The ENOPRIM project, funded by the German Federal Ministry of Education and Research under the codes 03ET1572A and 03ET1572B, ran from 1 October 2018 to 28 February 2022. It was carried out jointly by the Chair of Energy and Environmental Process Engineering at the University of Siegen and Harburg‑Freudenberger Maschinenbau GmbH in Freudenberg. The collaboration involved regular status meetings, detailed exchange of experimental data, and the integration of student theses into the research workflow. The project’s goal was to develop process‑integrated models that enable the energetic and temporal optimisation of industrial rubber‑mixing operations.
The technical work was organised into six work packages. In the first package, the mixing phases were defined and the fundamental mixing process was described. Subsequent packages focused on the development of experimental methods, the modelling of energy input, mixing quality, and batch temperature, and the execution of sensitivity analyses. The final package produced an optimisation model that combined evolutionary optimisation with brute‑force scenarios to determine optimal constant and variable mixing‑speed profiles.
Mathematical models were derived for the net power input, the mixing quality, and the batch temperature. The energy‑input model introduced a parameter α that was calibrated experimentally using a rubber‑process analyser and laser‑induced plasma spectroscopy. The mixing‑quality model relied on statistical descriptors of the dispersion of additives within the rubber matrix, while the temperature model linked the heat generated by mixing to the thermal properties of the batch. These models were validated against laboratory experiments and then applied to a pilot‑scale mixing process.
The optimisation phase produced significant performance improvements. For laboratory‑scale finished‑mixing, an optimal constant mixing speed reduced energy consumption by up to 7 % and mixing time by up to 30 %. When the same optimisation concepts were transferred to an industrial‑scale mixing line, the results were even more pronounced: mixing time could be cut by up to 64 % and energy consumption by up to 53 %. Sensitivity analyses revealed that rotation speed, component temperature, fill level, and stamp pressure each influence the final product quality and energy usage, allowing for a systematic tuning of process parameters.
The project also investigated the impact of varying the mixing‑speed profile. A constant speed profile was compared to a variable speed profile that adapts during the mixing cycle. The variable profile, derived through evolutionary optimisation, achieved further reductions in energy use and mixing time while maintaining product quality. The study demonstrated that a carefully designed speed trajectory can exploit the nonlinear relationship between shear rate and mixing efficiency.
Throughout the project, the partners maintained a close working relationship. The University of Siegen provided the theoretical framework and modelling expertise, while Harburg‑Freudenberger Maschinenbau GmbH supplied the industrial mixing equipment, experimental facilities, and practical knowledge of production constraints. The collaboration was supported by regular workshops, data‑sharing sessions, and joint publication efforts, ensuring that the research findings were both scientifically robust and directly applicable to industry.
In summary, ENOPRIM delivered a comprehensive set of process‑integrated models for rubber mixing, validated them experimentally, and demonstrated substantial energy and time savings through optimisation of mixing speed and other process parameters. The partnership between academia and industry, backed by federal funding, enabled the translation of theoretical insights into tangible industrial benefits.