PiControl Solutions

Multivariable Closed-Loop System Identification, Multi-Objective PID Tuning, PLC/DCS-based Advanced Process Control (APC) Design & Optimization, and Model Predictive Control (MPC) Maintenance Technology

Necessary Technology for all Process Control and Automation Engineers

View the Pitops brochure.

Please contact us to get a free demo on PITOPS - Multivariable Closed-Loop System Identifier, PID Tuner, Advanced Process Control (APC) Designer and Optimizer - Artificial Intelligence (AI) based Algorithm.

[email protected], Tel: (832) 495 643

Many PID control loops are not fully tuned and optimized due to lack of awareness of the potential and the resulting benefits. If PID control loop does not perform optimally, it can result with product quality reduction, off-spec products and other. Overall, the studies show that between 65 to 85 % of PID control loops can be improved. If PID control philosophy is understood clearly, it could provide tremendous benefits, control improvements and a financial rise. Knowledge of System Identification (or Model Identification) gives powerful capability to any process control or chemical engineer to:

  • mathematically calculate PID tuning parameters for any controller
  • mathematically calculate PLC/DCS-based Advanced Process Control (APC) parameters
  • mathematically calculate new Model Predictive Controller (MPC) models
  • mathematically simulates any what-if process control scenario and outcomes

Optimal PID controllers, appropriately designed PLC/DCS-based Advanced Process Controllers (APC) and/or Model Predictive Controllers (MPC) can help any industrial plant to:

  • Maximize production
  • Minimize utilities
  • Minimize waste and off-spec product
  • Reduce unplanned shut-downs
  • Provide faster grade transitions
  • Achieve faster new conditions
  • Improve stability and increasing safety
  • Assist operator to avoid mistakes
  • Improve automation level

There are a few ways how to do proper system identification and certain tools are available for this purpose, but unfortunately most of the them needs step-tests on PID OP in Manual mode (Open-Loop step-tests), and a few of them can do it by having-step tests on PID SP in Auto mode (Closed-Loop step-tests). These step tests many times are not possible, time-consuming or plant intrusive, and engineers and operators do not like them a lot. During steps-tests the plant many times will be in upset or spec-off mode, or even working at lower capacity than usual.

PITOPS from PiControl is the only technology across the globe which can do multivariable complete closed-loop system identification. The ability to identify open loop process models using completely closed loop data with PID control loops in Auto or even Cascade mode, and with APC schemes active or MPC model predictive controller ON and active is a unique and novel offering from PiControl, state of the art technology. Such functionality is helpful not only for PID tuning optimization for making PID tuning improvements but also to improve APC performance by optimizing APC parameters (like feedforward, inferential, constraint override and other APC parameters), and to maintain and improve existing Model Predictive Control (MPC) models.

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The model improvement functionality uses the new and improved Artificial Intelligence (AI) based algorithm optimization technique that proves to be superior to FIR (Finite Impulse Response), ARMAX (Auto Regressive Moving Average Models with Exogenous Inputs) Box Jenkins methods, which are commonly used by all other competitors including Honeywell, Aspen, Emerson, Yokogawa, MathWorks – all process control design, DCS vendors and PLC vendors. It is capable to automatically identify one or several unmeasured process disturbances, isolate their pattern, display as a trend, and save in the Excel. Works well admits fast random noise, medium frequency drifts and slow unmeasured disturbances.

PITOPS can take past data from any PLC or DCS historian during start-up/shutdown or normal plant operation and without any additional time-consuming and intrusive step tests can identify true and accurate process models from:

  • PID loops being complete in Auto or even in Cascade mode (where the user does not have to break the Cascade chain or set the loop in Manual).
  • Open-loop (step changes of PID OP) or complete closed-loop data (step changes of PID SP).
  • Ultra-short duration data (1/5th of data).
  • Data having process or equipment nonlinearities involved.
  • Complex and ugly closed-loop data without any need for data preconditioning (resample, high noise, missing data, outliers, etc.).
  • PID loops impacted with high noise and unmeasured disturbances in Auto/Cas mode.
  • PID loops having valve issues (stiction/hysteresis) and running in Auto or Cas mode.
  • PID loops being completely oscillatory (unstable) in Auto/Cas mode.
  • Running MPC controller.

All mentioned options above reduce intrusive and time-consuming plant step-tests and save the plant of running in undesirable conditions.

On the other hand, engineers still like to use not so effective old fashion PID tuning rules, where they need to conduct many time-consuming and intrusive plant step-tests, break a control chain, switch control loop modes, and eventually hope that during step-testing time plant will not be hit by unmeasured disturbance which will disturb the plant and ruin performing plant step-tests.

Also, each PID control loop has its own purpose and objective. The optimal tuning of critical loops must consider the nature of the process, how fast the control valve can be allowed to move, the nature of known and unknown disturbances and other customs issues. In any industrial plant there are PID control loops which:

  • Do not change their setpoints regularly (mostly barely)
  • PID loops which have continuous and dominant disturbances
  • PID loops which have complex setpoint trajectory changes (like all Cascade PID loops)
  • PID loops which have valve issues
  • PID loops which valves cannot be changed abruptly since it may cause some serious downstream upsets

Therefore, it is unreasonable to tune all PID control loops only based on the step setpoint change, like many tools do. This simplified PID tuning on a typical step setpoint change many times will produce poor PID control loop behavior sudden and unexpected process disturbances rejection, and/or control valve mechanical issues.

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Except being able to do powerful system identification, PITOPS can also do multi-objective PID loop tuning. It can accurately tune PID control loops based on the following multi-objective approach:

  • Step/ramp or even complex SP changes (Cascade loops)
  • Disturbances (Pulse, Step, Ramp, Sine)
  • Noise
  • PID OP rate of change
  • Control valve stiction

Non-linear process gains and/or process dynamics

Now, process control user/expert can finally get optimal PID tuning parameters based on real PID loops needs and objectives, and stop the usage of trial-and-error PID tuning.

PITOPS also provides powerful capabilities for designing and implementing Advanced Process Control (APC) schemes inside of any PLC or a DCS. It helps to precisely identify process dynamics required for optimizing the following schemes:

Multiple cascade PIDs - It can optimize both slave and cascade controllers.

  • Split range control
  • Ratio control
  • Fan-out control
  • Inferential control
  • Deadtime compensated (DTC) controller
  • Internal model control (IMC)
  • Production rate maximizer controllers
  • Discrete slow loops, like GC analyzer sample time delay
  • Special transforms like natural logarithms, square and square root to linearize commonly known non-linear processes, such as for constraint control for distillation column delta pressure to infer column flooding limits and for tighter control of tall Superfractionators where the distillation purities behave non-linearly.
  • Feedforward controllers – It automatically optimizes controller parameters for a closed-loop simulation configured with a disturbance and feedforward model precisely matching the process dynamics.

Some of the distinguishing and powerful features of PITOPS are listed below:

  • Simultaneous, multi-variable identification with multi-inputs, handles both SISO (single-input, single-output) and MISO (multi-input single-output) control problems.
  • Identifies Control Valve Stiction or Deadband.
  • Runs all in the time domain, no complicated discrete (Z) domain knowledge required.
  • Equipped with the powerful constrained nonlinear optimizer to identify process dynamics.
  • 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.
  • 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.
  • Optimizes PID tuning parameters to improve control action amidst control valve problems such as stiction and deadband.
  • Possesses all commercially available PLC/DCS PID algorithms. PID equation may be in ideal, interactive, parallel, series, integral only, proportional and derivative only and other different formats. PID equations format may be using error or PV on the proportional action and/or derivative action. All PLC/DCS forms of PID equations are supported.
  • Using regressed, empirical, semi-empirical or rigorous chemical engineering models, effective model-based dynamic controllers can be easily implemented.
  • Integrating, first-order, second-order, and open-loop unstable with dead time transfer function simulations and identification possible.
  • Simulation and optimization of random (white) noise, precisely matching the actual noise level seen on PLC/DCS.
  • Control valve characterization and Gap action control simulation.
  • Can be also used for process control training for process control engineers, process engineers, DCS and PLC technicians and for process control semester classes at colleges and universities.
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