Simulation-Based Design of an Efficient Control System for the Continuous Purification and Processing of Active Pharmaceutical Ingredients


In this study, an efficient system-wide controlsystem has been designed for the integrated continuous purification and processing of the active pharmaceutical ingredient (API). The control strategy is based on the regulatory PID controller which is most widely used in the manufacturing industry because of its simplicity and robustness. The designed control system consists of single and cascade (nested) control loops. The control system has been simulated in gPROMSTM (Process System Enterprise). The ability of the control system to track the specified set point changes as well as to reject disturbances has been evaluated. Results demonstrate that the model shows an enhanced performance in the presence of random disturbances under closed-loop control compared to an open-loop operation. The control system is also able to track the set point changes effectively. This proves that closed-loop feedback control can be used in improving pharmaceutical manufacturing operations based on the Quality by Design (QbD) paradigm.

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  1. Plumb K. Continuous processing in the pharmaceutical industry: changing the mindset. Chem Eng Res Des. 2005;83:730–738.

    CAS  Article  Google Scholar 

  2. Reklaitis GV, Khinast J, Muzzio FJ. Pharmaceutical engineering science—new approaches to pharmaceutical development and manufacturing. Chem Eng Sci. 2010;65:4–7.

    Article  Google Scholar 

  3. Food and Drug Administration. Guidance for industry. PAT-A framework for innovative pharmaceutical development, manufacturing and quality assurance, Food and Drug Administration September 2004.

  4. Food and Drug Administration. Guidance for industry. Q8 pharmaceutical development, Food and Drug Administration May 2006.

  5. Charoo NA, Shamsher AAA, Zidan AS, Rahman Z. Quality by design approach for formulation development: a case study of dispersible tablets. Int J Pharm. 2012;423:167–178.

    CAS  PubMed  Article  Google Scholar 

  6. Singh R, Ierapetritou M, Ramachandran R. An engineering study on the enhanced control and operation of continuous manufacturing of pharmaceutical tablets via roller compaction. Int J Pharm. 2012;438:307–326.

    CAS  PubMed  Article  Google Scholar 

  7. Boukouvala F, Niotis V, Ramachandran R, Muzzio FM, Ierapetritou G. An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process: an integrated approach. Comput Chem Eng. 2012;42:30–47.

    CAS  Article  Google Scholar 

  8. Boukouvala F, Chaudhury A, Sen M, Zhou R, Mioduszewski L, Ierapetritou MG, Ramachandran R. Computer-aided flowsheet simulation of a pharmaceutical tablet manufacturing process incorporating wet granulation. J Pharm Innov. 2013;8:11–27.

    Article  Google Scholar 

  9. Sen M, Rogers A, Singh R, Chaudhury A, John J, Ierapetritou MG, Ramachandran R. Flowsheet optimization of an integrated continuous purification-processing pharmaceutical manufacturing operation. Chem Eng Sci. 2013;102:56–66.

    CAS  Article  Google Scholar 

  10. Sen M, Chaudhury A, Singh R, John J, Ramachandran R. Multi-scale flowsheet simulation of an integrated continuous purification-downstream pharmaceutical manufacturing process. Int J Pharm. 2013;445:29–38.

    CAS  PubMed  Article  Google Scholar 

  11. Gnoth S, Jenzsch M, Simutis R, Luubert A. Process analytical technology (PAT): batch-to-batch reproducibility of fermentation processes by robust process operational design and control. J Biotechnol. 2007;132:180–186.

    CAS  PubMed  Article  Google Scholar 

  12. Cervera-Padrell AE, Skovby T, Kiil S, Gani R, Gernaey KV. Active pharmaceutical ingredient (API) production involving continuous processes a process system engineering (PSE)-assisted design framework. Eur J Pharm Biopharm. 2012;82:437–456.

    CAS  PubMed  Article  Google Scholar 

  13. Benyahia B, Lakerveld R, Barton PI. A plant-wide dynamic model of a continuous pharmaceutical process. Ind Eng Chem Res. 2012;51:15393–15412.

    CAS  Article  Google Scholar 

  14. Lakerveld R, Benyahia B, Braatz RD, Barton PI. Model-based design of a plant-wide control strategy for a continuous pharmaceutical plant. AIChE J. 2013;59:3671–3685.

    CAS  Article  Google Scholar 

  15. Fujiwara M, Nagy ZK, Chew JW, Braatz RD. First-principles and direct design approaches for the control of pharmaceutical crystallization. J Process Control. 2005;15:493–504.

    CAS  Article  Google Scholar 

  16. Ma CY, Wang XZ. Closed-loop control of crystal shape in cooling crystallization of L-glutamic acid. J Process Control. 2012;22:72–81.

    CAS  Article  Google Scholar 

  17. Kleinert T, Weickgennant M, Judat B, Hagenmeyer V. Cascaded two-degree-of-freedom control of seeded batch crystallisations based on explicit system inversion. J Process Control. 2010;20:29–44.

    CAS  Article  Google Scholar 

  18. Drews A, Arellano-Garcia H, Schoneberger J, Schaller J, Kraume M, Wozny G. Improving the efficiency of membrane bioreactors by a novel model-based control of membrane filtration. Comput Aided Chem Eng. 2007;24:773–776.

    Google Scholar 

  19. Zaror CA, Perez-Correa JR. Model based control of centrifugal atomizer spray drying. Food Control. 1991;2:170–175.

    Article  Google Scholar 

  20. Daraoui N, Dufour P, Hammouri H, Hottot A. Model predictive control during the primary drying stage of lyophilisation. Control Eng Pract. 2010;18:483–494.

    Article  Google Scholar 

  21. Singh R, Gernaey KV, Gani R. ICAS-PAT: a software for design, analysis and validation of PAT systems. Comput Chem Eng. 2010;34:1108–1136.

    CAS  Article  Google Scholar 

  22. Hsu S, Reklaitis GV, Venkatasubramanian V. Modeling and control of roller compaction for pharmaceutical manufacturing. Part I: process dynamics and control framework. J Pharm Innov. 2010;5:14–23.

    Article  Google Scholar 

  23. Hsu S, Reklaitis GV, Venkatasubramanian V. Modeling and control of roller compaction for pharmaceutical manufacturing. Part II: control and system design. J Pharm Innov. 2010;5:24–36.

    Article  Google Scholar 

  24. Ramachandran R, Chaudhury A. Model-based design and control of continuous drum granulation processes. Chem Eng Res Des. 2011;90:1063–1073.

    Article  Google Scholar 

  25. Burggraeve A, Monteyne T, Vervaet C, Remon JP, Beer TD. Process analytical tools for monitoring, understanding, and control of pharmaceutical fluidized bed granulation: a review. Eur J Pharm Biopharm. 2013;83:2–15.

    CAS  PubMed  Article  Google Scholar 

  26. Kleinert T, Weickgennant M, Judat B, Hagenmeyer V. On control of particle size distribution in granulation using high shear mixers. J Process Control. 2010;20:29–44.

    CAS  Article  Google Scholar 

  27. Sanders CFW, Hounslow MJ, III FJD. Identification of models for control of wet granulation. Powder Technol. 2009;188:255–263.

  28. Gatzke EP, III FJD. Model predictive control of a granulation system using soft output constraints and prioritized control objectives. Powder Technol. 2001;121:149–158.

  29. Long CE, Polisetty PK, Gatzke EP. Deterministic global optimization for non-linear model predictive control of hybrid dynamic systems. Int J Robust Nonlinear Control. 2007;17:1232–1250.

    Article  Google Scholar 

  30. Pottmann M, Ogunnaike BA, Adetayo AA, Ennis BJ. Model-based control of a granulation process. Powder Technol. 2000;108:192–201.

    CAS  Article  Google Scholar 

  31. Ramachandran R, Arjunan J, Chaudhury A, Ierapetritou MG. Model-based control loop performance assessment of a continuous direct compaction pharmaceutical processes. J Pharm Innov. 2012;6:249–263.

    Article  Google Scholar 

  32. Singh R, Ierapetritou M, Ramachandran R. System-wide hybrid MPC-PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction. Eur J Pharm Biopharm. 2013;85:1164–1182.

    CAS  PubMed  Article  Google Scholar 

  33. Singh R, Sahay A, Oka S, Liu X, Ramachandran R, Ierapetritou M, Muzzio F. Online monitoring, advanced control and operation of robust continuous pharmaceutical tablet manufacturing process. BioPharma Mag Asia. 2013;2:18–23.

    Google Scholar 

  34. Sen M, Singh R, Vanarase A, John J, Ramachandran R. Multi-dimensional population balance modeling and experimental validation of continuous powder mixing processes. Chem Eng Sci. 2012;80:349–360.

    CAS  Article  Google Scholar 

  35. Robles A, Ruano MV, Ribes J, Ferrer J. Advanced control system for optimal filtration in submerged anaerobic mbrs (SAnMBRs). J Membr Sci. 2013;430:330–340.

    CAS  Article  Google Scholar 

  36. Peiris RH, Budman H, Moresoli C, Legge RL. Fouling control and optimization of a drinking water membrane filtration process with real-time model parameter adaptation using fluorescence and permeate flux measurements. J Process Control. 2013;23:70–77.

    CAS  Article  Google Scholar 

  37. Singh R, Gernaey KV, Gani R. An ontological knowledge-based system for the selection of process monitoring and analysis tools. Comput Chem Eng. 2010;34:1137–1154.

    CAS  Article  Google Scholar 

  38. gPROMS model builder, gPROMS 3.4.0 documentation, Process system enterprise (PSE)

  39. Stephanopoulos G. Chemical process control. USA: Prentice-Hall,Inc.; 2006.

    Google Scholar 

  40. Blevins T, Wojsznis WK, Nixon M. Advanced control foundation: tools, techniques and applications. USA: International Society of Automation; 2013.

    Google Scholar 

  41. Vanarase AU, Alaca M, Rozo J, Muzzio FJ, Romonach RJ. Real time monitoring of drug concentration in a continuous powder mixing process using NIR spectroscopy. Chem Eng Sci. 2010;65:5728–5733.

    CAS  Article  Google Scholar 

  42. Miki H, Terashima T, Asakuma Y, Maeda K, Fukui K. Inclusion of mother liquor inside KDP crystals in a continuous MSMPR crystallizer. Sep Purif Technol. 2005;43:71– 76.

    CAS  Article  Google Scholar 

  43. Gunawan R, Fusman I, Braatz RD. High resolution algorithms for multidimensional population balance equations. AIChE J. 2004;50:2738–2749.

    CAS  Article  Google Scholar 

  44. Mccabe WL, Smith JC, Harriott P. Unit operations of chemical engineering. NY: McGraw-Hill; 2001.

    Google Scholar 

  45. Mezhericher A, Levy A, Borde I. Modelling of particle breakage during drying. Chem Eng Process. 2008; 47:1404–1411.

    Article  Google Scholar 

  46. Sen M, Ramachandran R. A multi-dimensional population balance model approach to continuous powder mixing processes. Adv Powder Technol. 2013;24:51–59.

    Article  Google Scholar 

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This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855.

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Corresponding author

Correspondence to Rohit Ramachandran.

Appendix and Mathematical Model

Appendix and Mathematical Model

The equations used for developing the mathematical model for each unit operation of the flowsheet have been listed.


In this study, a 2-D population balance model considering growth in two directions has been implemented for the crystallization process, as shown below.

$$\begin{array}{@{}rcl@{}} &&\frac{\partial{F(L_{1},L_{2},t)}}{\partial{t}}+\frac{\partial({G_{1}(L_{1},t)F(L_{1},L_{2},t)})}{\partial{L_{1}}} \notag \\ &&+\frac{\partial({G_{2}(L_{2},t)F(L_{1},L_{2},t)})}{\partial{L_{2}}} =B_{0}(C,t)\delta(L_1)\delta(L_2) \\ &&+ Inflow - Outflow \end{array} $$

The growth and the nucleation terms can be written as follows:

$$ G_1=k_{g1} \left(\frac{C-C_{sat}}{C_{sat}}\right)^{g_{1}} $$
$$ G_2=k_{g2} \left(\frac{C-C_{sat}}{C_{sat}}\right)^{g_{2}} $$
$$ B_0=k_{b} \left(\frac{C-C_{sat}}{C_{sat}}\right)^b $$

Another internal coordinate (Liq(L eq )) is added to the PBE which tracks the amount of liquid present in the crystals based on the works of Miki et al. [42]. Considering the width of the crystal to be L 1, the equivalent length of the crystal can be expressed as follows:

$$ L_{eq}(L_{1},L_2)=\left(\frac{6}{\pi}\times L_{1}^{2} L_{2}\right)^{\frac{1}{3}} $$
$$ Liq(L_{eq})=2 \times 10^{-5} L_{eq}^{2} -3 \times 10^{-9} L_{eq}+ 10^{-13} $$

The mass balance equation can be written as shown in the equation below [43]:

$$\begin{array}{@{}rcl@{}} &&\frac{dC}{dt}=-\rho_{c} \int_{0}^{\infty} \int _{0}^{\infty} F(L_{1},L_{2},t)(2G_{1}(L_1L_2-L1^2) \notag\\ &&+G_2L1^2)dL_1dL_2 \end{array} $$

Since this is a case of cooling crystallization, the temperature cooling schedule can be expressed as a function of time. C sat has been expressed as a function of temperature. The expression has been obtained by fitting experimental data as obtained from Bristol-Myers Squibb Co., NJ.

$$ C_{sat}=2.7357T-40.925 $$

The heat transfer between the coolant and crystallizer can be represented as follows:

$$ U A_{c} (T-T_c)=M_{w} C_{pw} (T_c-T_{in}) $$

The controller equations for the crystallization process are given below:

  • Master Controller:

    $$\begin{array}{@{}rcl@{}} &&T(t)=K_{c}(Set Point(t)-C_{sat}(t)) \notag \\ &&+\frac{K_{c}}{\tau_{I}}\int^{t}_{0}(Set Point(t)-C_{sat}(t))dt \\ &&+K_{c}\tau_{D}\frac{d(Set Point(t)-C_{sat}(t))}{dt}+c_{s} \notag \\ \end{array} $$
  • Slave Controller:

    $$\begin{array}{@{}rcl@{}} &&T_{c}(t)=K_{c}(Set Point(t)-T(t)) \notag \\ &&+\frac{K_{c}}{\tau_{I}}\int^{t}_{0}(Set Point(t)-T(t))dt \\ &&+K_{c}\tau_{D}\frac{d(Set Point(t)-T(t))}{dt}+c_s \end{array} $$

Filtration Process

The main design equations of the cake filter is as follows (adapted from McCabe et al. [44]):

$$\begin{array}{@{}rcl@{}} &&\frac{dV(L_{1},L_{2},L_{3},t)}{dt} \notag \\ &&=\frac{A^{2}\Delta P}{\mu (\alpha(L_{1},L_{2},L_{3},t) c V(L_{1},L_{2},L_{3},t)+R_{m} A) } \end{array} $$

c and α can be found as follows:

$$ c=\frac{C_{F}}{1-(\frac{m_{F}}{m_{c}}-1)C_F/\rho_{s}} $$
$$ \alpha(L_{1},L_{2},L_{3},t)=\frac{150(1-\epsilon(L_{1},L_{2},L_{3},t))}{D_{p}(L_{1},L_{2},L_{3},t)\epsilon(L_{1},L_{2},L_{3},t)^{3}\rho_{s}} $$

Assuming that there are no solid particles present in the filtrate, the mass of wet cake deposited on the septum is given as follows:

$$\begin{array}{@{}rcl@{}} &&{} m_{F}(L_{1},L_{2},L_{3},t)= F(L_{1},L_{2},L_{3},t)V_{p} \notag \\ &&(L_{1},L_{2},L_{3},t)\rho_{s} N_a \end{array} $$

Drying Process

For the drying process, a model has been developed where the liquid is being evaporated from the solid surface (adapted from Mezhericher et al. [45]).

The change in particle diameter with time can be represented as follows:

$$ \frac{dD_{p}(L_{1},L_{2},L_{3},t)}{dt}=-\frac{m_{v}(L_{1},L_{2},L_{3},t)}{\rho_{l} 2 \pi D_{p}(L_{1},L_{2},L_{3},t)^{2}} $$

The temperature profile of particle can be given as shown below:

$$\begin{array}{@{}rcl@{}} &&h_{fg}m_{v}(L_{1},L_{2},L_{3},t)+ c_{ps} \rho_{avg}(L_{1},L_{2},L_{3},t) \notag \\ &&V_{p}(L_{1},L_{2},L_{3},t)\frac{dT_{p}(L_{1},L_{2},L_{3},t)}{dt} \\ &&=h(T_g-T_{p}(L_{1},L_{2},L_{3},t))2\pi D_{p}(L_{1},L_{2},L_{3},t)^{2} \notag \\ \end{array} $$

The evaporation rate can be calculated as follows:

$$\begin{array}{@{}rcl@{}} && m_{v}(L_{1},L_{2},L_{3},t)=k(L_{1},L_{2},L_{3},t)(x_p-x_{eql})2 \pi \notag\\ &&D_{p}(L_{1},L_{2},L_{3},t)^2 \end{array} $$

The heat and mass transfer coefficients are given below:

$$ h(L_{1},L_{2},L_{3},t)=\frac{Sh(L_{1},L_{2},L_{3},t)D_{v}}{D_{p}(L_{1},L_{2},L_{3},t)} $$
$$ k(L_{1},L_{2},L_{3},t)=\frac{Nu(L_{1},L_{2},L_{3},t) k_{g}}{D_{p}(L_{1},L_{2},L_{3},t)} $$

such that Nusselts number is given as follows:

$$\begin{array}{@{}rcl@{}} Nu(L_{1},L_{2},L_{3},t)=(2+0.6Re(L_{1},L_{2},L_{3},t)^{\frac{1}{2}}Pr^{\frac{1}{3}}) \notag \\ (1+(C_{pv}*(T_g-T_p)/h_{fg}))^{-0.7} \notag \\ \end{array} $$

and Sherwood number is given as shown in Eq. 26.

$$\begin{array}{@{}rcl@{}} Sh(L_{1},L_{2},L_{3},t)=(2+0.6Re(L_{1},L_{2},L_{3},t)^{\frac{1}{2}}Sc^{\frac{1}{3}}) \notag \\ (1+(Cpv*(T_g-T_p)/h_{fg}))^{-0.7} \notag \\ \end{array} $$

The outflow from dryer is given as shown below:

$$\begin{array}{@{}rcl@{}} mass_{out}(L_{1},L_{2},L_{3},t)=m_{F}(L_{1},L_{2},L_{3},t) \notag \\ -m_{v}(L_{1},L_{2},L_{3},t)*F(L_{1},L_{2},L_{3},t)*Na \notag \\ \end{array} $$

The heat transfer between the air and superheated steam can be represented as follows:

$$ U A_{s} (T_s-T_{gas})=M_{steam} C_{psteam} (T_s-T_{in-steam}) $$

The controller equation for drying can be given as follows:

$$\begin{array}{@{}rcl@{}} &&T_{s}(t)=K_{c}(Set Point(t)-T_{gas}(t)) \\ &&+\frac{K_{c}}{\tau_{I}}\int^{t}_{0}(Set Point(t)-T_{gas}(t))dt \\ &&+K_{c}\tau_{D}\frac{d(Set Point(t)-T_{gas}(t))}{dt}+c_{s} \\ \end{array} $$


The mixing model has been assumed to be independent of size change based on previous work [46]. Hence the internal coordinates have been dropped from the population balance model. The PBM for the mixer can be written as follows:

$$\begin{array}{@{}rcl@{}} \frac{\partial}{\partial t}F(\textbf{z},t)+\frac{\partial}{\partial \textbf{z}}\left[F(\textbf{z},t)\frac{d\textbf{z}}{dt}\right] = 0 \end{array} $$

The above equation can be written in multidimensional form as shown below:

$$\begin{array}{@{}rcl@{}} \frac{\partial}{\partial t}F(n,x,y,t)+\frac{\partial}{\partial x}\left[F(n,x,y,t)\frac{dx}{dt}\right] \\ +\frac{\partial}{\partial y}\left[F(n,x,y,t)\frac{dy}{dt}\right] \\ =Inflow - Outflow \end{array} $$

The mass balance of a single component can be simplified according to the equation given below:

$$\begin{array}{@{}rcl@{}} &&\frac{\partial{F(n,x,y,t)}}{\partial{t}}= \frac{V_{f} [F_{n,x-1,y,t}-F_{n,x,y,t}]}{\Delta x} \\ &&+ \frac{V_{b} [F_{n,x+1,y,t}-F_{n,x,y,t}]}{\Delta x} \\ &&+ V_{r} \frac{[F_{n,x,y+1,t}+F_{n,x,y-1,t}-2F_{n,x,y,t}]}{\Delta y} \\ \end{array} $$

The properties of the final blend from the mixer output have been presented in terms of mean API composition (y API ) relative standard deviation (RSD).

$$ y_{API}=\frac{\sum^{y_{max}}_{y=1} {F(API,x_{max},y,t)}}{\sum^{n_{max}}_{n=1}{\sum^{y_{max}}_{y=1} {F(n,x_{max},y,t)}}} $$

The controller equation for drying can be given as follows:

$$\begin{array}{@{}rcl@{}} Excipient_{flowrate}(t) &=& K_{c}(Set Point(t)-y_{API}(t))\\ &&{} +\frac{K_{c}}{\tau_{I}}\int^{t}_{0}(Set Point(t)-y_{API}(t))dt\\ &&{} +K_{c}\tau_{D}\frac{d(Set Point(t)-y_{API}(t))}{dt}+c_{s}\\ \end{array} $$


A c
B 0
C sat C pw
D p
G 1
G 2
g 1
g 2
k g1
k g2
k b
L 1
L 2
L 3
M w
T c
T in
ρ avg
ρ s

m F
m c
m v
N a
R m
V p
A s
c ps
C psteam
h fg
T s
T p
T g
T insteam
mass out
M s
x p
x eql
n max
V f
V b
V r
x max
y max
y avg
y i
Area of heat transfer
Primary nucleation term
Kinetic parameter for crystallization
Solute concentration in crystallization
Saturation concentration of solute
Specific heat constant for water
Crytal diameter
Particle density
Growth rate
Growth rate
Kinetic parameter for crystallization
Kinetic parameter for crystallization
Kinetic parameter for crystallization
Kinetic parameter for crystallization
Kinetic parameter for crystallization
Internal coordinate for length of solid
Internal coordinate for length of solid
Internal coordinate for length of liquid
Cooling water flowrate
Temperature (cooling schedule)
Temperature of cooling water
Inlet temperature of water
Overall heat transfer coefficient
porosity of cake
Average density of wet particles
Density of solid
Filter pressure difference
Fluid viscosity
Specific cake resistance
Filter surface area
concentration of solutes in slurry
Mass of solute deposited on filter
per unit volume of filtrate
Mass of wet cake
Mass of dry cake
Rate of evaporation during drying
Avogadro number
Filter medium resistance
Filtrate volume
Particle volume
Area of heat transfer
Specific heat capacity
Specific heat constant for steam
Mass transfer coefficient
Specific heat of evaporation
Heat transfer coefficient
Temperature of steam
Temperature of particle
Drying gas temperature
Inlet temperature of steam
Outlet flowrate of API crystals from dryer
Steam flowrate
Nusselts Number
Prandtl Number
Reynolds Number
Sherwood Number
Overall heat transfer coefficient
Liquid content of solid particle
Liquid content of solid particle at equillibrium
Counter for number of components
Maximum number of components
Forward axial velocity
Backward axial velocity
Radial velocity
Spatial coordinate in axial direction
Maximum number of axial compartments
Spatial coordinate in axial direction
Maximum number of radial compartments
Fractional API composition at mixer outlet
Average spatial composition of component A
Composition of component A in ith compartment
m 2
particles/m 3/s
moles/m 3
moles/m 3
particles/m 3
particles/m 3/s
W/m 2K
kg/m 3
kg/m 3
m 2
moles/m 3
kg/m 3

m 3
m 3
m 2
W/M 2 K
W/m 2K
moles/m 3
moles/m 3

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Sen, M., Singh, R. & Ramachandran, R. Simulation-Based Design of an Efficient Control System for the Continuous Purification and Processing of Active Pharmaceutical Ingredients. J Pharm Innov 9, 65–81 (2014).

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  • Process control
  • Continuous processing
  • Flowsheet simulation
  • Powder mixing
  • Pharmaceutical manufacturing
  • Crystallization