MULTIVARIABLE CLOSED-LOOP PROCESS TRANSFER FUNCTION DYNAMICS SYSTEM IDENTIFICATION
Pitops-TFI is a software product for identifying process dynamics (transfer function parameters) using plant data. It provides the following powerful features:
- Transfer function identification using closed-loop data, open-loop data or a mixture of both.
- Simultaneous, muti variable identification with multi-inputs. Handles both SISO (single-input, single-output) and MIMO (multi-input multi-output) control problems.
- Identifies Control Valve Stiction or Deadband.
- Equipped with a powerful, highly automated, easy to use software interface.
- Runs all in time domain, no complicated discrete (Z) domain knowledge required.
- Equipped with powerful constrained nonlinear optimizer to identify process dynamics
View the Pitops brochure.
Pitops is the only software product that performs true closed loop system identification with the secondary (slave) PID controllers in Auto mode or even Cascade (Remote) mode. No other competitor tool can do successful transfer function identification using data with PID controllers in Cascade modes (Pitops is the only one). Furthermore, Pitops performs transfer function identification entirely in the time domain whereas all other competitor tools use the more complicated Laplace (S) or Discrete (Z) domain. Pitops can even handle multiple inputs and identify multiple transfer functions simultaneously. Pitops performs multiple input closed loop transfer function system identification in the time domain using a new and novel proprietary breakthrough algorithm that is far superior than the older methods like the ARX/ARMAX/Box and Jenkins methods that are used in competitor tools.
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Identify Process Transfer Functions Easily
Pitops-TFI provides powerful capabilities for designing and implementing Advanced Process Control (APC) schemes. It helps to precisely identify process dynamics required for optimizing, tuning of PIDs, feedforwards, Advanced Process Control (APC) and model-based control schemes.
Figure 1. Dynamics Estimation for Steam—>Temperature Loop
Anyone Can Use Pitops-TFI (both Engineers and Technicians)
Pitops-TFI is so simple that even a new technician or engineer with no prior control knowledge can learn to identify transfer functions using plant data in just about 20 minutes.
Transfer Function Identification Example
Figure 1 shows a transfer function identification between steam flow and distillation column temperature. The red trend with up-and-down steps is the steam flow setpoint. The noisy red trend is the temperature and the blue trend is the transfer function model prediction for temperature.
Figure 2. Simultaneous Multivariable Dynamics Identification using Short-duration Closed-loop Data on Super-fractionator
All Calculations Done in Simple Time Domain
Notice that almost all dynamics identification work can be done simply and easily from this single main Pitops-TFI screen. Since Pitops-TFI works wholly in the time domain, no conversion is needed to go from Z (discrete domain) or S (Laplace domain) to the time domain. Also there is no need to know or understand complicated terms like ARMA, Noise Models, poles or zeros and other complicated academic control jargon. There is also no need to understand cross correlation and auto-correlation theory. Everything is so simple and straightforward for anyone to understand and start using quickly.
What-If Analysis of Transfer Function Process Dynamics
The software also allows you to easily conduct “what-if” simulation studies by specifying guessed values of transfer function parameters and to even compare predicted models with other data sets not used in the dynamic estimation. This feature allows you to quickly and easily check the model accuracy.
Multivariable Closed-Loop Identification using Short Duration Data
Figure 2 shows the simultaneous multivariable closed-loop identification with a remarkably short duration of data. Three input variables (reflux, column feed and reboil duty) shown in the bottom three red trends are changing simultaneously. All three inputs impact the column impurity shown in the top red trend. The time constants for the three inputs are about 45 minutes long. Even though the data window is only about 4 hours long, Pitops-TFI is able to successfully identify all three transfer functions simultaneously. The combined effect of all three transfer functions is the blue trend shown in the top window. Note that this is a powerful illustration showing a true simultaneous, multivariable closed-loop identification.
Figure 3. Control Valve Stiction/Deadband Identification
Control Valve Stiction
Pitops TFI can identify Control Valve Stiction using plant data. The stiction identification location is shown in Figure 3.
Dynamics identification in other competitor products is far more complex, dealing with discrete (Z) transforms. Pitops-TFI makes dynamics identification delightfully simple even for a new engineer or technician. Pitops-TFI is the only product required for all possible primary and Advanced Process Control (APC) dynamics identification in any type of chemical or refining operation.
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PID CONTROL LOOP TUNING SOFTWARE WITH ADVANCED PROCESS CONTROL (APC) AND MODEL-BASED CONTROL (MPC) FUNCTIONS
Pitops-PID is a comprehensive primary and advanced process control (APC) simulator and optimizer. With Pitops-PID, you can build simulations to mimic any process in just a few minutes. For more information on PID tuning and advanced process control (APC) software: View the Pitops brochure.
Please contact us to get free trial software.
info@PiControlSolutions.com, Tel: (832) 495 6436
PITOPS is modern and unique Primary PID Tuning and Advanced Process Control design and optimization software which works entirely in the closed-loop mode without any step-test in the time domain. Unlike competitor PID controller software, Pitops-PID works from fast millisecond scan times to seconds, minutes, and multiples of minutes. This allows simulation from super-fast compressor-surge control loops to very slow distillation column online analyzer-based purity control loops. Pitops-PID has many features. An overview list of Pitops-PID features are shown below:
Figure 1. Transfer Function and Controller Parameters in Pitops-PID
- Single/Slave PID simulation and tuning optimization
- Cascade/multiple cascade simulation and tuning optimization
- Override constraint control schemes
- Random (white) noise, to precisely match the actual noise level seen on DCS
- Slow process drift representative of slow and medium-fast disturbances actually seen in DCS
- Internal model control (IMC) simulation
- Dead time compensated (DTC) controller implementation and optimization
- Feedforward simulation and optimization
- Model-based control using regressed and/or rigorous model
- Production rate maximizer design and parameter optimization
- Millisecond surge control tuning and optimization
- Integrating transfer function tuning and optimization
- First order, second order with dead time simulations
- Open-loop unstable process dynamics simulation and characterization
- Simulation and optimization of nonlinear processes
- Control valve characterization
- Optimizing PID parameters to deal with Control valve stiction or deadband
- Gap action control simulation
Figure 2. Configuring disturbances to match the real process
Figure 3. Cascade Simulation and Control Optimization
Pitops-PID decomposes the total controller contribution into the individual proportional, integral and derivative contributions and plots them individually as a function of the time axis. This provides diagnostics which are useful for critical advanced control loops, especially slow loops with long process dead time where use of derivative can significantly improve controller performance.
Control Objectives and Process Characteristics
The optimal tuning of critical loops must take into account the nature of the process, how fast the control valve can be allowed to move, nature of known and unknown disturbances and other custom issues unique to the loop. Pitops-PID allows you to configure a custom simulation quickly and easily.
Figure 2 shows a simulation comprising of superimposed disturbances like those seen in the real process. After configuring the disturbances, Pitops-PID optimizes the tuning parameters based on the custom simulation, taking into account the control needs of the loop, which include the following:
1. Typical setpoint changes
2. Typical disturbances
3. Output rate of change consideration
4. Any other custom needs specific to the PID loop
5. Optimize PID tuning to handle control valve stiction or deadband
Most other products optimize tuning based on heuristics and error criteria; in contrast, Pitops-PID optimizes based on the precise (custom) process characteristics and control objectives.
Figure 3. Cascade Simulation and Control Optimization
Powerful Multiple Cascade Simulation and Optimization
Pitops-PID provides cascade PID simulation and sequential optimization capability to optimize both slave and cascade controllers – see Figure 3. A single, easy-to-use master screen allows you to specify most parameters.
Signal Transforms, Analyzer PV Sample Delay and NonLinear Control
You can specify slow loops, GC analyzer sample time delay and special transforms like natural logarithms, square and square root to linearize commonly known non-linear processes. These transformations are used for constraint control for distillation column delta pressure to infer column flooding limits and also for tighter control of tall superfractionators where the distillation purities behave non-linearly.
Pitops-PID provides calculation procedures and commissioning guidelines to design and implement feedforward controllers.
Most other competitor software products run in the discrete (Z) domain. In contrast, Pitops-PID runs completely in the time domain which is easier to understand by people of all skills and experience levels.
Figure 4. Feedforward Simulation and Parameter Estimation
Feedforward Control Parameter Optimization
Pitops provides powerful, easy to use feedforward simulation module. Many skilled control engineers have admitted that they began to truly appreciate some important aspects of feedforward control design only after being exposed to Pitops-PID. Pitops-PID automatically optimizes controller parameters for a closed-loop simulation for a real-plant case configured with a disturbance and feedforward model precisely matching the process dynamics. The feedforward trends in Pitops-PID are shown in Figure 4.
Figure 5. Internal Model Controller (IMC) and Dead Time Compensation (DTC)
Powerful Multiple Cascade Simulation and Optimization
Powerful model-based control schemes can be built in the DCS using Pitops-PID suite of model-based controller design, see Figure 5 for an illustration.
Using regressed, empirical, semi-empirical or rigorous chemical engineering models, effective model-based dynamic controllers can be easily implemented. The procedures show how to build controllers at a fractional cost and effort compared to other options.
Pitops-PID is the only tool that any control engineer will ever need in the control room. All simulations can be built very easily in a matter of minutes. This ease of use is unmatched by any other competitor software. Both new and skilled engineers and also DCS technicians/operators have successfully used Pitops-PID with ease and confidence.
Control Valve Stiction/Deadband
Pitops optimizes PID tuning parameters to improve control action amidts control valve problems such as stiction and deadband. For control valves with stiction or deadband, Pitops will move the controller settings in the direction of increased proportional and derivative action and reduced integral action.