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Combined Feedforward/Feedback Control of an Integrated Continuous Granulation Process



Continuous manufacturing offers shorter processing times and increased product quality assurance, among several other advantages. This makes it an ever-growing interest among pharmaceutical companies. A suitable efficient control system is however desired for continuous pharmaceutical manufacturing to achieve a consistent predefined end product quality.


In order to control product quality more accurately, the effects of input disturbances need to be proactively mitigated. Therefore, it is desired that a combined feedforward/feedback control system integrated with suitable process analytical technology (PAT) be implemented over a traditional feedback-only control system. The feedforward controller measures and takes corrective actions for disturbances proactively before they affect the process and thereby product quality. The feedback controller considers the real-time deviation of control variable from a pre-specified set point and keeps it at a minimum possible value. The deviation of a control variable from the set point could be due to both measurable and unmeasurable disturbances.


In this work, a combined control strategy has been developed for a continuous twin screw wet granulation (WG) process. An integrated flowsheet model was developed and simulated in order to evaluate the effect of control loops on critical quality attributes (CQAs). Different strategies of manipulation were evaluated and the best strategy was identified.


In silico study on the combined feedforward/feedback control strategy and feedback-only control strategy demonstrates that the combined loop results in diminished variability of the CQAs.

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This work is supported by Glaxo Smith Kline (GSK) and the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855.

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Correspondence to Ravendra Singh.



Feedforward Model

The feedforward controller model was developed in MATLAB workspace and Simulink. The integrated flowsheet model developed in gPROMS was converted to an integrated flowsheet model in Simulink. Transfer function models were developed from data generated by the gPROMS model. These models were developed in System Identification Toolbox and the best fit model was selected. The pole-zero plot and bode diagrams for these transfer functions were also developed to ensure that the transfer functions were stable. These transfer function models describe the various unit operations in the flowsheet. The flowsheet transfer function model is described in Fig. 36.

Fig 36
figure 36

Integrated flowsheet model simulated in Simulink (open loop)

The above transfer function model was simplified because during implementation a simplified model for the entire process would be required. The simplified process model which also represents the ideal case was developed using the data generated by the Simulink model described in Fig. 36. The feedforward controller was then developed using the disturbance model and the process model given in Fig. 37. The feedforward controller transfer function is achieved by equating the characteristic equation to zero. The general form of that is given in Eq. 10. Both the disturbance transfer function (Gd) and process transfer function (Gp) are specific to a particular process and material and would change if any changes are made to the process or the materials.

$$ {G}_{\mathrm{FF}}=-\frac{G_{\mathrm{d}}}{G_{\mathrm{p}}} $$
Fig. 37
figure 37

Integrated flowsheet with combined feedforward/feedback control in Simulink

Appendix B: Nomenclature

 APAP Acetyl-para-aminophenol
 API Active pharmaceutical ingredient
 CPM Continuous pharmaceutical manufacturing
 CPP Critical process parameter
 CQA Critical quality attribute
 CSTR Continuously stirred tank reactor
 CU Content uniformity
 D2R Duration to reject
 HPMC Hypromellose
 IAE Integral of absolute error
 ISE Integral of square of error
 ITAE Integral of time absolute error
 LOD Loss on drying
 L/S Liquid to solid
 M2P Magnitude to product
 MgSt Magnesium stearate
 MPC Model predictive control
 MRT Mean residence time
 MSC Multiplicative scattering correction
 NaStGly Sodium starch glycolate
 NIR Near infrared
 PAT Process analytical technology
 PFR Plug flow reactor
 PID Proportional integral derivative
 PLS Partial least squares
 QbD Quality by design
 RMSE Root mean square error
 RMSEP Root mean square error of prediction
 RSEP Relative standard error of prediction
 RSD Relative standard deviation
 RTD Residence time distribution
 SMCC Silicified microcrystalline cellulose
 SP Set point
 SSE Sum of squared errors
 T2P Time to product
 TSG Twin screw granulator
 WG Wet granulation
Symbol Variable
 Gd(s) Disturbance transfer function model
 Gp(s) Process transfer function model
 Gc Controller transfer function model
 P Proportional gain
 I Integral time constant
 D Derivative time constant
Subscript Description
 d Disturbance
 p Process
 c Controller
 1,2,3,4 Process or controller numbers

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Pereira, G.C., Muddu, S.V., Román-Ospino, A.D. et al. Combined Feedforward/Feedback Control of an Integrated Continuous Granulation Process. J Pharm Innov 14, 259–285 (2019).

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  • Continuous pharmaceutical manufacturing
  • Feedforward control
  • Feedback control
  • Continuous twin screw granulator (TSG)
  • Process analytical technology