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Advanced Process Control (APC) Design Steps

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Phase 1: Benefits estimation

The first step when implementing an Advanced Process Control (APC) strategy is to understand what process units or parts of the units are good candidates for learning the strategy. To be able to quantify benefits and improvement opportunity, it is necessary to first understand the economics of the unit and the production process. It is also necessary to consider the unit by itself, or in many cases, its role within the bigger production value chain [1].

This phase is the most important one in implementing the APC strategy for a dedicated plant process. Mistakes lead to incorrect cost estimates, which will have negative consequences on the APC project [2].

Phase 2: Process modeling and algorithm implementation

After identifying the business case for an Advanced Process Control (APC) project, the next step is to start building and implementing the APC controller.

Modeling process dynamics and configuration of the real-time database and the advanced process controller are required before implementation of algorithms.

First, plants tests are carried out to obtain Advanced Process Control (APC) models for individual process dynamics. These projects have been long and expensive with many defined steps needed for the successful design of these projects [1].

After the completion of plant tests, a real-time database is designed. The next step is to define the communication with the process, determining, and establishing the protocol between a workstation and the DCS. The process models basically are derived from process identification. For the advanced process controller configuration, the manipulated, controlled variables and constraints are defined. The modules designed and programmed are then integrated followed by program tests to ensure the integration of these modules with the real-time operating system [2].

As a result, many production companies were faced with lost margins during the lengthy design phase and disruptions to the process of gathering data to build APC models. Additionally, this process required highly experienced users to build and sustain advanced process controllers [1].

Phase 3: Commissioning

Commission of an Advanced Process Control (APC) project should start after interfacing the associated system with the existing plant infrastructure. First, the communication with the existing plant control system is tested. Next, each multivariable advanced process controller module must be tested in an open-loop model.

For the next step, each control loop is tuned and the controller actions are tested and closed in an advisory model by the operator. It is then closed in automatic mode after each controller is successfully tested. The optimization package can then be commissioned after the multivariable controllers have exhibited the desired performance [2].

Phase 4: Maintenance

The last phase is maintenance, which is required to ensure continued benefits from the implemented advanced process controls. Advanced process control algorithms need regular analysis. When product specifications and process conditions change or new product specifications are added, re-tuning of the advanced process controller is required [2].

In the past, any changes to the process or equipment after deployment would require re-identifying the APC models, which would involve costly step testing and would be handled as a project.

Some companies today have moved beyond the traditional methods to a more advanced technology by integrating adaptive process control with Advanced Process Control (APC) technology. Adaptive process control allows users to experience faster deployments and sustained benefits through continuous models updates in the background with no disruptions to the process. In addition, these tools enable more and less experienced users to deploy and sustain Advanced Process Control (APC) controllers, which can save time and money within the organization.

Additionally, maintaining these controllers requires fewer resources, and as a result, controllers maintain peak performance, which in turn enables companies to deploy and sustain more controllers leading to a best-in-class Advanced Process Control (APC) program [1].

[1] Authors: Tushar Singh & Kate Kulik, Aspen Technology Inc.
[2] Author: Ali Awais Amin, Intech Process Automation

Pi Control Solutions COLUMBO (Closed-Loop Universal Multivariable Optimizer) is the latest breakthrough technology for identifying open-loop dynamic models using completely closed-loop data without any step-tests required.

Advanced Process Control (APC) Design Steps
  • It identifies multiple dynamic models simultaneously using complete closed-loop data without conventional step tests.
  • It 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 is not available in any other competitor tool that allows incorporation of the process and engineering knowledge into the building of accurate dynamic models.
  • It uses Model Predictive Control (MPC) / Advanced Process Control (APC) data with slave PIDs in auto, manual or even cascade modes.
  • It can be identified simultaneously as many as ten MPC / APC dynamic models.
  • It can calculate open-loop models using complete closed-loop data.
  • MPC /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.

Columbo will help you to identify the bad (wrong) Model Predictive Control (MPC) / Advanced Process Control (APC) models and help you to generate correct models. Now COLUMBO offers a breakthrough, novel and state-of-the-art solution like never before.

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