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The aim of this research project is to extend the technical-scientific basis for reducing the mercury loads in the exhaust gas path of cement rotary kiln plants by sorbent supported dust removal and to show possibilities for optimisation.
The aim of the research project was to identify those fuel particles which potentially cause the greatest disturbances and to determine their rate of conversion in the flame and in the clinker bed. A further goal was to derive quality criteria for AF, to elaborate recommendations for the optimisation of fuel feeding systems and to develop a tool to estimate the suitability of an AF.
ANICA is a research project which serves to develop concepts of the indirectly heated carbonate looping (IHCaL) process for CO₂ capture from lime and cement plants.
The project investigates the existing 1st generation oxyfuel technology and an innovative 2nd generation oxyfuel concept with the ultimate goal of reducing CO₂ avoidance cost, increasing plant efficiency and strengthening the overall competitiveness.
As part of the research project, the German cement industry is to be provided with a utility model of a new type of apparatus for quasi-continuous incoming inspection of SRF deliveries. Based on a fast, technically robust and efficient characterisation of flight capability, humidity and optically detectable features of SRF, a system shall be developed, constructed and tested in practice.
The aim of the project is to be able to select operating parameters and catalyst materials in such a way that a maximum reduction performance takes place and the function of the catalysts is not affected. This should enable operators of cement rotary kilns, including many SMEs, to achieve a reduction in organic components without having to expect frequent failures of the SCR plant.
The subject of the research project is the fine-tuning of dry cement raw materials. The focus is on the influence on the electrical energy requirement during the comminution of the raw material and the thermal effects on the subsequent clinker burning process, caused by a changed raw meal granulometry.
AI applications or machine learning applications such as soft sensors or assistance systems for production controllers represent promising solutions for process control and optimisation. However, this requires robust AI applications that do not jeopardise the operational safety of the complex production facilities in the cement industry.
