Past, Present, and Future of Automation – Part 6 (Operator Interface)

Line “D” of a pet food plant never operates as well as the other lines. Line “A” has the best performance. The operators for line “D” say that line “D” is different and it can’t do better. When a line “D” operator gets sick, a line “A” operator fills in on Line “D”. Line “D” begins to do as well as line “A”.

A builder and operator of ethanol plants puts process metrics on the operator screens for each plant that are viewable by operations at all of the plants. The competitive nature of people kicks in and all of the plants start to do better.

The energy cost for a lime kiln is displayed online. Model predictive control (MPC) is installed and the energy costs drop by 10%. Projects are started to install MPC on all of the lime kilns.

Online process metrics can blow away war stories, motivate operators, increase the on-stream time of advanced controls, justify process control improvements, and develop correlations between key performance indicators and operating conditions. For example, processes may show daily and seasonal performance variations because of the change in feed and cooling water temperatures. Also, process may run better or worse at night and or weekends and holidays depending upon whether automation, maintenance, and process engineers are supporting or distracting and interfering with operations.

However, the implementation is not necessarily straightforward. Process metrics can show us something essential but we may not always like what we see. The president of an MPC company years ago was unequivocally against online process metrics because they may initially take a dive when the MPC is turned on.

I installed online metrics of base reagent cost to show the advantage of adaptive pH control for neutralization of an acidic waste stream. The tighter control increased the reagent costs for disturbances that drove the pH below set point or for increases in the pH set point because the addition of caustic was larger and sooner driving the pH to the set point faster. For disturbances and set point changes in the opposite direction the tighter control decreased the reagent costs. So is tight control right or wrong and are process metrics in this case not useful? If there is a penalty for being below set point, it should be added to the online cost metrics. If not, the controller should be tuned with a lower gain when the pH is below the set point.

Consider a batch operation where the process must be heated up before a reaction occurs. A control system that gets temperature to set point fast will increase the steam use per batch by overdriving the control valve past its resting position. The question is whether the reduction in batch cycle time is worth more than the increase in steam per batch.

You cannot control what you can’t measure. To control plant profitability we need to have the automation system and computations to put process metrics online. Undoubtedly, improvements will be needed to the metrics and to the automation systems that affect them. Filtering and averaging will be needed to screen out noise and delays added to make process inputs coincide with process outputs. New measurements and valves will be needed. Throttling valves with better deadband and resolution can reduce limit cycles. Coriolis meters can provide accurate flow measurements and inferential measurements of stream compositions important for yield, quality, and production rate calculations. Ambient, piping and equipment wall, feed, and coolant temperatures can help provide indications of previously unknown adverse effects.

I see a future where the cost and revenue per production rate, batch, shift, day, night, week, month, and season besides yield and on-stream time are displayed for each production line. Data analytics will be used to develop correlations for projections to latent structures or partial least squares (PLS) to provide predictions of process metrics online and to provide a drill down to contributions most affecting the metrics for better process understanding. The trends and future predictions of these metrics immediately translate to improvements and eventually “closing the loop” for plant profitability. I expect an MPC will be developed to use process metrics as controlled variables and the principal components as manipulated variables.

It seems to me online process metrics are the key for a manufacturer, process control group, and automation company to thrive in a competitive worldwide economy. Loop tuning and performance is just the beginning. As automation engineers we tend to think of the loop as the “end all.” We need to get outside of the box that is the loop to prevent islands of automation. We need to think in terms of unit operation control and how these units interact to affect the process as a whole. We need “oneness” guided by process metrics as introduced in my control talk column. This is the moment.


For a list of some items we need, see slide 57 in my presentation.