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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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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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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