*3.2. Materials*

In circulating fluidized beds, the high mass of particles in the bed has a high heat storage capacity. This property of the bed has a large impact during load following operation. Therefore, it is important to implement the heat capacity and the thermal conductivity of the bed materials with high accuracy, if the model is to reflect the experimental data in good agreement. Seven solid materials are present in the riser: sand, solid fuel, char, ash, lime, limestone, and calcium sulfate. Their heat capacity and thermal conductivity highly depend on the temperature, which varies significantly during part load. In the model, this temperature dependency is implemented for many of the solid materials. The thermal conductivity at 800 ◦C, the temperature-dependent functions for the specific heat capacity and thermal conductivity, and the density of the materials are shown in Tables 6 and 7.


**Table 6.** Thermal conductivity of solid particles in the riser.


**Table 7.** Density and heat capacity of solid particles in the riser.

Tables 8 and 9: The refractory is made of three layers, starting with concrete at the inner layer via calcium silicate in the middle layer and ending with microporous material in the outer layer.


**Table 8.** Thermal conductivity of furnace insulation.


**Table 9.** Density and heat capacity of solid particles in the riser.

#### *3.3. Procedure of Dynamic Simulations*

The long-term steady-state test run with lignite lasted 57 h so that short-term fluctuations are compensated and the data is very reliable. Therefore, the test point is suitable to tune the APROS model to the corresponding solid fuel and the bed properties. The objective of the tuning process was to achieve a high agreemen<sup>t</sup> between the simulation results of the steady-state test and the experimental data, especially concerning the pressure and temperature profile. Mainly nine parameters were tuned, such as the number of calculation nodes, the heat transfer coefficient calculation method, or the global split coefficient between core and annulus and vice versa. Details of the tuning process and the tuned parameters are presented in Section 4.1.

After the model was tuned, validation of the model was done with the experimental data of the part-load tests. Therefore, no additional parameters were adjusted or tuned and only the boundary conditions were modified. The mass flow of solids (fuel and sand) and the air mass flow was modified with pre-defined setpoints according to the experimental data. Figure 3 shows a graphical visualization of the set points in the experiment and the simulation. As in the experiment, the load was increased

from 63% to 88% to 100% followed by a load reduction from 100% to 89% and 68%. For details regarding the boundary conditions during the part-load simulations, see Table 5.
