Smart Instrumentation for the Digital Present

Summary

Five case studies show how diagnostic data used with multivariable process data increases process efficiency and uptime.

Instrumentation selection for plant applications is a multistep process, requiring companies to consider process type, industry standards, approvals, sizing, and more. Although vendor experts and online applicator tools can help specify the right instrument to meet process requirements, digital data capabilities are now another topic to contemplate.

In today’s data-centric landscape, smart instrumentation provides a wealth of diagnostic and other information, so plant staff can get more from their instruments than simple 4–20 mA process variable measurements. Plant personnel can use this information—transmitted via digital communication protocols—to improve plant efficiency and avoid unplanned shutdowns, because they are empowered to implement proactive maintenance and predictive monitoring.

Many factors can interfere with the accuracy of a traditional instrument’s analog output, and operators may not know whether the 4–20 mA current signal processed by a programmable logic controller, distributed control system (DCS), asset management system, or other host system is accurate. Each circuit scales a single process value as electrical current, omitting the ability to transmit secondary variables such as temperature on a pressure instrument. Communication is one way only, so there is no way to send commands from a controller to the instrument.

Traditional analog instrumentation also lacks diagnostic information, making it nearly impossible to foresee instrument failure. These failures can cause unplanned downtime and costly instrumentation repairs in the best cases, or catastrophic equipment damage and safety hazards in the worst. Bound by analog electronics, these instruments must be hardwired to a host system, limiting placement in hard-to-reach areas of a facility, and especially in offsite remote locations.

Optimization made easy

When smart instrumentation is integrated into plant designs, facility operation and optimization become manageable tasks. Smart instruments incorporate digital communication protocols, sometimes in place of—and other times on top of—traditional analog communication protocols. This greatly increases capabilities and value.

For retrofitted and new applications where hardwiring transmitters back to a host system is convenient, instruments can use the two-way digital HART communication protocol. The protocol is superimposed on the analog current loop, so a calibration device or a host system can send and receive data. The data exchanged includes diagnostic, calibration, maintenance, and process information, increasing configuration ease and operational process insights compared to traditional 4–20 mA, analog-only instrumentation.

These HART-enabled instruments can transmit multiple process values to a controller via a single loop. This provides the flexibility to continue using existing analog loops for real-time control, while making process and diagnostic data available via HART for data-driven decisions in the facility.

Data transmission is available via many fieldbus and Ethernet-based protocols. These have many of the same benefits as HART, but typically operate at much higher speeds, so they can include much more information.

Where wired implementations are impossible or inconvenient, wireless smart instruments provide solutions via 2.4-GHz radio wave protocols, notably WirelessHART, WLAN, and Bluetooth. Many smart instruments have these connectivity options natively, and adapters can be added for those that do not. These capabilities can be used to create a mesh network of sensors around a plant and in the field.

WirelessHART and Bluetooth instruments typically send and receive as much or more diagnostic and process data as their wired HART counterparts. Although this data can bring immense benefits to a wide range of host systems and applications—such as maintenance management, asset information and health management, inventory control, and enterprise resource planning systems—many facilities do not take full advantage of what this data has to offer.

Automation systems regularly use flow, pressure, temperature, level, and other process data to monitor and control processes, but they often discard status and diagnostic data. By not using this data, facilities miss out on opportunities to optimize, simplify, and safeguard their plant operations.

When this data is ingested by intelligent plant analysis systems, facilities increase their ratio of proactive to reactive maintenance, thus reducing unplanned downtime, as well as equipment and human safety hazards. For example, instead of waiting to get an alert indicating a high-temperature condition, process data can be used to give an alert when conditions are detected that lead to this type of issue.

By integrating this diagnostic data into host systems, it can be analyzed to give advance warning of instrument failure or a troubleshooting insight in the event of failure. Because calibration and nameplate information are also internally stored in each instrument, tracking and managing assets is easier throughout plant lifecycles.

Consider the following five case studies where diagnostic data was effectively used in conjunction with multivariable process data to predict or identify failures and to increase process efficiency.

Specialty chemicals: Calibrate online

By replacing legacy flowmeters with smart instrumentation, a producer of specialty chemicals used built-in self-diagnostics and in situ verification to reduce calibration costs and process disruptions. The producer must monitor the flow of the individual components within a tank farm. Previously, the mass flowmeters used to make these measurements were manually calibrated each year. This was done by pumping a defined quantity of product from a tank into a rail tank car and validating the weight. 

For each of the 19 mass flowmeters installed, plant personnel were required to take a tank offline, fill a rail tank car, weigh it, drain it, and often dispose of the product, amounting to planned downtime and waste. The exercise also carried operational risk for personnel executing the chemical transfers.

Life sciences: Trust the temperature

A pharmaceutical supplier replaced its steam-in-place (SIP) process temperature sensors with smart instrumentation to increase process accuracy and automate calibration. To maintain high quality and control risks in bioproduction, frequent instrumentation calibration is critical. However, calibrating the old temperature sensors required great manual effort and frequent downtime. By upgrading to self-calibrating temperature sensors (Figure 2), the supplier eliminated these costs and roadblocks to production.

Nitrogen processes: Multivariable accuracy

In the pursuit of a more cost-effective flow measurement technique, a nitrogen services company specializing in the hydraulic fracturing industry standardized on Coriolis flowmeters to accurately measure non-Newtonian fluids. Accurately measuring the flow of these fluids is difficult due to their viscosities, which fluctuate with the changing shear rate. This same characteristic makes accurate measurement important. If equipment is not properly adjusted to the current viscosity before pumping fluid and frac gel down a wellbore, it can cause product, environmental, or personnel harm.

The company’s previous method of compensating for viscosity fluctuations involved a manual measuring process. Although workable, it required intense attention to detail. Operators obtained manual samples every 10 minutes and ran multiple tests to gauge the viscosity measurement and maintain exact product quality.

Because the Coriolis measuring principle operates independently of physical fluid properties—like viscosity and density—the new flowmeters measure reliably regardless of process conditions. Manual testing is therefore not necessary (Figure 3).

Acrylics: Automatically diagnose failure

A designer and manufacturer of acrylic-based products was experiencing a critical error with a flowmeter downstream of a reactor vessel. The instrument sporadically indicated zero flow through the pipeline, causing the process to shut down as a safety precaution. This false reading was eroding business continuity and profitability.

After several manual efforts to troubleshoot the problem, the company turned to the flowmeter’s original equipment manufacturer (OEM) to examine the issue. By running an automated onboard diagnostics verification within minutes of arriving on site, the OEM’s engineer determined the cause of flowmeter failure to be a problem with empty pipe detection (Figure 4).

Water treatment: Handle challenging pipe layouts

Maintaining water safety and quality while lowering energy consumption and meeting city, state, and national regulations is challenging enough, but throwing unreliable measurements into the mix makes meeting these objectives nearly impossible for water and wastewater agencies. With reliable measurement, technicians can better control the process, make better decisions, implement predictive maintenance, and save money and energy.
 

The present and future are digital

When organizations want to optimize plant processes, smart instrumentation provides host systems with indispensable data, which can be used for operational and maintenance insights. This is done using two-way communication from smart instruments to host systems, providing multivariable process values and diagnostic information. These insights help improve process efficiency and help avoid unplanned shutdowns by supporting proactive maintenance procedures.

Whether an organization is far along or just beginning to consider its digitalization journey, industry reliance on digital data and the Industrial Internet of Things is only becoming more pronounced.

All figures courtesy of Endress+Hauser

This feature originally appeared in the April edition of InTech magazine.