Prozessmodellierung der Kalzination von Aluminiumoxid im Drehrohrofen

• Process modeling of calcination of alumina in the rotary kiln

Kalkert, Matthias; Modigell, Michael (Thesis advisor); Mitsos, Alexander (Thesis advisor); Specht, Eckehard (Thesis advisor)

Aachen (2022)
Dissertation / PhD Thesis

Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2021

Abstract

Rotary kilns are used in a variety of ways in the chemical and basic industries to carry out high-temperature reactions. Typical applications are the production of cement, aluminium oxide or inorganic pigments. Although the rotary kiln is often the most energy-intensive part of the plant, information about the conditions inside the kiln is often not available. Therefore, process reliability and product quality depend to a large extent on the experience of the operating personnel. Within the scope of this work, the structure of a modular process model, which is mechanistic to a large extent, is therefore first described. The heat and mass transfers in the gas phase and solid matter are considered, as well as phenomena of material transformation, such as chemical reactions or sintering. Different types of energy supply are also taken into account, as well as the influence of the dust being blown up. Using the model modules presented, a process model of an existing industrial rotary kiln is set up based on the example process of calcination of aluminium oxide. In addition to the heat and mass transfers, the product quality is also taken into account in the model. In the example process, this is determined by the specific surface of the product. To determine the parameters required for this model module, isothermal sintering tests were carried out in the laboratory. The validation of the process model is carried out on the basis of operating data recorded in a detailed measurement campaign. Finally, a sensitivity analysis of the process model is carried out. It is shown that the example process can be reproduced well with the model set up and that additional process information can be generated that cannot or can only be determined with great difficulty with previously available measurement methods. This includes, for example, the temperatures of gas and solids inside the kiln or the real-time determination of the product quality. Due to the real-time capability of the model, it can be integrated into a model-predictive process controller and thus enable optimised furnace operation.