Advanced Control Smorgasbord

Disturbance measurements were added to or multiplied by the controller output for feed-forward control. Flows were multiplied by a factor for flow feed-forward and blending. The contribution from reset was preloaded and held to reduce overshoot for batch control. Nonlinear controllers were available that reduced the gain or turned off reset when the process variable was near setpoint (within a notch or dead band)and were particularly effective for pH and surge-tank level control. Adaptive control was achieved by adjusting the dead-band width to suppress oscillations. In some cases, the gain could be made proportional to the error to provide error-squared controllers. Dead-time compensators were developed including the Smith Predictor that cancels out the dead time in the response of the variable used for feedback control, Although transportation delays still existed, unless the loop was accelerated toward the speed of light, the elimination of dead time seen by the PID controller enabled users to tune the controller much more aggressively.

Signal characterization of the controller output and input were used to cancel valve and orifice nonlinearity, respectively. Many of these PID techniques were refined for boiler control. Later, signal characterization was also being applied to the controller input to cancel out the nonlinearities of the process variable for neutralizer pH and distillation column temperature control. A whole host of supervisory control techniques were programmed into a host computer. An experienced engineer could employ all sorts of tricks, but each function generally required a separate device, and the tuning and maintenance of these were often too tricky for even the most to accomplished personnel.

Furthermore, the degree of advanced control was set during the project estimation stage because adaptive, batch, feedforward, error squared, nonlinear, override, ratio and supervisory PID controllers had different model numbers, wiring and price tags. If you changed your mind, it meant buying a new controller and scrapping an existing one.

Signal characterization, selection, and calculations required separate boxes and special scaling factors. Only the strong survived. Leftover SAMA block and wiring diagrams and pot settings are the source of headaches to this day.

So Many Choices, So Little Time

In the 80s, the PID hangovers from the 70s became available as function blocks that could be configured at will within the basic process control system. Real-time simulations were developed to test configurations and train operators. The benefits from advanced regulatory control, instrument upgrades, and migration from analog to distributed control far exceeded expectations. Continuous process control improvement became a reality.

Meanwhile, advanced process control (APC) technologies including constrained multivariable predictive control (CMPC), artificial neural networks (ANN), real-time optimization (RTO), performance monitoring and expert systems were commercialized. These new technologies required expensive software packages ($100,000 and up), separate computers, special interfaces, and consultants to do the studies and implementation. The total bill could easily approach or exceed $1 million for a medium-sized project, the biggest chunk being the consultant's fee.

Add to that the fact that the process knowledge needed to not just exploit the system effectively but maintain it disappeared when the consultants left the site. Even so, the incremental benefits from advanced multivariable control and global online optimization over advanced regulatory (PID) control were huge, and enough to justify an extensive deployment as documented in benchmarking studies¹.

APC Integration

At the turn of the century, APC technologies were integrated into the basic process control system². Along with lower license fees, the whole cost of system implementation decreased by a factor of 20 or more with the automation of a variety of steps including configuration, displays, testing, simulation and tuning.

For example, an adaptive control (ADAPT), a fuzzy-logic controller (FLC), a model-predictive controller (MPC), and an artificial neural network (ANN) can be graphically configured and wired as simply as a PID function block. Figure 1 shows how an MPC is set up to maximize feed and an ANN is used to provide an online estimator of pH. A right-click on the MPC block offers the option to create the display for engineering that in turn, has buttons for automated testing and identification of the model for the MPC block.

Figure 1: A Maximum Feed Set Up

 MPC and ANN applications are now as easy to configure as a PID.

Similarly, a right click on the ANN block would offer an engineering display for an automated sensitivity analysis, insertion of delays and training for the ANN block. Model verification is also available to compare the predicted versus the actual response of the MPC and ANN blocks and applications can be launched for performance monitoring and simulation of all blocks.

Now that we have the tools at our finger tips, how do we make the most out of the control opportunities the technologies can deliver?

Feel the Power

The speed at which new APC techniques can now be applied is truly incredible. In the time it took to read this article, an APC block can be been configured. Rapid APC can rejuvenate and empower you to take the initiative and become famous by Friday. Instead of wasting time arguing the relative merits of an APC solution, it can be prototyped via simulation and demonstrated via implementation. Nothing melts resistance to change more than success. Of course, it is still best that the application drive the solution and that a pyramid of APC technologies be built in layers on a firm foundation. APC is not a fix for undersized or oversized valves and stick-slip³.