HC900-Crawler/docs/PITOPS-브레인스토밍-웹페이지복사.md

Continuous Monitoring: Why One-Time Tuning Is Not Enough One of the most common mistakes plants make is treating PID tuning as a project rather than a process. A control engineer tunes hundreds of loops during a turnaround, declares the work complete, and moves on. Twelve months later, process conditions have shifted, valve trim has worn, and a significant fraction of those carefully tuned loops are degraded. Yet nothing in the alarm system indicates this — because degraded control performance does not trigger conventional high/low alarms. Energy consumption creeps upward, emissions rates increase, and the plant reports poor progress against its decarbonization targets without understanding why. [6, 8, 9]

The engineering solution is continuous control loop performance monitoring (CLPM). A CLPM system connects to the DCS or PLC historian in real time and evaluates each loop against a defined set of KPIs — oscillation index, time-in-manual percentage, setpoint tracking error, integrated absolute error (IAE), control valve movement rate, and signal-to-noise ratio. When a loop’s performance grade falls below a threshold, the system flags it for attention, giving the control engineering team a prioritized action list rather than requiring manual trend audits across hundreds of tags. APROMON, PiControl’s AI-based loop monitoring platform, performs exactly this role — connecting to any DCS, PLC, or historian via OPC and distilling over 30 performance criteria per tag into a single intuitive Grade (0–100), while also detecting valve stiction and frozen or noisy sensors before they cause process upsets. [6, 9, 12, 14]

지속적인 모니터링: 왜 일회성 조정만으로는 충분하지 않은가 공장에서 가장 흔히 저지르는 실수 중 하나는 PID 튜닝을 하나의 프로젝트로 취급하는 것입니다. 제어 엔지니어는 턴어라운드 중에 수백 개의 루프를 조정한 후 작업이 완료되었다고 선언한 뒤 다음 단계로 넘어갑니다. 12개월이 지난 지금, 공정 조건이 변하고, 밸브 트림이 마모되었으며, 정성껏 조율된 루프의 상당 부분이 열화되었습니다. 하지만 경보 시스템에는 이런 현상이 전혀 나타나지 않습니다. 제어 성능 저하가 기존의 고/저 경보를 작동시키지 않기 때문입니다. 에너지 소비는 점차 증가하고, 배출량은 증가하며, 발전소는 탈탄소화 목표에 대한 진전이 저조하다고 보고하는데, 그 이유를 이해하지 못하고 있습니다. [6, 8, 9]

공학적 해결책은 연속 제어 루프 성능 모니터링(CLPM)입니다. CLPM 시스템은 DCS 또는 PLC 히스토리안과 실시간으로 연결하여 각 루프를 정해진 KPI 집합에 대해 평가합니다 — 진동 지수, 수동 시간 비율, 설정지점 추적 오류, 통합 절대 오차(IAE), 제어 밸브 이동률, 신호 대 잡음비 등. 루프의 성능 등급이 임계값 이하로 떨어지면 시스템은 이를 주의 대상으로 표시하여 제어 엔지니어링 팀에 수백 개의 태그에 대한 수작업 추세 감사를 요구하지 않고 우선순위 처리된 작업 목록을 제공합니다. PiControl의 AI 기반 루프 모니터링 플랫폼인 APROMON은 바로 이 역할을 수행합니다 — OPC를 통해 DCS, PLC, 또는 역사자와 연결하여 태그당 30개 이상의 성능 기준을 하나의 직관적인 등급(0–100)으로 추출하고, 밸브 고착과 얼거나 잡음이 나는 센서가 공정 문제를 일으키기 전에 감지합니다. [6, 9, 12, 14]

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.

info@PiControlSolutions.com, 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.

Schedule A Demo PITOPS 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. PITOPS_1Pi-control-img-4 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.

Schedule A Demo 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. Figure-6.-Cascade-Simulation-and-Control-OptimizationPi-control-imgPITOPS_2 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. Schedule A Demo PITOPS Question & Answers

What is PITOPS, and how does it support PID controller tuning? PITOPS is industrial multivariable closed-loop system identification, multi-objective PID tuning, and PLC/DCS-based APC design and optimization technology. PITOPS software is used for real-time PID loop tuning and model-based control. It supports DCS and PLC platforms by simulating time-domain control responses using real plant data to deliver optimal tuning parameters and controller performance.

Does PITOPS support both DCS and PLC environments? Yes, PITOPS is compatible with all major DCS and PLC vendors. It offers vendor-specific PID logic emulation and tuning units to match real-world PID loop dynamics.

How does PITOPS ensure compatibility with different DCS vendor PID equations? PITOPS includes a prebuilt library of PID structures and allows users to select or request custom-tuned logic blocks for full compatibility.

What makes PITOPS different from other PID tuning software? PITOPS:

Runs as offline (Excel) instance

Allows system identification based on completely oscillatory closed-loop data

Allows system identification using non-steady-state closed-loop data

Allows system identification based on cascade closed-loop data

Does multivariable system identification using closed-or-open loop data while MPCs are runing

Can do control valve stiction identification and PID optimization based on it

Can do unmeasured disturbance pattern identification

Does PID control loop optimization based on different disturbances

For what is PITOPS used in industrial process control? PITOPS is a control engineering software tool for identifying dynamic process transfer functions from plant data, enabling accurate PID controller tuning and model-based APC optimization using real-time operational data from DCS or PLC systems.

How does PITOPS simulate advanced process control (APC) schemes? PITOPS supports APC design by modelling multivariable process interactions and allowing optimization of cascade control, feedforward, and model-driven strategies using simulation.

Can I use PITOPS to tune cascade and feedforward controllers? Yes, PITOPS supports tuning of single-loop and multiloop controllers with high accuracy and configurability.

Is PITOPS suitable for fast or millisecond-scale loops like compressor surge control? Yes, PITOPS can simulate fast loop dynamics using sub-second or millisecond resolution, making it ideal for high-speed control loops.

Can I use PITOPS to identify transfer functions from plant data? Yes, the PITOPS suite includes a TFI module to identify process transfer functions from real data.

How does PITOPS differ from traditional system identification tools? Unlike frequency-domain tools, PITOPS performs system identification entirely in the time domain, making it more intuitive for control engineers and plant technicians without requiring deep academic control theory knowledge.

Can PITOPS handle multivariable systems and closed-loop data? Yes, PITOPS supports SISO and MIMO modelling using both open-loop and closed-loop plant data, including noisy signals and disturbance events.

What types of transfer functions can PITOPS identify? It can identify first- and second-order dynamic models, ramp-type behaviours, and systems with dead time or non-linearities, including combinations impacted by actuator stiction or sensor drift.

What are the maximum data limits for analysis in PITOPS? PITOPS can process up to 100,000 rows of historical or live plant data, enabling analysis of both short-term and long-duration process behaviours.

How do I improve the accuracy of transfer function identification in PITOPS? Use the zoom and TTSS (Time to Steady State) tools to select relevant process data segments, and initialize reasonable initial estimates for model parameters such as gain, delay, and time constant before identification.

What model validation criteria does PITOPS use? PITOPS uses goodness-of-fit metrics such as FIT (%), IAE (Integrated Absolute Error), and NRMSE (Normalized Root Mean Square Error) to evaluate the accuracy of identified models.

What kind of data format is required for PITOPS to identify transfer functions? PITOPS accepts Excel or CSV files where process measurement (PV), control input (MV), and disturbance input (DV) are structured in columns starting from row 4. The first column is typically a timestamp and is ignored.

What are the system requirements for running PITOPS? PITOPS requires a Windows-based system, minimum 4MB RAM, and 500MB of disk space, and a full HD resolution. It is lightweight PID tuning software.

Is the software compatible with international regional settings? Yes, PITOPS supports global numerical formats and time standards, but for best results, it is recommended to use U.S. English local settings.

Can I select time units in milliseconds, seconds, or minutes? Yes, PITOPS supports millisecond to minute-level time unit resolution depending on your data. Ensure consistency when defining delay and time constant parameters.

What is the difference between CV1, CV2, and MV1, MV2 and MV3 in PITOPS? CV1 is the primary output (controlled variable) used for model identification. CV2 is optional for comparison. MV1 to MV3 are independent manipulated input signals used to model the process behaviour.

Can I save and reuse identification cases? Yes, you can save your work as a .TF project file with embedded notes for future analysis and modification using the “Save Case File“ and “Add Notes“ options.

How is the training structured—live, self-paced, or hybrid? PITOPS training is available on-demand with instructor support via email or live Q&A. It includes recorded sessions, quizzes, and interactive process control exercises.

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PID Control Loop Tuning Consulting and Improvements Home | Services | PID Control Loop Tuning Consulting and Improvements

Request More InFo PiControl Solutions offers PID control loop tuning consulting services to help you optimize the performance of your industrial processes. We can help you achieve the following benefits from PID controller optimization:

Improved process stability Higher product quality and consistency Reduced waste and energy consumption Increased productivity and profitability Enhanced safety and reliability Extended equipment life and reduced maintenance costs PID stands for proportional-integral-derivative, which are the three terms that make up a PID controller, a typical element of industrial automation. A PID controller is a feedback control mechanism that adjusts the output of a system based on the difference between the desired setpoint and the measured process variable. PID controllers are widely used in various industrial process control applications that require precise and stable control, such as temperature, pressure, flow, level, and more.

PID control loop tuning is the process of adjusting the parameters of the PID controller to achieve the best possible response from the system. Tuning a PID controller involves finding the optimal values for the proportional gain, the integral time, and the derivative time, which are the factors that determine how much the controller reacts to the error, the accumulated error, and the rate of change of the error, respectively. The goal of tuning is to minimize the error, the overshoot, the oscillation, and the settling time of the system, while maximizing the process stability, the robustness, and the efficiency of the system.

We have a team of qualified engineers with 30+ years of hands-on control room experience, 500+PID tuning projects done, 10,000+ tuned control loops, 100+Advanced Process Control (APC) projects, who will help you with PID control optimization for your specific application. Our expertise covers many DCSs and PLCs, such as Honeywell TDC3000, Honeywell Experion , Yokogawa, ABB, Emerson DeltaV, Foxboro IA, Allen Bradley PLC, Siemens PLC, and others.

During the tuning project we will analyze your system, define wanted controller behavior after tuning, apply the best tuning method, and implement the optimal PID parameters for your controller. In our control system consulting projects we use our own software tool, PITOPS, superior to any other tool on the market. Using our Control Loop Performance Monitoring (CLPM) software APROMON, we can also provide you with a loop performance analysis that shows the performance of your system before and after tuning, as well as recommendations for future improvements. PID control tuning projects can be conducted as on-site and remote consulting, according to the client’s preference.

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MPC Model Predictive Controller Maintenance Home | Services | MPC Model Predictive Controller Maintenance

Request More InFo Model Predictive Controller (MPC) Maintenance and Improvement Services from PiControl Solutions Please contact us to get free trial software. info@PiControlSolutions.com, Tel: (832) 495 6436

Improve your model predictive controller (MPC) performance using PiControl Solutions software and technology. PiControl offers remote model predictive controller (MPC) maintenance consulting and support. Any model predictive controller (MPC) software – DMC (Dynamic Matrix Control), RMPCT (Robust Model Predictive Control Technology), Predict Pro, Connoisseur, or any other can be improved. PiControl software is called COLUMBO – closed loop universal multivariable optimizer. COLUMBO reads Excel data files containing MV, CV and FF data from the model predictive controller (MPC). Its powerful optimizer then refits existing MPC models and improves MPC models accuracy thereby reducing prediction errors. A fast, powerful, novel, revolutionary method of improving any MPC models using a totally new methodology not available in any other product.

Model predictive controller (MPC) performance may be poor because of any of the following:

1. Incorrect MPC dynamic models.

2. Poor selection of MV and CV variables inside of model predictive controller (MPC).

3. Poor PID tuning for slave PIDs connected to the model predictive controller (MPC).

4. Certain CVs and MVs in current model predictive controller (MPC) that need to be relocated to DCS-based APC (advanced process control).

5. Judicious selection and deletion of dynamic MPC models.

6. Combining process engineering knowledge to improve model predictive controller (MPC) performance.

PiControl can help you in checking your model predictive controller (MPC) and fixing all six above listed opportunities.

Schedule A Demo most-common mpcsay-goodbye COLUMBO just reads offline Excel files, does not need to connect to the Level 3 process control network – so there are no cyber security concerns!

COLUMBO can read Excel files with MV, CV and FF (manipulated variable, controlled variable and feedforward variable) data with the model predictive controller (MPC)ON (active and in closed-loop mode). With the normal changes in CV targets limits (setpoints) or the normal changes in DVs (disturbance variables), COLUMBO optimizer can determine new and improved models with superimposing new intrusive step tests on the setpoints of the slave PIDs. Not needing new intrusive step tests prevents plant perturbations and disturbances and reduces stress and headaches for the control room operator.

analysis-report PiControl can help you improve your model predictive controller (MPC) remotely – no travel costs, all work can be done safely and remotely. Contact PiControl at Info@PiControlSolutions.com and let us help you improve your model predictive controller (MPC) performance today. Increase plant profits and benefits using COLUMBO software from PiControl.