Key points
The performance and profitability of chemical, pharmaceutical, power and food plants rely on consistency.
In these settings, minimising process variability depends heavily on analytical instruments robustly measuring and monitoring in liquid process parameters. Easy accessibility to intelligent instrumentation diagnostics is the key to proper field device management and greatly improving reliability and even plant safety.
Maintenance of Field Devices in Liquid Processes
In large-scale manufacturing facilities, thousands of instruments and measurements are often involved in running the various processes. Normal wear-and-tear causes field device performance to degrade over time, commonly resulting in process variability.
Like all assets in a plant, automation equipment requires maintenance – liquid process instruments need regular verification, cleaning and calibration, and devices at the end of their life cycle need timely replacement.
This is a great undertaking considering the hundreds of devices and locations that may be spread throughout modern plants. The maintenance of field devices requires a significant cost in man-hours and considerable time is necessary to physically check each field device, especially in plants with limited staff.
Maintenance typically sees technicians physically checking each instrument on a fixed schedule. However, this strategy results in the checking of many devices that are functioning correctly, while also carrying the risk of missing devices that have developed potentially catastrophic failures in between periodic checks. It is largely accepted that routine maintenance does not prevent failures from happening and therefore a higher level of asset management is required to identify all failing devices.
Smart Sensors and Industry 4.0
The ‘Smart Industry’ is seen as the 4th industrial revolution. The set of guidelines coined to promote computerisation in the industrial manufacturing segment is known today as Industry 4.0. The concept was further defined in 2013 by the Working Group on Industry 4.0, which clearly defined recommendations and four main principles.
These principles are:
- Interconnection
- Information transparency
- Technical assistance
- Decentralised decisions
These principals are applied to all assets in the industrial workplace to enable seamless connectivity and transparency for decision making and planning. The industrial network is the backbone in such a system.
Historically, bandwidth has limited the amount of information on any network, and thus has been reserved for critical data. With the introduction of digital networks, bandwidth and connectivity have almost unlimited possibilities.
With the development of HART and fieldbus protocols such as FOUNDATION Fieldbus, PROFIBUS and now industrial Ethernet, a great deal of further information from field analysers can be fed to a platform in parallel to DCS and SCADA systems.
With such a large amount of data being generated, dedicated software is required to collect, organise and present the information in a fashion that is easy to understand and to base decisions on.
Industry 4.0 and the IIoT is Changing Process Plants
The Internet of Things (IoT) is already changing our personal lives in new and innovative ways. The ability to have everyday household objects analyse, observe and interact with us is a valuable tool for improving our lives. Similarly, a key aspect of Industry 4.0 is the Industrial Internet of Things (IIoT) which is bringing the future of modern technology into your process plant.
With the implementation of Industry 4.0 and IIoT concepts, asset management software platforms have become critical for receiving, organising and analysing the vast amount of data generated throughout process plants.
These platforms allow staff to easily prioritise asset maintenance and other interventions based on how critical a task is, as well as its urgency. Access to this data from remote locations also limits the need for technicians to enter hazardous areas for non-critical routine tasks.
Intelligent Maintenance Systems Bring It All Together
The combination of the network, software and plant assets make up a complete example of IIoT: the Intelligent Maintenance System (IMS).
The IMS includes the use of advanced sensors, data collection and data analytical tools. Thus, the system can collect data from machinery and instrumentation to predict and prevent their potential failure. As failures in equipment can be costly and even catastrophic, the system analyses the behaviour of the asset and provides alarms and instructions for predictive maintenance.
Maintenance based on such intelligent information can save upwards of 50% of the costs associated with repairing assets after they have failed and can prevent potentially life-threatening accidents.
Intelligent or ‘smart’ sensors for liquid process analysis fit firmly into the principles of Industry 4.0 by offering robust diagnostics to optimise their maintenance, replacement and inventory planning as well as providing reliable fault alerts.
Process Analysers Need Frequent Servicing
Analytical instrumentation in liquid processes monitoring parameters such as pH, ORP and dissolved oxygen require regular maintenance, cleaning and calibration, especially when compared to sensors measuring simple physical parameters such as pressure or flow.
Eventually, process analytical sensors need to be replaced when no longer measuring reliably. Standardising procedures for instrument maintenance is difficult (and potentially dangerous) as different processes require different sensors dependent on process conditions at the measurement location.
Digital Does Not Mean ‘Smart’
Digital analytical sensors for liquid process streams are rapidly gaining ground over analogue models but being digital does not automatically mean the sensor is intelligent or ‘smart’. Some sensors may store calibration settings, or may simply record their duration of service, but none of this information will contribute to Intelligent Maintenance Systems if the sensors lack predictive diagnostics.
Such simple data does not bring greater reliability than simple routine maintenance. Sensors that simply count down until a maintenance task should be performed generate a false idea of the state of operations or process safety. If a counter has not reached zero, a maintenance engineer may wrongly assume that a heavily compromised sensor is still fit for use.
What Makes Process Sensors and Analysers Truly ‘Smart’?
Intelligent or ‘smart’ process analysers that truly embrace the concepts of Industry 4.0 should use real process information to constantly calculate unambiguous time-based diagnostic values while measuring, namely:
- Number of days until a sensor will require calibration or maintenance – a precise date when attention will be required based on actual process conditions. Costs of calibrating or maintaining earlier than necessary are saved.
- Number of days remaining in which a sensor can confidently be used – calculated in real time. Avoids early replacement or unexpected failures.
- Instant alerts in the event of breakage, instrument fouling etc.
- Verified alarms that use intelligent sensor diagnostics to remove spurious or nuisance control room alerts.
These genuinely intelligent diagnostics will provide clear guidance to inform users exactly when sensor calibration, maintenance and replacement will be required in pH, ORP, dissolved oxygen, dissolved ozone, dissolved CO2 and other analytical instruments.
Visibility of these unambiguous diagnostics can then be provided to asset management system using communication protocols such as HART, PROFIBUS, FOUNDATION Fieldbus, EtherNet/IP and Profinet.
Summary
Industry 4.0 and its advances will continue to be a driver for modern process plants. An inherent part of the strategy is an Intelligent Maintenance System that improves reliability, plant safety and profitability.
Continuous real-time availability of instrument and machine diagnostics form the core of the IMS, while asset management platforms allow instant access to smart field devices such as pH, ORP and dissolved oxygen sensors. This helps accurately and reliably define maintenance tasks to optimise workers’ time, reduce operational costs, drive process safety and ensure reliable measurements.
Only unambiguous, time-based, device diagnostics based on real process data and continuous instrument self-assessment can predict maintenance needs and protect safe, consistent operation of processes. Only online, continuous liquid analysers and sensors that support these intelligent functions can help form the basis of truly ‘smart’ manufacture.
