Even if users can get more data to the cloud than ever before, they still have to process it when they get there, and pick and choose what will allow them to make the best decisions.
“In our everyday lives, we all use data analytics—from personal fitness devices to apps on mobile phones—to improve how we shop, eat and manage our health. The same is happening in the process industries to establish data inconsistencies, identify trends, and analyze the real-time behavior of equipment and plants. The resulting, highly-contextualized information can be used in analytics tools like digital twin simulations to predict future performance, production, emissions and other parameters, so users can take action sooner,” says Monil Malhotra, industrial software VP in the systems and software group at Emerson. “These tools all run faster and provide more intuitive decision support than the manual data entry and spreadsheets we used previously, but they require much more data to be analyzed from many more applications. Plus, data analytics must also run faster to maintain and update the digital twins built on them.”
Because advances in software and processor technology increasingly allow analytics and controls to coexist in the same system, Malhotra reports that Emerson’s Ovation power-control software also includes an Ovation digital twin, while its DeltaV DCS uses Mimic, an Emerson dynamic simulation platform. In addition, capabilities of Emerson’s majority-owned AspenTech business are likely to be integrated with these and other Emerson software packages in the near future.
To cope with today’s multiplying data sources and all the information that’s coming in, Malhotra advises users to prioritize their top objectives, so they can decide what’s the most relevant data they require. “After teams prioritize objectives, they should set up an operations technology (OT) data hub that combines data from sensors and other data sources, stores it on premises or in the cloud, and supports secure data egress to corporate business systems and IT data lakes. The hub also takes the output of the user’s data analytics engines, and quickly gets results to the right people. This lets them compare what’s normal for their process application to what’s actually happening. Then, every few weeks or months, they can gauge if they’re achieving their desired objectives, and determine if those objectives are still valid. This also helps avoid another data tsunami because data isn’t just going to IT and everyone else, but is going only to the right decisionmakers.”
Delivering gas in Colombia
For example, as the largest natural gas transporter in Colombia, Transportadora de Gas Internacional (TGI) in Bogotá safely maintains a 4,000-km pipeline network with a compression capacity of more than 193 hp. Maintenance activity, plant and pipeline shutdowns, and consumption fluctuations require TGI’s staff to handle planned and unplanned operating conditions every day. Impacts of field or compressor plant shutdowns, pipeline maintenance, and leaks or ruptures were typically calculated using complex spreadsheets. However, as it grew difficult to manage these changes with manual documentation on paper or software-based spreadsheets, TGI’s reliability team decided to digitalize and improve decision support by implementing a real-time digital twin simulation of the company’s pipelines (Figure 1).
TGI linked Emerson’s OpenEnterprise SCADA system to its PipelineManager simulation software to create the digital twin, which can be accelerated to let users project future operations. Its predictive analytics models run automatically at 10 to 50 times normal speed, and rely on current operating conditions to let users examine what conditions will be like in six hours, and shows how actions they perform now will resonate across the network and affect future operations. The same predictive model can run on demand to easily test operations changes. And, if TGI’s reliability team wants to push the boundaries of its system, pre-configured conditional alerts will inform operators how many hours it will take before problems occur, such as losing a compressor, compressor plant or injection plant, or other conditions that could cause a network imbalance.
“The digital twin simulation helps our operators predict how the changes they make will impact processes today and far down the line,” says Jesus Vargas Torres, operations management advisor at TGI, “This foreknowledge leads to easier, better decisions, and drives more efficient operations.”
In fact, the team has used its digital twin to predict downstream consequences when a single production site shuts down. The look-ahead model let them determine the time it would take to reach minimal suction pressure at the compressor stations and the time it would take to reach minimal arrival pressure at a site. Using this data, TGI identified an exact window of opportunity for production site shutdowns. This insight lets maintenance crews approach future activities knowing how much time they have to perform tasks, which makes it easier to plan and schedule. For instance, when maintenance needed to stop a compressor station for repairs, the reliability team used the digital twin to identify the time it would take to reach minimal arrival pressure at two customer sites. This informed TGI how long its technicians had to complete the repair without risking breaching contract obligations.
“The digital twin’s ability to calculate exactly how long a maintenance window can last before it impacts our customers is invaluable in helping technicians schedule upgrades, installations or emergency service without interrupting production,” adds Omar Caro Vargas, control room lead engineer at TGI.
The team also uses the digital twin to test hypothetical scenarios for process improvement and disaster preparedness. Before installing devices or changing pressure and flow, engineers can simulate related adjustments in the digital twin to determine in real time how those changes will cascade across the pipeline. The digital twin also let the team simulate and test emergency shutdowns. For example, TGI can test responses to potential emergencies in the simulation, such as rerouting without breaking contracts and activating the proper response teams, and do it without risk to safety or operations.
Finally, simulating with the digital twin let TGI better meet local regulations. The Colombian Regulatory Entity (CREG) requires pipeline operators to communicate with customers before planned shutdowns or restrictions. TGI can now report more accurately to the Consejo Nacional de Operación de Gas Natural (CNOGAS) by simulating and estimating conditions in advance, and confirming them later that day with actual condition data. Also, for customers with varied daily deliveries, TGI can use the digital twin to determine if the network can handle required changes and renegotiate daily requirements as needed.
