Model Predictive Control software

Model Predictive Control software helps you reach a stable production process

Model Predictive Control definition

Have you ever wondered the difference between Advanced Process Control and the Model Predictive Control definition? MPC is a technique under the APC umbrella. Model Predictive Control (MPC) software is the most popular control technology in the industry. Model Predictive Control is a model of the process to predict the plant’s behavior in the foreseeable future. Typically several hours ahead.

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Model Predictive Control basics

An APC application performs the following steps every minute, over and over again, 24 hours per day, 7 days per week:

The read step

In the so-called “Read” step, it starts by reading a long list of current process values from the DCS. This data is used to evaluate where the process is currently operated, and to see how the process operation has changed since the last calculation by Advanced Process Control (APC) was performed. The APC application keeps a record of history data, so it knows not just the process values that it has read this cycle, but also remembers all the process variables it has read in recent history (typically several hours in the past).

The prediction step

In the “Prediction” step, using the data it just read, and using data from recent history, the APC application calculates where the key process variables will drift to in the next several hours. This prediction is calculated based on the process model, and is continuously adjusted based on the actual values read from the DCS, so the APC application adapts the predictions every minute based on the actual measured behavior.

The optimization step

In the “Optimization” step, the APC application calculates the optimal operations point where the process should be operated. This calculation is done based on input provided by the end-user in the form of economic information. Having determined the optimal point of operation, it then compares the actual and predicted process values with this optimal point of operation, and calculates how to shift the process from its current operating point to the optimal operating point. The change is calculated whilst taking the limitations into account, i.e. the APC application will ensure that no process variables exceed their limits.

Model Predictive Control definition-steps

The final step

In the final step, called the “Write” step, small changes are sent to the setpoints for key process variables. It’s important that these changes are kept small. This will assure smooth operation of the plant.

This approach makes sure that the plant is optimized on a minute by minute basis. If disturbances hit the process, and/or if end-users change the specifications of products or limitations on some key process variables, the APC application will calculate the new optimal operating point for the process, and shift the process towards the new point. The plant is operated in a very consistent way; APC continuously calculates the right adjustments and implements them very consistently. Any variations between shifts are removed, and therefore an APC application is often referred to as a “Best Practice Operator”. And last but not least, due to the small adjustments being continuously made to the key setpoints of the process (as opposed to the larger adjustments normally made by operators), the overall stability of the plant is increased significantly.

Advantages of MPC

What is APC? Benefit creation

The main advantage of model predictive control is that it is able to anticipate the future. Knowing what the limitations of the process are (safety limits, operating limits, and quality specifications), MPC will calculate the optimal adjustments to be made to the process. It will implement these adjustments, by sending small changes to the setpoints of key process variables in the DCS.

The process limitations are specified by the operators and/or process engineers. They enter high and low limits for every process variable that is included in the application. These limits can be adjusted to suit the operational targets, i.e. quality limits can be adjusted in accordance with planning demands, operational limits such as maximum allowable temperatures can be adjusted based on catalyst lifetime, and so on.

The main advantages of MPC are:

  • The MPC concept is very intuitive. This boosts acceptance by operators and engineers
  • The MPC concept is very similar to how your most experienced operators operate a plant
  • Operating limits can easily be integrated in the control concept
  • It is easy to implement the optimization targets (less energy, more production,…) in the MPC controller
  • There is no real limit to the size of an MPC controller. There are ethylene cracker APC systems with 500 MV’s and about 1000 CV’s

Software: MPC implementation

It has been proven many times in the last 40 years that implementation of Model Predictive Control results in many benefits. Paybacks of months and weeks can be achieved when you follow the best practices for MPC implementations:

  • Be sure the process is APC ready. This means a good level of instrumentation, good performing actuators, a DCS with OPC interface and with optimally tuned PID controllers
  • Execute a pretest, checking all the above. Spend time to make a correct functional design. Use the keep it simple principle
  • Spend enough time on step testing. A good model is key for the performance of an MPC controller. Step all MV’s at least 5 times. Use automatic testers when useful
  • Use all your knowledge during the modeling phase
  • Introduce your operators asap to the MPC technology
  • Commission the controller for summer/winter, low/high load conditions
  • Organize the maintenance of the MPC controller. The benefits will be generated every minute/hour/month/year only if maintenance is organized.

Predictive Control software: INCA MPC


The Model Predictive Control software, INCA MPC is a suite of tools that allow the easy development and deployment of an APC application:

  • INCA Acquire: a quick and easy tool to start data collection from your DCS
  • INCA Discovery: modeling package that allows you to build dynamic models based on plant data, with the ability to include prior knowledge about the process in the identification cases (for example, known gain ratios, known delays, etc)
  • INCA Engine: the online APC engine that optimizes your plant operation on a minute-to-minute basis
  • INCA Sensor: a soft sensing tool allowing estimated product qualities like concentrations, Melt flow rate, Particle size distribution, … in real-time. Visit the INCA Sensor page
  • INCA Webviewer: the web-based operator and engineer interface to the control application. Access and authorization levels are role-based and fully configurable

We’re happy to help you with your Advanced Process Control (APC).

Knowing the definition of Advanced Process Control and how it can benefit your plant can make a difference to reach a stable production process.

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