The post How Do You Measure Control System Performance? first appeared on the ISA Interchange blog site.
For the automation profession to gain the recognition it deserves, we need to show the impact of our achievements on improving plant performance. This question is from Sridhar Dasani, a recent addition to the ISA Mentor Program, with my answer and additional input from co-founder of the program Hunter Vegas (project engineering manager at Wunderlich-Malec) and resource Brian Hrankowsky (consultant engineer at a major pharmaceutical company).
The question by Sridhar Dasani helps us seek a suite of practical metrics in terms of key performance indicators (KPIs) that can have an impact on plant performance. At the end is a link to a Control Talk column that offers metrics more directly related to plant efficiency and plant capacity.
Sridhar Dasani’s Question
As a new engineer supporting plant operations, I regularly attend monthly meetings where the operations team presents its key performance indicators in terms of the production rates, savings, and uptime, and maintenance personnel present their SAP-scheduled maintenance numbers.
I always wonder how an automation engineer supporting DCS system administration or process control can present his/her performance?
Based on your experience, would you be able to suggest what KPIs can be presented during these meetings, anything from system health to loop performance to start with?
Hunter Vegas’s Answer
If the plant is continuous you can look at:
(1) Percent of time the control loops are in their highest mode (e.g., auto, cascade, remote cascade).
(2) Alarm rates (per week, per month). Obviously this is somewhat dependent on the plant operations but if you are doing an alarm rationalization project it gives you a feel for how well you are doing.
(3) If you have made a control system change to the process you can document before/after performance (within range of setpoint, production rate, quality, etc.).
(4) Non-routine instrument work orders generated per month gives you a clue on instrumentation reliability.
Those are some that immediately come to mind. No doubt Greg will have a longer list.
Greg McMillan’s Answer
(5) Percent of time and number of times PID output hits output limit.
(6) Standard deviation of the process variable.
(7) Number of oscillations where the process variable has crossed the setpoint by more than a deadband or noise band (may or may not want to detect noise) forgetting results older than an analysis period.
(8) Peak error for a constant setpoint forgetting results older than an analysis period.
(9) Integrated absolute error (IAE) forgetting results older than an analysis period.
(10) Rise time: time to reach setpoint (within deadband around setpoint) for setpoint change.
(11) Overshoot and undershoot for setpoint change.
(12) Settling time: time to settle out at setpoint (stay for more than four dead times within deadband around setpoint) after setpoint change.
Even better as noted by Hunter is if you could tie what you do to operations KPIs on process capacity (production rate and onstream time) and efficiency (energy and raw material use and waste). This could be done by noting if the KPIs are better because of changes you made or from better loop metrics you achieved. For a view on how to use a virtual plant to develop and demonstrate online KPIs, see the April 2015 Control Talk column “Getting innovation back into process control.”
Brian Hrankowsky’s Answer
(13) How many operator actions are logged in the audit trail/event history?
(14) How long are the “waits” in the process? (How long is the heat up or cool down before the next value added step can take place and what is the duration of prompts lingering for an operator?)
(15) How many batch “hold” events are there and what has been the duration?
(16) How often are interlocks tripping?
(17) How often are estop or shutdown events occurring?
(18) How much paper work does it take to run the process?
Any change that is to be made to a control system must be thoroughly functionally tested by realistic simulations of the process’s dynamic response. The ability of the control system improvement to deal with abnormal besides normal operating conditions must be verified. The commissioning and performance of improvements should be closely monitored to ensure they meet plant requirements.
See the ISA book 101 Tips for a Successful Automation Career that grew out of this Mentor Program to gain concise and practical advice. See the InTech magazine January/February 2013 feature article “Enabling new automation engineers” for the candid comments of some of the original program participants. See the May 2015 Control Talk column “How to effectively get engineering knowledge” with Keneisha Williams, who joined the ISA Mentor Program in 2014, on the challenges faced by young engineers today. Providing discussion and answers besides me and co-founder of the program Hunter Vegas (project engineering manager at Wunderlich- Malec) are resources Brian Hrankowsky (consultant engineer at a major pharmaceutical company), Michel Ruel (executive director, engineering practice at BBA Inc.), Leah Ruder (process systems automation group manager at the Midwest Engineering Center of Emerson Process Management) and Nick Sands (ISA Fellow and Manufacturing Technology Fellow at DuPont).