Eight years ago, I co-authored an article around the use of process graphics and how in most cases they are ineffective to increase performance and prevent deviations from the standard. Some recent experiences have once again led me to revisit my thoughts from that time. I am sorry to say that I have seen only slow adoption. My hope is that this article will re-energise the thinking around HMI design. I also want to note that companies that adopted the thinking proposed in the article have achieved amazing improvement as result.
Automation vendors still mostly provide process-type graphic templates to simulate the plant, and only a few provide alternative ‘situational awareness’ type graphic templates. HMI graphics in most control rooms today still simulate the plant in as much detail as possible (so the operator can relate) and try to provide as much information on the HMI as possible (it may be important). These plant control systems present operators with sophisticated animated plant graphics and bombard them with thousands of alarms and events every minute of the day. Although the graphics may look impressive, the operator is flooded with too many values and too much information to process adequately, leading to mistakes or sub-optimal operations.
How do operators make process control decisions?
New research has indicated that a person can only process and react to a certain number of events at a time and that more information will become ‘noise’ or a distraction. This is very apparent when one looks at a typical plant alarm log − thousands of alarms per hour, when an experienced operator can typically only react effectively to around six alarms per hour.
HMI graphics design (it is the human machine interface after all) must have a strong emphasis on the human element and specifically on decision-making within the operating environment. In addition, the effectiveness of decision-making is a function of experience, understanding and interpretation of production process conditions. This is not often realised, and HMI graphics design (after all the clever thinking has been done) is typically left up to the most junior engineer, most often without any standard or guide.
Learning and experience play a significant role in building an operator’s perception and opinion of a specific plant condition and the appropriate actions to take as a result. The correctness of this learning and resulting action is influenced by the complexity of the process the person interacts with. The multiple dimensions, complexity and number of data points for processes are in most cases too much for any single person to easily grasp. Consequently, operators will look for patterns, simple rules and cause-effect relationships to reduce this complexity and make it understandable for themselves and the people they train.
Unfortunately, human minds do not all filter information in the same way, interpret the same information in the same way, or come to the same conclusion given the same information. It is also interesting to note that the possibility of misinterpreting a set of data will not deter a person from reaching a conclusion and making a decision (any flat-earthers out there?). This human trait introduces inaccuracy and bias into decision-making.
Information is defined as communicated or received useful knowledge concerning a particular fact or circumstance. This implies that the data describing the circumstance, event or condition has been interpreted, processed and presented in a useable form. This reasoning also implies that in the absence of a reliable interpretation, presented process data is nothing more than noise in the decision-making process.
Given the above, it is important to not only show process data, but to interpret and convert production process data into confirmed, validated and de-biased decision-making information. In other words, process decision-making is more than merely deciding what the set point should be for a flow rate or temperature. It is about the performance of the total system, and may consist of various measurements. As most plant operators have limited process experience and little knowledge of design principles, it is critical to give them interpreted and unbiased information if you want to ensure effective and optimum decision-making in plant operations.
Understanding what factors contribute to process variance, what the operational decision-making requirements are, and linking different operating conditions to specific performance levels provides a baseline to improve process (and KPI) performance through better decisions.
Improving plant level decision-making using real-time tools
HMIs in plants need to mitigate the process complexity and reduce biased decision-making as much as possible. To be effective, HMI graphics need to be simple and should draw the eye immediately when the process moves outside of the ideal process state, even if not yet in alarm condition. This principle of management by exception through situational awareness should be applied throughout the different layers of the HMI, from dashboard to motor faceplates.
It is thus imperative that HMI graphics standards should be defined that are based on de-biased information and proven relationships between KPIs and real-time process variables. Operators typically fail to detect gradual changes in process measurements over a long time period. The HMI must thus display information in such a manner that operators can clearly notice pertinent changes in the process performance. This will aid operators to detect changes timeously to prevent inefficiency and/or failures. The HMI needs to show simple graphics that immediately catch the eye when situations change (such as reduction in efficiency) or become safety critical so that the appropriate actions can be taken.
The graphics need to be bland, as people can easily be distracted by colour and movement. Flashing colours in abundance become distractions and may cause operators to lose focus. The simple principle for HMI graphics design should be ‘knowing what’s going on around you’ with the objective of preventing abnormal or dangerous situations. The most effective visualisation tools are the ones that give us a broad system perspective according to the principles below.
a) Clarity
• Graphics are easy to read and intuitively understandable (easy to see what is going on).
• Graphics show the process state and conditions clearly (easy to see if the process is under control or not).
• Graphics do not contain unnecessary detail and clutter (less is more).
• Graphics convey relevant information, not just data (provide interpretation of information).
• Information has prominence based on relative importance (easy to see what is important).
• Indications of abnormal situations are clear, prominent and consistently distinguishable (easy to identify out-of-control, dangerous or sub-optimum conditions, for instance using colour).
b) Consistency
• Graphic functions are standardised, intuitive, straightforward and involve minimum keystrokes or pointer manipulations (easy to react).
• The HMI is set up for navigation in a logical, hierarchical and performance-oriented manner (easy to get around).
c) Feedback (interpreted)
• Graphic elements must behave and function consistently in all graphics and all situations (standardised interpretation and functions).
• Important actions with significant consequences should have confirmation mechanisms to avoid inadvertent activation (information interpretation and associated action guidance included in the HMI graphics).
• Design principles should be used to minimise user fatigue as graphics are used constantly (bland with only abnormal or dangerous conditions providing colour).
Benefits of new visualisation standards
Designing HMI graphics according to the principles above will provide the following benefits:
• Provides common, consistent use of colour (colour means something and is not for decoration – this immediately draws attention to indicate abnormal situations).
• Provides condition information (are we running in acceptable limits?).
• Allows you to see abnormal situations at a glance (shape or colour).
• Puts information in context (provides the interpreted information).
• Interprets the process by putting data in context to reference values (e.g. alarm limits, past data, expected values, optimal limits).
• Uses graphical tools such as bars, graphs and trends to give operator perspective and reduce their memory load (what’s going on now vs what happened a while ago).
This allows operators to:
• Detect and react to abnormal or sub-optimal situations before alarms occur.
• Handle abnormal situations better by providing interpreted information and action guidance.
• Complete/resolve abnormal situation tasks faster by providing action steps and guidance.
The above principles highlight the need for confirmed, validated, de-biased and interpreted information that has clearly proved to define factors contributing to process efficiency or safety improvement. If the information is not properly validated and interpreted it will mean that operators will be wasting time reacting to events and variances in the process that will not affect efficiency or safety.
Conclusion
Leading technology providers and system integrators need to rethink the Tools, Templates, Libraries and Alarm Management functionality they provide, taking into consideration human learning, constraints and behaviour. Making use of these tools can assist operations to drastically improve plant operational excellence and safety by providing information to operators in a format that allows them to be effective and efficient. Alarm standards and best practices such as EEMUA 191 and ISA-18 are also available to guide and direct engineers and developers today.
Today more than ever before, the global environment demands operations to operate safely, sustainably and continuously, and strive towards operational excellence. Some technology partners already provide the tools to make this a reality on the plant floor, and system integrators or HMI design engineers are encouraged to adopt this thinking sooner rather than later.
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