Since the 1980’s the field of process control has become increasingly important in chemical and petrochemical plants, oil refineries and other manufacturing units. The widely used and still very powerful tool in process control domain is the PID controller. In order to get optimal performance of any PID controller and to extract the full economic and safety benefits of it, the PID tuning is a crucial step. This paper examines and compares industrial mostly-used old-fashion PID controller tuning methods with the brand new and most powerful PITOPS technology. Old-fashion PID controller tuning methods use Trail-and-Error approach or Empirical sets of rules, whereas the PITOPS technology uses very powerful mathematical NC-GRG (Nonlinear Constrained General Reduced Gradient) optimization approach developed by International American automation and process control company PiControl Solutions. The main goal of the paper is to highlight the benefits of PITOPS over the mostly-used old-fashion methods for industrial PID controller tuning, over several typical examples.
Need for automation and process control was felt back in early 1900’s, old factory mills, distilleries, breweries, mechanical transmission, and in many other examples. Since the 1960’s the field of process control has generated numerous money-saving ideas. Therefore, has become increasingly important in chemical and petrochemical plants, oil refineries and in other manufacturing, units in order to improve and keep stable their entire operation and production. Process control continues to be one of the most fascinating and growing areas with tremendous future prospects related to economy, safety and process stability. Optimal process control strategy can help by:
- Improving product quality
- Increasing production rates of desired products
- Reducing production rates of unwanted by-products
- Reducing consumption of utilities
- Reducing environmental pollution
- More stable plant and equipment operation
- Smoother plant start-up and shut-down
- Increasing automation and modernization
The primary control layer is the backbone in the process control hierarchy and it supports all advanced control and complex optimization applications. Study of the chemical and other manufacturing plants reveals that often the modern, complex advanced control applications receive primary focus and attention and often the underlying bottom-layer, primary control, issues are somewhat neglected because of lesser emphasis.
The heart and soul of a primary control layer is an PID controller. The PID control algorithm is the oldest, yet most popular and widely used control method. Amazingly, the PID control algorithm is unclear and misunderstood by many. If understood clearly, the PID control algorithm can provide, tremendous benefits, control improvements in a simple and robust manner.
A manufacturing plants can have anywhere from few tens to several thousands of PID controllers. No matter how large or small is the plant, PID tuning is a prerequisite for achieving any benefits. Also, changes in process conditions, market or economic conditions, hardware and equipment changes result in a need for changes in the PID control, 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. Guess work on PID controller tuning 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. A plant then could run in this mode with many PID controllers in manual (inactive) mode for years and even decades. Therefore, proper process control PID tuning is increasingly important today in many chemical plants.
The basic PID control algorithm stands for Proportional-Integral-Derivative controller. Each of these terms in most of the times depends on the error (e). Error is the calculation value between the desired process trajectory i.e. setpoint (SP), set by the operator or some advanced process control logic and the actual measurement signal i.e. process value (PV), coming from the field measurement sensor. According to the present and past error values the controller output (OP), which moves final control element (valves, motors, etc.) has been calculated, in order to minimize the error. In the PID control algorithm PID parameters play a key role, where the most effective control action depends on their optimal values, as shown in Figure 1.
|Table 1 : The list of functionalities used in old-fashion and PITOPS technology|
|Applicable to ramp/self-regulating/runaway process||NO||YES|
|Model identification based on OP changes||YES||YES|
|Model identification based on SP changes||NO||YES|
|Model identification using non steady-state data||NO||YES|
|Multivariable model identification||NO||YES|
|Model identification based ultra-short duration data||NO||YES|
|Control valve stiction identification||NO||YES|
|Unmeasured disturbance identification||NO||YES|
|Data preconditioning required||YES||NO|
|Cascade PID tuning||NO||YES|
|Calculation of feedforward parameters||NO||YES|
|Inferential controller design||NO||YES|
|Smith predictor design||NO||YES|
|PID tuning based on the SP change||YES||YES|
|PID tuning based on different disturbances||NO||YES|
|PID tuning based on the valve stiction||NO||YES|
|PID tuning based on the OP rate of change||NO||YES|
|Robustness analysis of PID parameters||NO||YES|