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Tadpole is a completely new, breakthrough product to reliably detect oscillations in any process using PiControl’s proprietary TAD (True Amplitude Detection) algorithm invention. No other competitor has access to the TAD algorithm.

Tadpole is an online software product to reliably detect oscillations in important control loops in a chemical plant. Every plant has several control loops ranging from as few as ten in a small plant to hundreds in a large complex that are considered critical. Examples of such loops are reactor temperature, distillation temperature, product purity, surge calculation in compressor, certain important valve positions, motor amps or power and many others. Oscillation of these loops because of change in plant or process conditions or excitement caused by interacting external loops or disturbances can cause the entire process to start oscillating. This can result in lost production because of inability to operate closer to constraints and it can also impact product quality.

Tadpole provides an online window which can be displayed in the control room showing the oscillation status of all important tags. A quick glance at the screen and the engineer/operator is alerted on loops that are oscillating.

The field of online oscillation detection is still very much in its infancy. PiControl’s Tadpole will reliably and unfailingly determine instability and/or hunting as soon as it is mathematically possible. PiControl’s revolutionary TAD (true amplitude detection) software coupled with other iterative online algorithms process the data effectively and fast, and make it possible to reliably detect instability and oscillations like no other technique or software available in the market.

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Figure 1. Hunting and Instability Characteristics


See Figure 1 for an illustration about the capability of Tadpole.

In case 1, amplitudes are growing, this is a sign of impending instability. In case 2, amplitudes are high but constant, rather large; this is a sign of hunting.

Cases 1 and 2 are easier to flag as a problem compared to case 3.

In case 3, there are small peaks and valleys all over, as is typical in a chemical plant signal. Some of the amplitudes are high, but might be passing (temporary). Some of the smaller ones may be real and important.

Most competitor products get confused by the squiggles in the data and can miss real oscillations or report false ones.

Tadpoles’s TAD algorithm guarantees correct identification of oscillations unfailingly, irrespective of the extent of data noise and data squiggles. Tadpole’s™ capability of identifying oscillations reliably irrespective of the nature of the fast noise, medium or slow process drift is totally unique and unmatched by any competitor product.

Tadpole also alerts if the control is sluggish (too slow).

Based on the oscillation and control status, Tadpole generates a status 1, 2, 3, 4 online alarm signal. The signal meaning is as follows:

  1. signals OK status
  2. signals Hunting
  3. signals Instability
  4. signals Sluggish Control

Tadpole can be used to switch tuning automatically on critical tuning loops and also alarm operators, control engineers. Prior to Tadpole technology, detection of the problem could take much longer resulting in possible equipment shutdown, process problems and lost production or quality.

Model Predictive Controls (MPC) can have 10s or 100s of dynamic models. One or more could be wrong. Bad (wrong) Model Predictive Control (MPC) dynamic models produce a bias (model prediction error) between the predicted signal and measured signal coming from the sensor.

How to identify the bad (wrong) Model Predictive Control (MPC) models and how to generate correct models?

This has been a long challenging problem and a nightmare for the process control staff responsible for Model Predictive Control (MPC) quality and performance. Now COLUMBO offers a breakthrough, novel and state-of-the art solution like never before.

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Figure 2. Tadpole Online Program Control Screen


Tadpole can be made to run as fast as every 10 seconds, 1 minute, as slow as 30 minutes or even slower. The online run status screen for Tadpole is shown in Figure 2.

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Figure 3. Tadpole True Amplitude Display Monitor


Tadpole analyzes the signal from historical data and then determines the true amplitudes. The true amplitudes from a real-plant data set is shown in Figure 3.

Figure 4. Unstable, Hunting and Spectrum Display


Loops that are oscillating (either unstable or hunting) are flagged. The spectrum distribution calculation is shown for all tags, as shown in Figure 4.

Figure 5. Tadpole Oscillation Tuning Configuration Screen


DMC3 and Calibrate Function from Aspen

Figure 5 shows the online oscillation tuning display screen for customizing the oscillation detection. Some tags oscillate more than others and this might be natural and unavoidable. To avoid false positives and annoying false alerts, Tadpole allows tuning of each tag so that only the real oscillations are alerted.

Tadpole can be easily made to trigger adaptive control schemes and rule-base control schemes to:

  • Automatically change the modes of various controllers
  • Activate backup control schemes
  • Change PID tuning parameters
  • Change Feedforward or Model-based Configuration Parameters.

PiControl provides free technical support for designing and implementing these adaptive schemes and controllers.

Tadpole is the first truly simple, powerful and practical adaptive control solution for the practical control room environment.

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Matlab ARMAX Box Jenkins Models

  • ARMAX (auto regressive moving average with exogenous inputs) and Box Jenkins models have been used for several decades and are the main modeling methodology provided by Matlab from Mathworks and Labview from National Instruments.
  • These methods do not work well with complete closed-loop data without any step tests at all and/or slave PIDs or MVs in cascade modes.
  • They also do not work well with unmeasured disturbances and noise. These methods offer what they call “noise models” for separating noise from the true process dynamics, but when unmeasured disturbances comprise of a mixture of fast random noise, medium frequency drifts and slow unmeasured disturbances, the noise model concept is not useful.

Model Predictive Control (MPC) Model Identification

  • Most models are based on making small steps of about 1-3% of the current MV values. So, if a setpoint of an MV (a slave PID) is (say) 100 kg/h, then changes from 100 kg/h to 102 kg/h or 98 kg/h or so) are made.
  • If the step changes are too large (say going from 100 kg/h to 120 kg/h and then to 90 kg/h), this may upset the process and the step cannot be help for too long and the process gain of the model cannot be unambiguously determined and there will be a lot of uncertainty in the data. COLUMBO overcomes the problem by analyzing closed-loop data with Model Predictive Control (MPC) running, a function not possible with other current Model Predictive Control (MPC) algorithms and software products.

Identify and Improve Model Predictive Control (MPC) dynamic models with COLUMBO breakthrough Model Predictive Control (MPC) model technology

  • COLUMBO is the latest breakthrough technology for identifying open-loop dynamic models using completely closed-loop data. No step tests are required.
  • Slave PID controllers can be in auto mode and even in cascade mode. How does this work? Please read below…

COLUMBO closed-loop system identification method

  • Currently, Model Predictive Control (MPC) models are built using small steps (1-3% of current setpoints). When the Model Predictive Control (MPC) is running, it uses these models that were identified using the small steps to calculate the trajectory for various MVs in closed-loop mode.
  • When a feedforward changes (measured disturbance changes), the Model Predictive Control (MPC) calculates compensating moves based on the models that were identified using the small steps.
  • The difference now is that a measured disturbance signal (feedforward in Model Predictive Control (MPC) ) may have changed by 20% (not 1-2% like during step tests for model identification) and the larger change (20%) can lead to nonlinearities that cannot be seen during the 1-2% smaller moves.
  • Also, during Model Predictive Control (MPC) closed-loop mode, several MVs are changed simultaneously (in a correlated manner) and this is also different compared to the model identification phase where typically only one MV is moved at a time to avoid complications due to correlations.
  • So, there are two main differences between the Model Predictive Control (MPC) model identification stage (to identify Model Predictive Control (MPC) models) and the closed-loop stage with Model Predictive Control (MPC) running (to calculate MV trajectories in order to keep the CVs at their targets) and these are:

A) Moves are 1-2% of the3 current slave setpoints during model identification stage but could be 1-30% of the current slave setpoints with Model Predictive Control (MPC) running trying to provide control.

B) During model identification stage, only one MV is moved at a time followed by waiting time to try to see the new steady state. This is recommended by all Model Predictive Control (MPC) vendors as a good guideline to avoid correlation problems as if more than one MV (slave PID setpoint) is changed at the same time, then there is ambiguity and uncertainty regarding the impact of the multiple variables and subsequently dynamic models could be wrong due to correlations. However, when the Model Predictive Control (MPC) is running, the Model Predictive Control (MPC) moves various MVs (slave PID setpoints) simultaneously in order to keep the CVs at their targets.

  • The differences between the open-loop model identification stage and the closed-loop stage with Model Predictive Control (MPC) on and running can be significant especially since there is nonlinearity of varying magnitude in all real-life chemical processes.
  • The closed-loop behavior with multiple MVs changing by larger amounts is often different compared to the open-loop behavior with smaller changes and only one MV changing at a time. These differences lead to model prediction errors and is often the root cause of poor control in many Model Predictive Control (MPC) systems.
  • When these problems occur, the fixing and the solution are not easy. COLUMBO was designed to address these problems and help the control engineer come up with improve the Model Predictive Control (MPC) models and thereby the Model Predictive Control (MPC) quality and performance.
  • If after a few months or years, if an Model Predictive Control (MPC) is not performing well and if you turn off the Model Predictive Control (MPC) and resume step tests again in an effort to re-identify the Model Predictive Control (MPC) open-loop models because things have changed the problems caused by the differences explained in the A and B bullets above will still cause a potential error in the real Model Predictive Control (MPC) models when the Model Predictive Control (MPC) is running and moving several MVs simultaneously with larger step sizes.

COLUMBO dynamic process model calculations using complete closed-loop data

How does COLUMBO work? What are details about the COLUMBO algorithm and internals? The COLUMBO algorithm is a PiControl invention and PiControl corporate intellectual property and cannot be disclosed in open literature. All that can be revealed is that COLUMBO tries to identify new models based on the measured data (CVs) and the MV trajectory calculated by the Model Predictive Control (MPC) system or the Advanced Process Control (APC) system. Internally, COLUMBO is equipped with a powerful and new optimizer designed for handling multiple inputs, both analog and digital subject to constraint limits and nonlinear math processing capabilities. Written in C++, COLUMBO code is super-fast and super-compact allowing the optimization calculations to complete and converge in an amazingly short time. See below an overview of how COLUMBO works:

Summary of Unique Functions and Capabilities of COLUMBO

  • Identify multiple dynamic models simultaneously using complete closed-loop data without conventional step tests.
  • Allows fixing all known parameters like time to steady state, dead time or time constant or even process gain based on operator experience/knowledge, engineering calculations, vessel dimensions and vendor data. This unique functionality not available in any other competitor tool allows incorporation of process and engineering knowledge into the building of accurate dynamic models.
  • As-is Model Predictive Control (MPC)/Advanced Process Control (APC) data with slave PIDs in auto, manual or even cascade modes can be used.
  • As many as ten Model Predictive Control (MPC) dynamic models can be identified simultaneously.
  • Open-loop models are calculated using complete closed-loop data.
  • Model Predictive Control (MPC)or Advanced Process Control (APC) system can be on in complete closed-loop mode or off.
  • All calculations are performed entirely in the time domain and not in the complicated and abstract Laplace or Z (discrete) domain).
  • No need for advanced math or process control skills needed.
  • PiControl can easily assist you the customer remotely using data in Excel files and remote support thus saving time and travel costs.

View Columbo brochure.

For technical help and additional details on Columbo, please contact PiControl Solutions Company via email at [email protected]