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Contributors
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Sara Badr Department of Chemical System Engineering, The University of Tokyo,
Tokyo, Japan
Massimiliano Barolo CAPE-Lab—Computer-Aided Process Engineering Laboratory, University of Padova, Padova, Italy
Paul I. Barton Process Systems Engineering Laboratory, Massachusetts Institute of
Technology, Cambridge, MA, USA
Mohammad Amin Boojari Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
Richard D. Braatz Department of Chemical Engineering, Massachusetts Institute
of Technology, Cambridge, MA, USA
Daniel Casas-Orozco Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN, USA
Pierre-François Chavez UCB Pharma, Braine l’Alleud, Belgium
Giorgio Colombo Process and Systems Engineering Centre (PROSYS), Depart-
ment of Chemical and Biochemical Engineering, Technical University of Denmark,
Kgs. Lyngby, Denmark
Abina M. Crean University College Cork, Cork, Ireland
Ashok Das Department of Mathematics, Indian Institute of Technology Kharagpur,
Kharagpur, West Bengal, India
Francesco Destro CAPE-Lab—Computer-Aided Process Engineering Laboratory,
University of Padova, Padova, Italy
Tump a D e y Faculty of Engineering and Science, School of Science, University of
Greenwich, Kent, UK
CIPER—Centre for Innovation and Process Engineering Research, Kent, UK
xi
xii Contributors
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Dennis Douroumis Faculty of Engineering and Science, School of Science, University of Greenwich, Kent, UK
CIPER–Centre for Innovation and Process Engineering Research, Kent, UK
Julie Fahier UCB Pharma, Braine l’Alleud, Belgium
Mohammad Fakroleslam Process Engineering Department, Faculty of Chemical
Engineering, Tarbiat Modares University, Tehran, Iran
Zhenguo Gao Tianjin University, Tianjin, China
Krist V. Gernaey Process and Systems Engineering Centre (PROSYS), Depart-
ment of Chemical and Biochemical Engineering, Technical University of Denmark,
Kgs. Lyngby, Denmark
J. Glassey School of Engineering, Newcastle University, Newcastle upon Tyne, UK
Parag Gogate Chemical Engineering Department, Institute of Chemical Technol-
ogy, Mumbai, India
Junbo Gong Tianjin University, Tianjin, China
Matteo Grossi Process and Systems Engineering Centre (PROSYS), Department
of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs.
Lyngby, Denmark
Izumi Hirasawa Department of Applied Chemistry, Waseda University, Tokyo,
Japan
Yashraj Jagtap Chemical Engineering Department, Institute of Chemical Technology, Mumbai, India
Mark Nicholas Jones Process and Systems Engineering Centre (PROSYS),
Department of Chemical and Biochemical Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
M. Karlberg School of Engineering, Newcastle University, Newcastle upon Tyne,
UK
Brian M. Kerins University College Cork, Cork, Ireland
A. Kizhedath School of Engineering, Newcastle University, Newcastle upon Tyne,
UK
Jitendra Kumar Department of Mathematics, Indian Institute of Technology
Kharagpur, Kharagpur, West Bengal, India
Daniel J. Laky Davidson School of Chemical Engineering, Purdue University,
West Lafayette, IN, USA
Corentin Larcy UCB Pharma, Braine l’Alleud, Belgium
Contributors xiii
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Seyed Soheil Mansouri Process and Systems Engineering Centre (PROSYS),
Department of Chemical and Biochemical Engineering, Technical University of
Denmark, Kgs. Lyngby, Denmark
Christos T. Maravelias Andlinger Center for Energy and the Environment and
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
Ikuma Masaki Department of Applied Chemistry, Waseda University, Tokyo,
Japan
Kensaku Matsunami Department of Chemical System Engineering, The University of Tokyo, Tokyo, Japan
Shamik Misra Department of Chemical and Biological Engineering, University of
Wisconsin, Madison, WI, USA
Zoltan K. Nagy Davidson School of Chemical Engineering, Purdue University,
West Lafayette, IN, USA
Uttom Nandi Faculty of Engineering and Science, School of Science, University of
Greenwich, Kent, UK
CIPER—Centre for Innovation and Process Engineering Research, Kent, UK
Morteza Nikkhah Nasab Process Systems Engineering Laboratory, Department of
Chemical Engineering, AmirKabir University of Technology (Tehran Polytechnic),
Tehran, Iran
Anastasia Nikolakopoulou Massachusetts Institute of Technology, Cambridge,
MA, USA
Mehrdad Pasha UCB Pharma, Braine l’Alleud, Belgium
Mayur M. Patel Department of Pharmaceutics, Institute of Pharmacy, Nirma Uni-
versity, Ahmedabad, India
Michael Patrascu Department of Chemical Engineering, Technion-Israel Institute
of Technology, Haifa, Israel
Simone Perra Process and Systems Engineering Centre (PROSYS), Department
of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs.
Lyngby, Denmark
Gabrielle Pilcer UCB Pharma, Braine l’Alleud, Belgium
Gintaras V. Reklaitis Davidson School of Chemical Engineering, Purdue Univer-
sity, West Lafayette, IN, USA
Ali M. Sahlodin Process Systems Engineering Laboratory, Department of Chemical Engineering, AmirKabir University of Technology (Tehran Polytechnic),
Tehran, Iran
xiv Contributors
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Chinmayee Sarode Chemical Engineering Department, Institute of Chemical Technology, Mumbai, India
Seyed Abbas Shojaosadati Biotechnology Group, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
Fanny Stauffer UCB Pharma, Braine l’Alleud, Belgium
Hirokazu Sugiyama Department of Chemical System Engineering, The University
of Tokyo, Tokyo, Japan
Isuru Udugama Process and Systems Engineering Centre (PROSYS), Department
of Chemical and Biochemical Engineering, Technical University of Denmark, Kgs.
Lyngby, Denmark
Joi Unno Department of Applied Chemistry, Waseda University, Tokyo, Japan
Preksha Vinchhi Department of Pharmaceutics, Institute of Pharmacy, Nirma
University, Ahmedabad, India
Matthias von Andrian Massachusetts Institute of Technology, Cambridge, MA,
USA
Lifang Zhou Department of Chemical Engineering, Massachusetts Institute of
Technology, Cambridge, MA, USA
Xiaoxiang Zhu Department of Chemical Engineering, Massachusetts Institute of
Technology, Cambridge, MA, USA
Process Control and Intensification
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of Solution Crystallization
Junbo Gong and Zhenguo Gao
Solution crystallization attracts more and more attention in the pharmaceutical
industries because of the functions of separation and the tuning ability of solid-state
properties at the molecular level. Generally, the research of solution crystallization
includes crystal engineering and crystallization process design and control. The
crystallization process design and control have achieved great progress in the past
decade, in which process analytical technology (PAT) has come to the real manufacturing practice and the conversion from batch to continuous is becoming a clear
tendency. In this chapter, the design and optimization of the crystallization process
are summarized that covers process control, seeding technique, intensification by
external fields, and the solution crystallization in continuous manufacturing.
1 Solution Crystallization Process Control
1.1 Introduction of Solution Crystallization Process Control
As a unit operation for separating and purifying solid products, crystallization is
widely used in the fields of medicine, food, microelectronics, and fine chemicals.
The crystallization process determines the purity, morphology, polymorph, particle
size, and particle size distribution of the solid product and many other characteristics, which have a significant impact on the performance of the drug and the
efficiency of the post-processing process [1]. Therefore, the precise control of the
crystallization process is of great significance to the production process and the
quality of products.
J. Gong ()·Z.Gao
Tianjin University, Tianjin, China
e-mail: junbo_gong@tju.edu.cn
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
A. Fytopoulos et al. (eds.), Optimization of Pharmaceutical Processes, Springer
Optimization and Its Applications 189, https://doi.org/10.1007/978-3-030-90924-6_1
1
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Fig. 1 A high-level overview of the efficient combination of model-free and model-based QbC
approaches for rapid (model-free) and optimal (model-based) crystallization process design.
(Caption and figure reprinted with permission from Ref. [5]
The actual purpose of crystallization control is to govern the crystal nucleation
and growth. Based on modeling and experimental methods, there are a lot of
researches on the control of polymorph, shape, and size [2–4]. The control of the
crystallization process usually aims to control the crystallization path in a safe
operation zone that ensures a robust manufacturing process. The crystallization
control strategies can be divided into two major categories: model-free and modelbased control approaches. Model-free techniques are based on feedback control
algorithms relying on in situ PAT measurements that provide the critical quality
attributes of the product, including size distribution, crystal shape, fewer impurities,
and target crystal form. Model-based control involves using a mathematical model
and numerical simulation to design the process by solving process optimization
problems. Nagy [5] proposed a general framework for the optimal design of
crystallization processes, which combined the application of two QbC methods:
model-free (mfQbC) and model-based (mbQbC). In addition to its robust operating
procedures, mfQbC also automatically generates model parameters and experimental data required by the mbQbC. As shown in Fig. 1, the derived model can be used
for optimal crystallization process design.
For a classical cooling crystallization process, the mfQbC includes the following
main steps: first, cooling-holding-heating experiments under different heating rates
Process Control and Intensification of Solution Crystallization 3
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and cooling rates, these experiments provide the nucleation rate and solubility
kinetics. In the second step, the direct nucleation control (DNC) experiment obtains
the width of the metastable zone of primary nucleation and secondary nucleation.
The third step is to guide the supersaturation control (SSC) experiment according
to the crystal phase diagram obtained from the DNC experiment to obtain the temperature control curve. Finally, to be easily implemented in an industrial distributed
control system, the temperature curve generated by the SSC is approximated by a
linear ramp to obtain the given temperature curve designed by mfQbC. The process
control technology plays an increasingly important role in the controlling of product
polymorph, purity, shape, particle size, and particle size distribution.
1.2 Polymorphic Control
Drug polymorphism refers to two ormore molecular assembly modes when the drug
molecules crystallize from solution to solid state [6]. Polymorphism is a common
phenomenon in the crystallization process. Since the different crystal forms of
the drug may seriously affect the stability, bioavailability, therapeutic effect, and
product quality, so ensuring the consistency of the crystal form is crucial to the drug
production process. There are challenges in polymorphic control, for example, the
transformation of the crystal form will lead to difficult control of the polymorphism
in which the purity of the crystal form is hard to control during the production
process.
In response to the challenge of polymorphic control, feedback control strategies
provide a pathway for solving the difficulties in the control process. The control
of polymorphs in the crystallization process mainly controls the nucleation of
nontarget crystals and promotes the growth of target crystals. For polycrystalline
materials, different ways to produce supersaturation may result in different crystal
forms. Supersaturation is the driving force of the crystallization process, and many
researchers have shown that an optimal supersaturation exists for a crystallization
process, and various methods for supersaturation measurement and implementation
of constant SSC strategy have been investigated. The SSC control strategy is based
on the understanding that the crystallization process needs to be operated in the
metastable region in the phase diagram, as shown in Fig. 2a. This method can
specify an arbitrary concentration target curve in the phase diagram, which is
particularly useful for the control of the polymorphic crystallization process. In the
polymorphic crystallization process, a complex operation trajectory is designed to
selectively control a specific crystal form [7]. Besides, setting the operation trajectory in the crystallization phase diagram can greatly reduce the sensitivity of the
crystal size distribution to process disturbances and can prevent the crystallization
process crossing the metastable zone and causing undesired explosion nucleation.
The advantage of this method is that by specifying the operation trajectory in the
crystalline phase diagram, the best operation trajectory in the time domain (such as
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Fig. 2 Crystallization phase diagram (a); supersaturation and corresponding temperature profiles
obtained during the SSC process (b)
the cooling curve) is automatically determined, and it can be implemented on an
industrial scale through a standard tracking control system.
By using traditional open-loop control approaches to implement the cooling
profile, such as simple linear cooling, it is hard to maintain the concentration
operating curve along with an expected trajectory. The SSC is a higher-level
control approach through controlling the crystallization operating trajectory in the
phase diagram than controlling the process by just following the timely determined
temperature profile (or solvent/anti-solvent ratio). The main advantage of this
approach over uncontrolled crystallization is that the operating curve can be directly
maintained within a “robust operating zone,” which can represent the nucleation
metastable zone or the targeted polymorph nucleation/growth region. In this way,
SSC can avoid undesired nucleation and polymorph transformation and achieve
optimal crystallization performance without a large number of experiments for
investigating the influence mechanism of process conditions [8–10]. The schematic
representation of the SSC approach is shown in Fig. 2b.
The SSC strategy can directly control the crystallization process on the phase
diagram, which is a relatively intuitive control method. However, when the crystallization phase diagram is greatly affected by disturbances or the nucleation rate
is high, the robustness of this method will be greatly reduced [11]. In addition,
the concentration feedback control strategy cannot directly control the properties of
solids, which means that even if the supersaturation levels of the two batches are the
same in the batch process, the product properties may still be quite different due to
process disturbances.
The temperature cycle during the crystallization process is beneficial to dissolve
the metastable crystal form produced during the crystallization process. Pataki et al.
[12] used Raman to detect nontarget crystal forms in the crystallization process and
trigger automatic heating to dissolve and eliminate metastable crystal forms. Tacsi
et al. [13] adopted polymorph concentration control to separately refine two crystal
Process Control and Intensification of Solution Crystallization 5
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forms of carvedilol. During the crystallization process, Raman detected a nontarget
crystal form to trigger temperature-rising dissolution, and the products obtained
were all target crystal forms. Active polymorphic feedback control realizes the
refining of the stable crystal form of OABA in the case of impure seed crystal form.
Raman detects that the metastable crystal form triggers heating and dissolution, and
then the system performed supersaturation control to prepare stable crystals [14].
In the past 20 years, in situ monitoring of polymorphism in the crystallization
process has developed rapidly, including online Raman, in situ XRD, in situ laser
backscattering, and in situ process image microscopy. Although none of these
technologies can be applied to all solute-solvent systems for online monitoring
of polymorphs, for most systems, at least one sensor technology can be used to
monitor the conversion between different crystal forms [15]. In recent years, process
detection and online analysis methods have been widely used in polymorphic
selective crystallization processes. Based on this, the development of polymorphic
feedback control (or closed-loop control) strategies has also made continuous
progress.
1.3 CSD and Morphology Control
In industrial crystallization process, crystal morphology, crystal size, and crystal
size distribution (CSD) are important properties of crystals, because these properties
play a vital role in determining the quality of the final product and the efficiency
of downstream processes. Also, a poor particle size distribution may lead to
solvent entrainment, and then leading to impurity problems, resulting in a reduced
purity. The process control technology is playing an increasingly important role in
improving yield and purity, ensuring the consistency of crystal products in terms of
particle size and crystal morphology, and avoiding particle coalescence and solvent
encapsulation.
Generally, the explosion nucleation process will promote the crystallization
process to produce fine particles, and the dissolution process will be the dissolution
of fine particles. Therefore, the heating-cooling cycle can eliminate fine crystals
and prepare larger crystals. The DNC strategy is based on the idea that the smaller
the system particles, the larger the product particle size and the temperature cycle
is beneficial to eliminate fine crystals. The DNC strategy shows good consistency
in the crystallization process, because this method does not need to know the
crystallization process model, kinetics, and the width of the metastable zone in
advance, and these parameters change due to the hydrodynamic properties during
the amplification process. Changes often occur, so it is a robust feedback control
strategy. When the number of monitored crystal count changes, DNC automatically
adjusts the operating conditions, which can well overcome the adverse effects
of process disturbances. The most significant feature of the DNC strategy is the
ability to directly monitor and control the crystal properties, which can be achieved
through a controllable growth and dissolution cycle (cooling/heating cycle or anti-
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Fig. 3 Schematic working of ADNC approach (a); effect of temperature cycling on a crystal
suspension (b) (Caption and figure reprinted with permission from Ref. [19]
solvent/solvent addition cycle). The advantage of DNC is that it can produce crystal
products with a more regular particle size in line with expectations [16], reduce
particle coalescence and solvent occlusion [17], and improve the purity of crystal
[18]. This is because the direct nucleation control can inhibit the adsorption of
impurities on the crystal surface by repeatedly dissolving the growth cycle, and
the fine particles and impurities on the crystal surface will dissolve continuously
during the heating process. Thus, in the subsequent cooling process, crystal growth
is promoted, and the crystal with a larger particle size has a smaller specific surface
area, which reduces the adsorption of impurities on the surface, as shown in Fig. 3.
The DNC nucleation strategy is also suitable for anti-solvent crystallization.
Nagy et al. [20] used the DNC strategy to control the CSD of glycine by controlling
the flow of solvent and anti-solvent. Most of the crystallization process indirectly
affects CSD through real-time temperature control or anti-solvent to follow the
supersaturation set in the phase diagram. Using the SSC strategy during the
crystallization process can keep the supersaturation constant at the set value, and
reduce or avoid secondary nucleation during the entire crystallization process to
promote crystal growth. And the image analysis-based direct nucleation control
method based on image processing has a very significant effect on the control of
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