Developing successful closed-loop control
A reliable solution for continuous real-time particle-size measurement provided the opportunity to prove the concept of fully
automated mill control. Steps were taken to integrate the mill and analyzer and to work toward the goal of full automation.
The Insitec computer provides a link between the mill programmable logic controller (PLC) and the on-line particle-size analyzer.
A fully integrated system was developed that uses upgraded PLC code and Malvern M.I.M.I.C software to handle data exchange
between the main pieces of hardware. The operator interacts with this computer via the mill human–machine interface (HMI) and is able to input set points for the control loop, remotely start and stop the analyzer
and mill, perform background tests, and receive particle-size results. Figure 8 shows the integrated system architecture.
Figure 8: Integrated system architecture for the automated mill with on-line particle-size analyzer. OSI PI is an operational
event and real-time data-management infrastructure by OSISoft. TCIP/IP is an Internet protocol. M.I.M.I.C is Malvern Instruments
Multiple Instruments Control. OPC is object linking and embedding for process control. PSA is particle-size analyzer. PLC
is programmable logic controller.
A closed-control loop links particle size with rotor speed. Given a particle size set point and control range, the mill will
adjust speed until particle size is brought into the defined range. In initial trials, proportional (P) control only was used
and the chosen feedback parameter was average Dv50 (the particle size below which 50% of the distribution lies), with a 30-s
rolling average. Even though the system was challenged by deliberately setting rotor speed extremely low and extremely high,
the loop brought the process to the required steady state within a window of ± 2 μm in less than 5 min.
In further tests, the set point was reduced from the initial 58 μm to 50 μm and then back up to its original value. During
this trial, the system stabilized at the first set point after about 1 min, reached the second set point in about 30 s after
the change had been made, and completed the final transition in less than 2 min. These short changeover times highlight the
effectiveness of the control system, even in the absence of comprehensive loop tuning.
The trend shown in Figure 9 is from the latter part of this trial when the process had steadied at the final operating condition.
In steady state, the control loop is able to keep the particle size in the desired range by varying rotor speed within a ±
200 rpm range.
Figure 9: Steady-state operation of the mill under automated control.
In addition, the trend demonstrates how with real-time measurement, process upsets are detected instantly. At the time highlighted
by the green oval, Dv90 spikes while transmission plunges rapidly, indicating some kind of unexpected event. In fact, the
product containers overfilled at this point, backing up into the spool piece and forcing the sample probe to work in a densely
packed powder bed. Consequently, the concentration of powder flowing into the measurement zone rose and caused a fall in transmission
because gas flow was insufficient to break up so much agglomerated material, Dv90 increased. This rapid detection of process
upsets is extremely beneficial because it enables the operator to quickly take remedial action, thereby minimizing the production
of out–of–specification material.
Finally, it is useful to examine what happens at the end of the run as the mill starts to empty. Transmission becomes high
as the amount of powder drawn into the measurement zone starts to fall. Particle size drops because the mill is discharging
leftover fines. Eventually the material level drops to near zero and a low-scattering signal is generated by the analyzer.
During this final stage, automated control will wind down the speed of the rotor in response to decreasing particle size.
The low-scattering signal is used to trigger the mill shutdown sequence.
With full automation, the operator simply selects the target particle size and then feeds material into the mill. Real-time
process variable tracking and cumulative batch results are displayed at the mill HMI. Tight control virtually eliminates out-of-specification
production and reduces the requirement for in-process testing. If control is sufficiently good, which is feasible with a well-tuned
proportional integral derivative (PID) loop, it is possible that product quality will ultimately be assured through on-line
testing and allow for real-time release and reduction for the overall cost of quality.
The mill is responsive to variability in the feed, and compensates for it, thus keeping the particle size of the output within
the specification at all times. Product consistency is exemplary, with the mill effectively absorbing upstream variability
rather than simply passing it on to downstream processing. The history report produced for each batch shows exactly how mill
speed has changed to maintain particle size at any point and confirms that the specification is met throughout the process.
These real-time data quantify variability in the feed and give insight into upstream operations.
Automated mill control based on real-time particle-size analysis can be applied to many different processes. For existing
manufacture, its use is likely to necessitate regulatory approval, but for new products, there is the option of developing
on the basis of automated manufacture. Both pilot- and full-scale operation are likely to benefit in the future, the specific
benefits being unique to each case.
At the pilot scale, improved product consistency is enormously beneficial because it enhances decision-making processes. It
is much easier to correctly quantify the impact of API particle-size distribution on drug-product manufacturability and quality
when the particle size is tightly controlled. This milling-control study illustrates one of the reasons why PAT solutions
are valuable when the knowledge-based approach encouraged by QbD is adopted. In manufacture, better control translates into
first-time-right production, less waste, better equipment utilization, and a reduced requirement for manual input, all of
which reduce production and quality costs.