Successfully Implement the Industrial Internet of Things

First focus on fast-payback projects that solve specific problems

By Dan Carlson, Emerson Automation Solutions

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Suppose the PRVs are scattered across the site and providing proper coverage requires ten wireless gateways, each of which can connect up to 100 wireless instruments. The wireless option not only saves $1.5 million for the initial project but also provides 900 spare input/output (I/O) points for other applications.

If the wireless infrastructure already existed, project cost would drop dramatically — and primarily would involve installing additional wireless instruments — plus some small amount for integrating the information from these instruments into the control and monitoring systems.

In general, a wireless project typically costs 25% less and takes only 25% of the time of one using a traditional wired approach. These wireless projects provide the foundation for an IIoT implementation, as they automatically supply data previously inaccessible due to cost.

Annual savings realized by wireless applications accrue in several key areas: lower cost for compliance; increased production; reduced maintenance expenses; decreased exposure to hazardous environments; and more productive use of staff (by switching personnel from routine inspections to higher-value work).

Many engineers find it challenging to determine these estimated annual savings. An industry consultant or process automation vendor can assist in evaluating the cost-effectiveness and ROI of options by holding meetings with various plant groups, doing a walk-through, evaluating what needs to be done, and providing estimates of annual savings — along with technology recommendations.

Once annual savings are estimated, the next step is calculating costs.

Figuring Implementation Costs

The calculation process starts by understanding the requirements and costs for each application. From these, you can develop general infrastructure requirements that will include leveraging existing infrastructure as well as adding infrastructure as appropriate. The key is a method to see the common requirements and features across many applications for different functional groups in the plant such that the value and costs can be shared.

Traditionally, instrument data came through the automation system used for control, typically a distributed control system (DCS). Other process data were manually collected or ignored altogether. However, when applying the capabilities of IIoT to solve problems outside fundamental control, it’s often simpler and more cost-effective not to go through the control system. A more-efficient IIoT infrastructure can provide this new path, delivering the right information to the right person.

The distributed information system (DIS) model shown in Figure 3 provides a basis for summarizing the general requirements for individual applications and IIoT Infrastructure. Its five boxes indicate equipment or technology, while the dotted lines show potential paths for communication. The DIS model allows a user to understand the fundamental data flows without being bogged down with technology and implementation requirements. This enables calculation of ROI for individual applications and creation of an IIoT architecture.

Let’s look at the five components in the model:

1. Relevant Time Field Connectivity. This component represents the process areas and the three types of raw data: data already connected to the automation system; data stranded in sources like programmable logic controllers, analyzers and third-party skids; and data missing or subject to a manual round. These data are labeled as relevant time data because they’re collected at a rate sufficient for the application, usually slower than the once-per-second rate typical for points connected to the DCS. Generally, WirelessHART instrumentation serves for automating the retrieval of missing data due to cost savings but cellular and Wi-Fi options or traditional wired instruments also can handle the task.

2. Automation System. A large amount of data already may exist in the automation system; augmenting — rather than duplicating — these data often can reduce costs. In some applications, incremental data belong in the automation system but, in most cases, the historian or an asset-class specialty tool should be their end destination.

3. Historians and Analytics. Most sites have a platform that serves as the repository for data from the automation system. Such a platform can be used for processing incremental data from the field; addition of data analytics software can simplify the process of turning data into information. In many cases, all the data required to monitor an asset already exists and the value comes from adding the right data analytics package to create the information.