Figure 2 – PID control algorithm behavior
With changes in process conditions, hardware and equipment; there is a
need for PID tuning according to the modified conditions, which are
rarely made, often due to lack of available tools or the required skill
set. On the other hand, many control loops are not fully optimized due
to lack of awareness of the potential and the resulting benefits.
Often the old-age Trial-and-Error, Ziegler Nichols (ZN) and similar
empirical PID tuning methods are used with little chance of correctness
and weak control quality performance. Inappropriate tuning of PID
controller results in poor control quality, often oscillations or
sluggish control followed by the control room operator turning off the
advanced process control schemes or putting the PID controllers in
manual mode. If PID controllers tuning parameters are not adjusted, then
the controller could run in this mode for years and even decades.
Therefore, proper PID tuning is increasingly important for many chemical
plants in present scenario.
For the best performance of the PID controller the values of P, I and D
parameters have to be at their optimum. If PID tuning parameters are not
optimal, control action will be sluggish or oscillatory. Bad PID control
action can reduce product quality, make products sub-prime, off-spec,
prevent production rate maximization and distract the
operators/engineers focusing on the other important tasks in the plant.
The procedure opted to find the optimum values of PID tuning parameters
is known as controller PID tuning or optimization. There are plenty of
methods, tools & theories which are available for tuning of PID
controllers, however finding the best parameters for the dedicated PID
controller is still a tricky task. Mostly used industrial PID tuning
methods are old fashion. In 90% of the cases they are: Trial-and-Error,
Ziegler Nichols, Cohen Coon (CC), IMC (Internal Model Control )
and Lambda methods. Against all of them, this paper introduces brand new
and more powerful PITOPS PID tuning technology. It uses sophisticated
mathematical optimization algorithm NC-GRG (Nonlinear Constrained
General Reduced Gradient ) developed by PiControl Solutions and it will
be compared with previously mentioned old-fashion methods.