Neural Networks

Neural networks which "learn" your process can predict product quality for those properties where there are no on-line sensors. The neural network models can then be used to optimize your process. This new technology has matured and packages are now available for industrial processes.

Being able to continuously predict quality delivers the following benefits:

  • less off-spec product
  • early warning of deviations
  • greater output
  • faster changeover times
  • manufacture closer to quality limits
  • reduce costs
  • reduced testing

Neural networks can recognize and match process conditions to QA lab results. The relationships that are identified are updated over time as more process information and QA results are gathered. Thus, processes with variability of raw materials or other process characteristics can learn and adapt.

The accuracy of the predictions is such that the loop can be closed to give real-time process control of the predicted quality parameters.

In fact, any calculated variable can also be used to allow optimization of costs, for example.

Procex has successfully created neural network models to predict:

  • Strength properties of linerboard
  • Curl in white papers
  • Bleach plant pulp brightness
  • Pulp mill Kappa Number
  • TRS emissions from a lime kiln
  • TRS emissions from a recovery boiler
  • Final turbidity in a water treatment plant

The Models can be used to:

  • Investigate relationships between inputs and outputs
  • Optimize the quality and/or cost of production. 

If you are interested in pursuing this application of modern technology, then Contact Procex!

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