EC: PlantPAx ModelBuilder data monitoring and analysis software

Software – Diagnostics: PlantPAx ModelBuilder from Rockwell Automation is a data modeling and analysis tool that predicts real-time process and product conditions critical to peak performance. This is a Control Engineering 2012 Engineers’ Choice honorable mention.


PlantPAx ModelBuilder data monitoring and analysis software. PlantPAx ModelBuilder is a data modeling and analysis tool that predicts real-time process and product conditions critical to peak performance. This is a Control Engineering 2012 Engineers’ Choice finalist.PlantPAx ModelBuilder from Rockwell Automation is a powerful empirical modeling and data analysis tool designed to predict real-time process and product conditions critical to peak performance. Using Soft Sensors, a model that predicts process values based on real-time data, the tool virtually measures variables that are difficult to measure by physical devices, like in-line sensors, due to limited reliability and associated cost.

Providing outstanding reliability and performance, ModelBuilder allows the Soft Sensor to run directly in the PlantPAx controller. By running the model directly in the controller, the Soft Sensor can run as fast (subsecond) or slow as the process dynamics dictate. The PlantPAx ModelBuilder helps provide significant benefits on almost any continuous industrial process. These online predictive models supplement complex analyzers or traditional laboratory measurements and can keep production on track by supplying feedback in minutes rather than hours. This fast time to value decreases off-spec production and raw material waste by identifying process errors before they occur.

PlantPAx ModelBuilder is ideally suited for continuous process industries with highly variable product or feed-stream quality. This includes processes that continuously operate against a single constraint and produce the same product with the same feed at the same production rate. For example, effective control of chemical processes such as reactors and distillation columns requires reliable measurements of key quality parameters, such as polymer melt index or top and bottom compositions from a column. Often, these measurements come from offline analyzers that can take several minutes or hours to get a value. A Soft Sensor uses other process measurements to build a model that infers the quality parameter in real time, allowing for more precise control using basic or advanced process control techniques such as model predictive control (MPC). This allows the process to operate closer to constraints, providing optimized production rates and yields.

Rockwell Automation

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