Ian Elsby, Siemens Digital Industries, Head of Chemical Industry
Nearly 90 per cent of field device information remains unused; Chemical Industry must act now to benefit from Smart Instrumentation data for comprehensive diagnostics and monitoring says Ian Elsby.
It has been almost four decades since ‘Smart Instrumentation’ was introduced in chemical plants and the process industry in general. There have been remarkable advancements since then across industries: the internet technology (IT) and operation technology (OT) are converging and AI is making inroads into every field. Smart instrumentation itself has changed dramatically, providing highly valuable intelligent and timely data for operation and safety in production processes.
As a result, data generation has increased significantly over the years. However, it is also a fact that up to 90 per cent of field device information remains unused. Plants are therefore challenged to take optimum benefit of this readily available data source with comprehensive diagnostics and monitoring.
Some of these steps are:
- Preventing device failure by monitoring the health of field instruments.
- Reducing maintenance costs through outcome predictive analytics.
- Validation of device measurements, with specific insight for process values.
- Comparison of actual and expected measurement ranges
- Detection of unauthorised device or configuration changes.
- Tracking of device replacements and configuration updates over the device lifecycle.
- All this data can be aggregated, viewed and managed via dashboards within the distributed control system (DCS).
In the current scenario where industrial plants only use 10 per cent of smart instrumentation data sets, there remains a valuable unused resource. The fact remains that many plants do have capability within their installed base to extract this data but they choose not to use it to its full potential.
Vendors are working with clients to rectify this situation. Several projects have been carried out to enable companies to gain more from the extracted data and the results have given the industry tools for generating palpable customer savings.
An example of this is a project that was delivered using a Valve Monitoring app which was featured in an automation summit in 2019. The app allowed the plant to optimise its valve maintenance procedures, enabling it to move from reactive to condition-based and predictive maintenance.
The company, which produces a polymer used in the manufacturing of solar panels, needed additional process insights due to them operating two plants with just 35 employees. It was imperative they stayed ahead of the maintenance curve that could slow down or stop production.
Usually in a plant of this type, the valve manufacturers recommend checking the valves at every 100,000 changes. Since the plant size is small it was difficult for the operational team to assess whether the valves need replacing or repair. Using the valve monitoring app enabled them to pinpoint the exact problem and resolve it.
It is now an acknowledged fact that many plants do not fully use the diagnostic data provided by the protocol. This means that the valves are not being routinely maintained according to operating data. In such cases maintenance of the valves is reactive and valves are repaired or replaced only when issues arise.
The app was able to extract the data using the inter-linked DCS to monitor the process solutions installed in the plant. These AI driven technologies allow users to monitor smart instrumentation to ensure preventive maintenance, service due dates, location and criticality.
These technologies empowered the industry with new and effective capabilities. For instance, the client was able to drill down into the valve to see trends and anomalies and add notes for future referencing.
Profibus and more so now Profinet communications are helping advance smart instrumentation capabilities even further.
Profinet allows users to access added value information of multivariable devices. For example, mass flow, density, temperature, totaliser settings as well as diagnostics, can be delivered over a single cable. The connectivity to the DCS, use of analytics and other technologies have proved that up to 40 per cent savings can be made through reduced commissioning time. The time spent on loop identification, device integration and process-loop tuning can also be reduced by 25 per cent in plants.
For now we see some plants and companies are benefitting and operating reasonably well due to reduced production outages and through proactive maintenance. Use of dashboards in a plant to monitor its processes and performance should become second nature to effectively capture diagnostic information.
Smart instruments have the innate ability to monitor their own performance they are primed to capture and measure variable functions they control. While the control systems depend on smart instruments so can the production and maintenance teams to support in preventive maintenance.
Finally, the question is, will the chemical and wider process industry adopt these technologies and use these rich data sources to their true potential?