get really dicey for us in this space if you want to scale value,” Schern said.

Nova began its digital initiative at its cracker in Geismar, Louisiana, with the intent of creating a decoking analyzer, said Ahmed Musa, Nova’s digital transformation leader. The company was exploring ways it could increase the run time of a furnace without having to double up on decoking at the same time. In steam cracking, coke often forms on the furnace coil, requiring costly shutdowns to remove it.

Scaling the analyzer across 11 plants would have been a difficult task since each site has different data sources, Schern explained. Cognite describes data modeling as a type of industrial “Google,” where users can query information — such as time series data from the historian, asset hierarchy information from the ERP and maintenance history and records from the CMMS — and return with meaningful search results.

The company estimated that it has cut data discovery time by up to 30% since deploying the data-modeling solution. Previously, Musa said, maintenance engineers would have to sift through inspection reports in one database, separately examine the historian for event anomalies and then navigate to the ERP system to examine the latest work orders.

“Now, they can start immediately, look at their equipment and bring all this information in front of them, and they can pick and choose the relevant information,” explained Musa. “At the same time, instead of maneuvering within multiple systems, they can really stay within the same window to do that time-series data looking at their equipment.”

Legacy to Modern: Dow's Measured Migration Strategy

While partnering with technology providers is crucial for digitalization success, the path to digitalization also requires a level of internal collaboration.

Dow is in the midst of a multi-decade plan to migrate legacy proprietary process control systems to commercially available automation platforms.

That’s a significant undertaking when you consider the $43 billion materials science company deployed the latest version of its trademarked Manufacturing Operating Discipline (MOD) process control system in the 1980s across more than 150 operating facilities worldwide, said Billy Bardin, the company’s global climate transition director. (For more information on the history of MOD, read “The MOD Squad: Process automation at Dow,” a six-part series published by Chemical Processing sister publication, Control).

In addition to Dow’s legacy control systems, the company has acquired various older control systems through multiple acquisitions over the past several decades.

“Integration of these legacy systems with modern data and analysis systems present its own unique challenges in order to avail operations of the latest capabilities, such as advanced diagnostics and digital worker capabilities,” said Bardin.

For example, expanding physical infrastructure, such as control rooms, remote buildings, server rooms, and cabling, requires extensive planning and optimization to minimize project expenses, Bardin said.

Dow is trying to ease this transition through a rigorous change-management process that includes operator training well ahead of the actual deployment. The training often includes the use of simulators to provide operators with real-world experience prior to using the new system, Bardin said.

Automation project teams include plant staff members to ensure they’re aligned with operations’ needs.

“Engineers, particularly process automation engineers, such as in our engineering centers, develop high levels of expertise who are brought on as part of the automation project team,” Bardin said. “As part of the change management, reviews are routinely held with the operations team to ensure alignment on approaches and outcomes.”

The teams develop contingency plans to address any risks that may be identified from a risk-mapping or analysis tool.

“After deployment, there usually will be a period of ‘hyper-care’ in which experts from the project teams and automation groups are on call to support any unexpected events,” Bardin explained.

Bardin added that he measures implementation success of new digital tools based on several factors. For one, the deployment team must gain buy-in with the end users and ensure the tools the operations team is using works. Another success factor is whether the digital tool makes their jobs easier.

“The new tool must be reliable and transition support must be provided,” Bardin said. “If the tool fails at 2 a.m. on the night shift and there’s no support, it will not be used.” ⊕