Automation Spending Trends Diverge in Continuous Processing

Aug. 10, 2021
Research shows that digital transformation is the primary driver of automation technology spending across the discrete, batch, and continuous process industries. But which technologies get the most focus when it comes to continuous processing?

Having assessed the responses to our survey of technology suppliers about expected automation purchasing and use trends in the discrete and batch manufacturing industries, we now turn our attention to the continuous processing industries. This survey was designed to help us better understand how automation technology suppliers see their customers reacting to the economic and societal changes that impacted industry in 2020. 

Just as we found overlaps in expected spending trends between the discrete and batch manufacturing industries, a few of the same overlaps exist with the continuous processing industries as well. For example, IoT (Internet of Things) platform software, which ranked in first place for batch manufacturers and third place for discrete manufacturing, tied for first place for continuous processing with data acquisition and analytics (which came in first place for discrete manufacturers and third place for batch manufacturers).

Remote access, which ranked second for batch manufacturers also ranked second for continuous processors. This technology did not make it into the top five for discrete manufacturers. However, cybersecurity, which ranked third for discrete manufacturers, also ranked third for continuous processors.

Rounding out the top five for continuous processors are two technology categories that did not rank among the top five for either discrete or batch manufacturers. These technologies were simulation/digital twin and edge computing.

“These results fit with the idea that digital transformation is driven by advanced process optimization and other data-centric initiatives, and that is certainly what we see from our customers,” said Josh Eastburn, director of technical marketing at Opto 22. “They are seeking faster, more efficient data acquisition options to feed and expand essential process monitoring and maintenance analytics, to connect these systems to others, and to use that data and connectivity to gain new insights.”

Kevin Finnan, Yokogawa industry consultant, noted that nearly all continuous process industry digital transformation programs deploy technologies such as advanced analytics, IoT, digital twins, cloud, and edge technologies. “The primary application of our edge technology is for high-speed processing where the cloud would introduce too much latency. For example, our AI-based pump cavitation solution with IoT sensing has a cycle time of 100 milliseconds. Another key reason for edge deployment is to isolate intellectual property from the internet. To address this issue, many end users are operating proprietary analytics and control algorithms in edge devices rather than in the cloud.”

Pointing to the pandemic as a major driver of remote access technology use in continuous processing, Finnan said, before last year “only a few of our customers were using it. Most of the industry had been avoiding it over cybersecurity concerns. Now, remote access has been incorporated into digital transformation programs. Some users have even realized more benefits than expected. For instance, we’ve conducted numerous virtual factory acceptance tests. Our customers have been able to meet all testing objectives—without the time, costs, and scheduling issues involved with travel.”

“I think [this shows] we are in the data revolution,” said Jesse Hill, process industry manager at Beckhoff Automation in response to a review of the top five spending areas projected for continuous processors. “For instance, most instruments don’t provide just a process variable. They often also provide data on the health of the device and even configuration or parameterization data, all of which is valuable. The problem then is how to make that data useful while also having the bandwidth to transmit that data. This is where technologies like edge computing can be very useful…to process live information and take action on premises rather than having to send massive amounts of data up to the cloud first. This approach reduces bandwidth use and also reduces latency.”

Digital transformation drivers 
Focusing on the perceived benefits driving digital transformation projects in the continuous processing industries, Aaron Crews, director for modernization solutions and consulting at Emerson said, “Continuous processing organizations often embrace digital transformation to increase performance by reducing variability, improve throughput and quality, reduce unplanned downtime, and drive down production costs.” 

To do this, they need access to the critical production data residing in the control system. “Not only is it important to gain access to that data, but it also needs to be moved securely to the enterprise level,” he said. “Legacy control systems may not support the secure digital technologies that organizations need to access all the data that moves through the control system. To enable their digital transformation, many organizations are modernizing their control systems and moving toward new advanced control software that allows them to enhance their levels of automation and leverage techniques like state-based control. Many of them are doing this by digitalizing control systems and adding edge gateways that put data in context and builds a smart manufacturing infrastructure from the field to the enterprise. Once the right people have access to plant data in context—whether in the plant or in the business office—they can more effectively improve control strategies to make the necessary improvements.”

Michael Risse, vice president and chief marketing officer at Seeq, concurred with Crews’ observations about quality, yield, availability, uptime, and throughput driving the digital transformation in continuous processing, adding that sustainability has also joined this list. He noted that Seeq’s customers are increasingly focusing on carbon capture, renewable energy, optimization of energy and water use, as well as greenhouse gas detection and mitigation. 

“Everything comes back to improved performance and looking at the cloud and analytics to make a material difference—and soon—in business and production metrics,” he said. “This is in the context of oil going negative last spring, demand swings due to COVID-19, and the rebound that’s expected in months ahead, [that’s why it’s] time to land innovation to drive impact including cloud via IoT platforms, data with new sensor deployments, and execution at the edge. Of course, across all of this is advanced analytics for insights and improved outcomes.”

Simulation and digital twin
One automation technology area that ranked among the top five categories only in the continuous process vertical was simulation. This technology has been used widely for years in the oil and gas industry as a means of operator training. It has also been adapted for virtual reality immersive training applications. 

Beyond these training uses, the bigger potential for simulation technologies in the continuous process vertical, as well as the discrete and batch industries, is in digital twin applications.

Explaining the higher interest in simulation technologies in the continuous process industries, Beckhoff’s Hill said, “Process control systems have a unique scope and scale in terms of deployment and commissioning. A typical DCS (distributed control system) or control system upgrade or migration requires a shutdown of the process; and anytime a process stops, so does revenue generation. As a result, upgrades and migration projects within the process industry are typically done less frequently and on a larger scale than in discrete manufacturing. Therefore, anything that can be done to decrease the timeline of the shutdown or turnaround is critical. Simulation using MatLab/Simulink and digital twin technologies provide benefits in many areas, such as virtual FATs (factory acceptance tests) and commissioning, to ensure a swift, smooth and efficient upgrade or migration with reduced downtime.”

As to why increased spending on simulation technologies is expected more highly in the continuous processing industries as compared to discrete manufacturing, Emerson’s Crews said, “Much of the choice around simulation comes down to the need to reduce risk to processes and personnel, and those needs break down differently in discrete and continuous manufacturing. Discrete industries are often used to operating with lower overall equipment effectiveness (OEE) and uptime metrics than continuous manufacturing, where 90% or higher uptime is common. There are many strategic levers that organizations can pull in discrete industries to improve operations before they choose to explore simulation. Discrete manufacturers also commonly use less-complex processes with fewer gradations in production quality than continuous processing plants. In continuous processing, simulation is critical for identifying how the repercussions of a changed setpoint will cascade across the process, requiring testing of a wide range of options to determine an ideal. The equipment adjustments necessary to improve performance in discrete processes are no less essential, but they are less likely to have cascading effects across the process.”

Finnan added that several Yokogawa customers continue to operate linear programming models and have recently added artificial intelligence and digital twins. “Operators can use digital twins to create high-fidelity models for performance monitoring, simulation, and optimization to deliver enhanced yield performance, flow assurance, energy-efficiency improvement, enhanced reliability, and operator-capability assurance,” he said. “Understanding when and where products are in demand allows end user companies to adjust production and labor needs while exploiting market opportunities. Data analytics that operators derive from digital twin applications are invaluable when predicting market demand.”

As much potential as digital twin technologies hold for the continuous processing industries, Seeq’s Risse said that some users in this vertical are “frustrated by the digital twin language. They’ve been building models of processes and assets for decades and consider the digital twin language to be marketing hype. With the data in historians and context from manufacturing applications, advanced analytics, and an asset framework—such as OSIsoft PI—there is a lot you can do with the real data, not a twin of it. But asset models in place are too few and far in between; perhaps [increasing interest in] digital twin highlights the lack of asset frameworks.” (Editor's note: the OSIsoft PI System is now known as the OSIsoft and Aveva PI System following Aveva's acquisition of OSIsoft.)

The control divide
When thinking about control systems in continuous processing, distributed control systems (DCS) tend to come to mind first. But the reality is that programmable logic controllers—the common controller used in discrete manufacturing—are used widely in continuous processing as well. The push toward greater levels of open systems use across industries appears to be driving higher levels of interest in PLC use in continuous processing.

“We are absolutely seeing that [increased interest in PLCs versus DCS] across all process industries,” said Beckhoff’s Hill. “Historically, DCSs have been closed, proprietary systems that rely on a specialized workforce. They also limit vendor choices across the process infrastructure, whether it be control and I/O systems, communication media, or instrumentation and sensing. As the convergence of IT and OT organizations and systems continues—with IT having more influence in decisions across the enterprise—process engineers will look even more for open solutions that are scalable and that their current workforce understands.”

According to Emerson’s Crews, much of the recent use of more robust PLCs in processing comes from adopting functionality that “used to be more common in a DCS,” he said. “New and automated connectivity options between PLCs, or between a PLC and a DCS, help users avoid creating islands of automation and stranding data. While the DCS is still the most efficient and effective solution for most continuous manufacturers, PLCs have adapted to a wider variety of use cases than in the past.”

He added that continuous processors are also “embracing controllers designed to operate standalone—for processes that aren’t yet DCS ready.” Crews said these controllers can be “natively integrated into control systems for fast scale-up or scale-out as an organization expands manufacturing. These systems offer many of the flexibility benefits of a PLC and can also provide a single database with a smart I/O infrastructure and distributed control to meet the critical goal of keeping data in context flowing to all levels of the organization.”

Further clarifying continuous processors’ increasing interest in PLCs, Yokogawa’s Finnan said, “The continuous process industries are using PLCs more in OEM, skid-mounted equipment, and process units. While the PLCs are tightly integrated in those units, they interface with the DCS that controls the entire plant process. In our case, we have developed PLC interface applications that allow engineers to considerably reduce the number of project hours required for integration, testing, and commissioning.”

Finnan added, however, that Yokogawa is not seeing a major trend away from DCSs toward PLCs in continuous processing. “The PLCs apply best to non-critical equipment and processes, whereas the DCSs are used for critical processes and those in which downtime is very expensive,” he said. 

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