3 Dynamics of Spray Granulation in Continuously …
101
1. Model-free controllers using auto tuning [60]. This approach was used for direct
determination of the open loop stability boundaries in closed loop operation as
described in Palis et al. [61].
2. Model-based robust [62] and nonlinear control [63] of a multi-stage FBLG process.
3. Adaptive control of continuous fluidized bed spray granulation with external
sieve-mill cycle [64].
4. Decentralized cascade controllers for continuous fluidized bed spray granulation
with external sieve-mill cycle. Controllers were developed step by step using a
detailed plant model and also validated experimentally [54, 65].
For the latter, the plant model introduced in Sect. 2 of this chapter was extended to
account for the specific plant characteristics of the pilot plant in Hamburg considered
in this chapter, such as
• classifying product removal from the granulation chamber,
• size dependent milling of the oversized particles,
• a variable bed mass, to test different approaches for bed mass control of the granulation chamber with the model.
For the bed mass control, the pressure difference across the fluidized bed is determined as a direct measure for the bed mass to be controlled. Manipulated variable is
the rotational speed of the rotary valve at the product withdrawal from the granulation
chamber. It turned out that the performance of this control loop depends crucially on
the operation of the mill. The mill is used for the grinding of the oversized particles,
which are fed back to the granulation chamber as new nuclei. Stable operation of
the bed mass control was not possible for the standard mode of operation, where
the rotational speed of the mill is kept constant. This problem could be resolved
by introducing another controller to adjust the mill power instead of its rotational
speed. With this, a stable bed mass control could be achieved, which is crucial for
continuous operation of the plant over a prolonged period [65].
However, even for constant bed mass, oscillations of the particle size distribution
and the recycle flow rate can occur as described in the previous section. Such a
scenario is shown in Fig. 24 under the label open loop dynamics. Here, the particle size
distribution shows a very weakly damped oscillation so that the startup of the plant
takes several days, until finally a stable steady state of the particle size distribution
and the Sauter diameter is obtained. For finer milling, the steady state is even unstable
and will never be reached due to permanent oscillations without damping. To solve
this problems and achieve not only stable bed mass but also a stable particle size
distribution, a further control loop was added. Here, the Sauter diameter is determined
online with a Parsum probe and the mill power is manipulated to achieve a stable
given value of the Sauter diameter within short time. The model was used for the
tuning and testing of this controller before it was implemented at the plant. As shown
101
1. Model-free controllers using auto tuning [60]. This approach was used for direct
determination of the open loop stability boundaries in closed loop operation as
described in Palis et al. [61].
2. Model-based robust [62] and nonlinear control [63] of a multi-stage FBLG process.
3. Adaptive control of continuous fluidized bed spray granulation with external
sieve-mill cycle [64].
4. Decentralized cascade controllers for continuous fluidized bed spray granulation
with external sieve-mill cycle. Controllers were developed step by step using a
detailed plant model and also validated experimentally [54, 65].
For the latter, the plant model introduced in Sect. 2 of this chapter was extended to
account for the specific plant characteristics of the pilot plant in Hamburg considered
in this chapter, such as
• classifying product removal from the granulation chamber,
• size dependent milling of the oversized particles,
• a variable bed mass, to test different approaches for bed mass control of the granulation chamber with the model.
For the bed mass control, the pressure difference across the fluidized bed is determined as a direct measure for the bed mass to be controlled. Manipulated variable is
the rotational speed of the rotary valve at the product withdrawal from the granulation
chamber. It turned out that the performance of this control loop depends crucially on
the operation of the mill. The mill is used for the grinding of the oversized particles,
which are fed back to the granulation chamber as new nuclei. Stable operation of
the bed mass control was not possible for the standard mode of operation, where
the rotational speed of the mill is kept constant. This problem could be resolved
by introducing another controller to adjust the mill power instead of its rotational
speed. With this, a stable bed mass control could be achieved, which is crucial for
continuous operation of the plant over a prolonged period [65].
However, even for constant bed mass, oscillations of the particle size distribution
and the recycle flow rate can occur as described in the previous section. Such a
scenario is shown in Fig. 24 under the label open loop dynamics. Here, the particle size
distribution shows a very weakly damped oscillation so that the startup of the plant
takes several days, until finally a stable steady state of the particle size distribution
and the Sauter diameter is obtained. For finer milling, the steady state is even unstable
and will never be reached due to permanent oscillations without damping. To solve
this problems and achieve not only stable bed mass but also a stable particle size
distribution, a further control loop was added. Here, the Sauter diameter is determined
online with a Parsum probe and the mill power is manipulated to achieve a stable
given value of the Sauter diameter within short time. The model was used for the
tuning and testing of this controller before it was implemented at the plant. As shown