The Increase of Energy Efficiency in the Rubber Processing by the Reduction of the Mixing Time on the Basis of Data-Driven, Mathematical Models


“Energy efficiency is the driver for automation”, according to a current issue of the VDI-news, where the common goal is formulated as: “Everyone should contribute to making machines and means of transport more energy efficient”. Consequently, that must mean that not only mechanical engineering, but also the process control in the production will be optimized in terms of their energy efficiency. This concerns in particular energetically intensive processes, such as mixing processes which are carried out in discontinuous processes. The enhancement of automation in mixing processes offers a great potential for saving energy, costs and time, and thus, for increasing emission, efficiency, and competitiveness. Discontinuous mixing processes are used in various industries, such as in the rubber and plastics industry, in the food industry, in the manufacturing of paint and adhesives, as well as in the pharmaceutical, cosmetics and general chemical industry.

In the German rubber processing companies up to 10.000 different mixture recipes, consisting of 30 rubber grades, are in use. In particular, from each rubber grade, a wide range of different types with own specifications regarding molecular weight and molecular weight distribution are available in the market. For example, there are 150 types of the rubber grade Ethylen-Propylen-Dien-Kautschuk (EPDM). Moreover, in the field of carbon black fillers alone, 42 different standard types exist. In only one company, 800 to 900 recipes can be applied. The mixture instruction in each recipe is largely developed empirically in order to achieve constant mixture properties from batch to batch for the required mixture quality. Nevertheless, it is not consistently optimized regarding the mixing time and the energy efficiency. The resulting mixture instructions, which have grown over the years, are often no longer questioned or further optimized, as for example on their mixing time. Therefore, there is a great potential for optimization. In a cooperation between the IKV ( and the chair, and within the scope of an AIF project (, this optimization potential is to be increased.

Key objectives of the chair are:

  • Data storage in a feature server
  • Support of the data-driven modelling
  • Development of an OPC UA profile for internal mixer
  • Optimization of the mixing times to reduce energy consumption and process time

Contact: Christian von Trotha