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Project - Data science

Optimisation of the moisture content of slag cement

The challenge

In many processing industries, rotary dryers are used at some stage in the process to dry large amounts of material. Controlling such a dryer presents several challenges. First of all, the dryer consumes a lot of energy, so it is crucial to limit this energy consumption. Second, the humidity of the input material is often variable, but the output humidity must meet specific criteria.

However, in a classical approach, trying to work on one of these two challenges would make the other challenge worse: reducing energy consumption increases the risk that the output material is not dry enough, while reducing the target output humidity increases energy consumption.

The solution

To work on these two challenges at the same time, a model of the drying process is required. The available input and output measurements were used to represent and predict the moisture of the output material. With this model, the control can be adjusted in real time so that the outlet humidity is exactly as required, without drying some batches too much and others too little.

The potential gain

Return on investment, in this case, is measured both in terms of the quality and use of energy :

  • By ensuring constant output humidity, the quality and predictability of the downstream process is improved.
  • By drying the material “just enough” at all times, energy consumption is kept to a minimum.

Project Details

Client Activity

  • Extractive industry
  • Cement/Lime Production

Solution Brand

  • Siemens
  • PCS7
  • Python

Nature of the work carried out

  • Drying/Calcination



  • Decision support tool
  • Software sensors

Notre ThinManager-Integrator Certified

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