Iterative learning control of crystallisation systems

Under the increasing pressure of issues like reducing the time to market, managing lower production costs, and improving the flexibility of operation, batch process industries thrive towards the production of high value added commodity, i.e. specialty chemicals, pharmaceuticals, agricultural, and biotechnology enabled products. For better design, consistent operation and improved control of batch chemical processes one cannot ignore the sensing and computational blessings provided by modern sensors, computers, algorithms, and software. In addition, there is a growing demand for modelling and control tools based on process operating data. This study is focused on developing process operation data-based iterative learning control (ILC) strategies for batch processes, more specifically for batch crystallisation systems. In order to proceed, the research took a step backward to explore the existing control strategies, fundamentals, mechanisms, and various process analytical technology (PAT) tools used in batch crystallisation control. From the basics of the background study, an operating data-driven ILC approach was developed to improve the product quality from batch-to-batch. The concept of ILC is to exploit the repetitive nature of batch processes to automate recipe updating using process knowledge obtained from previous runs. The methodology stated here was based on the linear time varying (LTV) perturbation model in an ILC framework to provide a convergent batch-to-batch improvement of the process performance indicator. In an attempt to create uniqueness in the research, a novel hierarchical ILC (HILC) scheme was proposed for the systematic design of the supersaturation control (SSC) of a seeded batch cooling crystalliser. This model free control approach is implemented in a hierarchical structure by assigning data-driven supersaturation controller on the upper level and a simple temperature controller in the lower level. In order to familiarise with other data based control of crystallisation processes, the study rehearsed the existing direct nucleation control (DNC) approach. However, this part was more committed to perform a detailed strategic investigation of different possible structures of DNC and to compare the results with that of a first principle model based optimisation for the very first time. The DNC results in fact outperformed the model based optimisation approach and established an ultimate guideline to select the preferable DNC structure. Batch chemical processes are distributed as well as nonlinear in nature which need to be operated over a wide range of operating conditions and often near the boundary of the admissible region. As the linear lumped model predictive controllers (MPCs) often subject to severe performance limitations, there is a growing demand of simple data driven nonlinear control strategy to control batch crystallisers that will consider the spatio-temporal aspects. In this study, an operating data-driven polynomial chaos expansion (PCE) based nonlinear surrogate modelling and optimisation strategy was presented for batch crystallisation processes. Model validation and optimisation results confirmed this approach as a promise to nonlinear control. The evaluations of the proposed data based methodologies were carried out by simulation case studies, laboratory experiments and industrial pilot plant experiments. For all the simulation case studies a detailed mathematical models covering reaction kinetics and heat mass balances were developed for a batch cooling crystallisation system of Paracetamol in water. Based on these models, rigorous simulation programs were developed in MATLAB®, which was then treated as the real batch cooling crystallisation system. The laboratory experimental works were carried out using a lab scale system of Paracetamol and iso-Propyl alcohol (IPA). All the experimental works including the qualitative and quantitative monitoring of the crystallisation experiments and products demonstrated an inclusive application of various in situ process analytical technology (PAT) tools, such as focused beam reflectance measurement (FBRM), UV/Vis spectroscopy and particle vision measurement (PVM) as well. The industrial pilot scale study was carried out in GlaxoSmithKline Bangladesh Limited, Bangladesh, and the system of experiments was Paracetamol and other powdered excipients used to make paracetamol tablets. The methodologies presented in this thesis provide a comprehensive framework for data-based dynamic optimisation and control of crystallisation processes. All the simulation and experimental evaluations of the proposed approaches emphasised the potential of the data-driven techniques to provide considerable advances in the current state-of-the-art in crystallisation control

Population balance model-based optimal control of batch crystallisation processes for systematic crystal size distribution design

During recent years crystallisation has found applications in many chemical industries, such as pharmaceutical, petrochemical, micro-electronics and food industries. Crystallisation is a basic step for purification or separation for a large variety of organic, inorganic and pharmaceutical compounds. Most of the product qualities are directly related to the shape of the crystal size distribution (CSD). The main difficulty in batch crystallisation processes is to accomplish a uniform and reproducible CSD. On-line control during the process allows for improved crystalline product quality, shorter process times and reduction or elimination of compromised batches. The actual prediction and estimation of the shape of the distribution at the end of the batch can provide useful information for monitoring or designing the operating curve for the supersaturation controller. Model-based approaches provide consistency of the CSD, can be used for better control and also for product design by reverse engineering the process to achieve the desired CSD and shape. This research presents a novel methodology for solving the population balance equation (PBE) for the estimation of the shape of the crystal size distribution for batch crystallisation processes. The approach combines the quadrature method of moments (QMOM) and the method of characteristics (MOCH), and provides a computationally efficient technique for the reconstruction of the whole crystal size distribution. The technique was used to estimate the kinetic parameters for the size-dependent growth and secondary nucleation, for potash alum-water system using industrial pilot plant data provided by BASF, Chemical Company. The combined technique was also used to estimate the size-dependent dissolution parameters for potash alum-water system, using laboratory scale data. The QMOM-MOCH solution approach is evaluated in a model-based dynamic optimization study, with the aim to obtain the optimal temperature profiles, which drive the system in both the supersaturated and under-saturated region, to achieve desired target CSD. Using growth, dissolution and nucleation parameters the technique was used to optimise the temperature trajectories to obtain bimodal and mono-modal distributions. The technique can serve as a soft sensor for predicting the CSD, or as a computationally efficient algorithm for off-line design or on-line adaptation of operating policies based on knowledge of the full CSD data. Additionally, the PBE model was solved using the method of characteristics under the assumption of constant supersaturation. At constant supersaturation growth is the dominating phenomenon, yielding a simplified analytical expression for the prediction of the CSD. The research presents the new methodology for the systematic design of the setpoint operating curves for supersaturation controlled crystallisation processes, which produces a desired target crystal size distribution (CSD) at the end of the batch. A design parameter, was introduced as a function of the supersaturation and time, and is evaluated for supersaturation controlled processes. Based on the design parameter and the simplified analytical model, the supersaturation setpoint and batch time are determined using an optimisation approach to obtain a target distribution with a desired shape. Two additional methods are also proposed that use the seed in conjunction with the supersaturation setpoint design, and analytical CSD estimator for shaping the product CSD. The first approach designs a seed recipe as a mixture of crystals resulting for example from standard sieve analysis. In this approach the seed was introduced at the beginning of the batch. The second approach introduces the dynamic seeding concept, which allows an easily implementable methodology to achieve complex target CSDs using seed with mono-modal distribution as a process actuator. These methodologies were validated for potassium dichromate-water system. Size-dependent growth kinetic parameters for the potassium dichromate-water system were identified using as experimental setup developed at Loughborough University. The experiments presented in the thesis also illustrates the simultaneous application of in situ Process Analytical Technology (PAT) tools, such as focused beam reflectance measurement (FBRM) for nucleation detection, attenuated total reflection (ATR) UV/Vis spectroscopy for concentration monitoring, as well as the in-line use of a Mastersizer for real-time CSD measurement in the case of the potassium dichromate in water system. The approaches provide a comprehensive framework for model-based dynamic optimisation of crystallisation processes, which combines efficient numerical solution approaches of the PBE with the formulation of novel optimisation problems. The techniques presented include controlled dissolution, simultaneous optimisation of operating policies and seed recipes and dynamic seeding. Simulation and experimental evaluations of the proposed approaches demonstrate the potential of the techniques to provide significant improvement in the current state-of-the-art in crystallisation control

On-line monitoring and controlling of batch crystallisation using rapid heating and cooling

Batch crystallisation is a common operation in the pharmaceutical and fine chemical industries for purification and separation. With the advent of industrially robust instruments to monitor crystallisation, it is now possible to develop more sophisticated control systems, to better control the crystal size and shape. However, with batch cooling crystallisation, the number of control handles that can be used in any control system is limited; a stirrer speed and heating/ cooling rates are the two that are readily available. The work carried out in this PhD project aims to demonstrate that an online video imaging technique can be used to both monitor and control batch cooling crystallisation. The measuring technique has been developed using low-cost readily available camera and has been utilised to perform measurements of meta-stable zone width (MSZW) at different operating conditions and other key properties of L-glutamic acid and glycine solutions. Nucleation kinetics parameters for both the polythermal and isothermal experiments were calculated according to KBHR method. The outcomes of these experiments exhibited a good agreement with previous workers using more sophisticated measuring methods. Traditional laboratory scale batch cooling systems use one hot/cold source in order to study crystallisation. When information from laboratory is applied to industrial scale, there is inherent issue with heat transfer related to the time constant which industrial systems can respond to. Therefore, in this thesis, a system which has the advantage of introducing a method to rapidly heat and cool a batch crystalliser has been developed. This was achieved by switching the water flow through the crystalliser jacket between hot and cold water baths using six solenoid valves. Different variables were examined; those included the switching frequency and duration as well as the temperature set point of the two baths. It was found that the switching duration had a little effect on the nucleation time and temperature. In contrast, the switching frequency had impact which was more obvious when the ratio became higher either for the cold bath duration to the hot one or vice versa. Moreover, the temperature set point of the hot and cold baths showed to be of great potential for switching effectiveness. For the first time, a comparison of the switching technique, crash cooling and constant linear cooling rate effects on the nucleation point of glycine was presented. It was found that the switching mechanism gives controllable profile by selecting the hot and cold bath temperatures set point and the switching frequency. Therefore, switching method adds an additional level of control not possible with one water bath which is used in traditional cooling profiles (crash and linear). Understanding the heat transfer phenomena in processes that are temperature limited, for instance cooling crystallisation, is of great importance for the overall process efficiency. Consequently, a simple heat transfer model of agitated vessel was developed in this work and showed its ability to predict the vessel temperature in the case of switching between hot and cold baths, programmed heating/cooling rates and crash heating/cooling. The evaluation of the different heat transfer resistances was also considered by using Wilson method. There has recently been increasing emphasis on the control of crystallisation process to obtain particular physical properties for the produced crystals as this has a major effect on the effectiveness of the downstream processes. Accordingly, a control approach that integrated the process video imaging system with the switching technique was developed in this thesis. The experimental findings showed that the developed PVIswitching control system was able to control the crystallisation of LGA and glycine as it improved the overall quality of the crystals produced in terms of size and presence of fines over conventional methods. This was proved by analysing the images captured of the crystals at the end of experiment. In addition, the sensitivity and robustness of the developed control approach were also verified

Population balance model-based optimal control of batch crystallisation processes for systematic crystal size distribution design

During recent years crystallisation has found applications in many chemical industries, such as pharmaceutical, petrochemical, micro-electronics and food industries. Crystallisation is a basic step for purification or separation for a large variety of organic, inorganic and pharmaceutical compounds. Most of the product qualities are directly related to the shape of the crystal size distribution (CSD). The main difficulty in batch crystallisation processes is to accomplish a uniform and reproducible CSD. On-line control during the process allows for improved crystalline product quality, shorter process times and reduction or elimination of compromised batches. The actual prediction and estimation of the shape of the distribution at the end of the batch can provide useful information for monitoring or designing the operating curve for the supersaturation controller. Model-based approaches provide consistency of the CSD, can be used for better control and also for product design by reverse engineering the process to achieve the desired CSD and shape. This research presents a novel methodology for solving the population balance equation (PBE) for the estimation of the shape of the crystal size distribution for batch crystallisation processes. The approach combines the quadrature method of moments (QMOM) and the method of characteristics (MOCH), and provides a computationally efficient technique for the reconstruction of the whole crystal size distribution. The technique was used to estimate the kinetic parameters for the size-dependent growth and secondary nucleation, for potash alum-water system using industrial pilot plant data provided by BASF, Chemical Company. The combined technique was also used to estimate the size-dependent dissolution parameters for potash alum-water system, using laboratory scale data. The QMOM-MOCH solution approach is evaluated in a model-based dynamic optimization study, with the aim to obtain the optimal temperature profiles, which drive the system in both the supersaturated and under-saturated region, to achieve desired target CSD. Using growth, dissolution and nucleation parameters the technique was used to optimise the temperature trajectories to obtain bimodal and mono-modal distributions. The technique can serve as a soft sensor for predicting the CSD, or as a computationally efficient algorithm for off-line design or on-line adaptation of operating policies based on knowledge of the full CSD data. Additionally, the PBE model was solved using the method of characteristics under the assumption of constant supersaturation. At constant supersaturation growth is the dominating phenomenon, yielding a simplified analytical expression for the prediction of the CSD. The research presents the new methodology for the systematic design of the setpoint operating curves for supersaturation controlled crystallisation processes, which produces a desired target crystal size distribution (CSD) at the end of the batch. A design parameter, was introduced as a function of the supersaturation and time, and is evaluated for supersaturation controlled processes. Based on the design parameter and the simplified analytical model, the supersaturation setpoint and batch time are determined using an optimisation approach to obtain a target distribution with a desired shape. Two additional methods are also proposed that use the seed in conjunction with the supersaturation setpoint design, and analytical CSD estimator for shaping the product CSD. The first approach designs a seed recipe as a mixture of crystals resulting for example from standard sieve analysis. In this approach the seed was introduced at the beginning of the batch. The second approach introduces the dynamic seeding concept, which allows an easily implementable methodology to achieve complex target CSDs using seed with mono-modal distribution as a process actuator. These methodologies were validated for potassium dichromate-water system. Size-dependent growth kinetic parameters for the potassium dichromate-water system were identified using as experimental setup developed at Loughborough University. The experiments presented in the thesis also illustrates the simultaneous application of in situ Process Analytical Technology (PAT) tools, such as focused beam reflectance measurement (FBRM) for nucleation detection, attenuated total reflection (ATR) UV/Vis spectroscopy for concentration monitoring, as well as the in-line use of a Mastersizer for real-time CSD measurement in the case of the potassium dichromate in water system. The approaches provide a comprehensive framework for model-based dynamic optimisation of crystallisation processes, which combines efficient numerical solution approaches of the PBE with the formulation of novel optimisation problems. The techniques presented include controlled dissolution, simultaneous optimisation of operating policies and seed recipes and dynamic seeding. Simulation and experimental evaluations of the proposed approaches demonstrate the potential of the techniques to provide significant improvement in the current state-of-the-art in crystallisation control.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Improving continuous crystallisation using process analytical technologies: design of a novel periodic flow process

In this thesis novel configurations and operating strategies in the mixed suspension mixed product removal (MSMPR) crystalliser are investigated, aided by integrated process analytical technologies (PAT) and crystallisation informatics system (CryPRINS) tools. The MSMPR is an idealised crystalliser model that assumes: steady-state operation; well mixed suspension with no product classification, such that all volume elements contain a mixture of particles (small and large) and crystal size distribution (CSD) that is independent of location in the crystalliser and is identical of the product withdrawn; and uniform supersaturation thought, leading to constant nucleation and growth rates. Single-stage MSMPR designs with continuous recycle/recirculation and modified heat exchanger were investigated and found to minimise fouling, encrustation and transfer line blockages. In particular, a modified MSMPR with baffled heat exchanger was found to significantly reduce the temperature between incoming feed hot feed solution and the cooled crystalliser, leading to a significant reduction in fouling, encrustation and blockages. In addition, the concept of the periodic mixed suspension mixed product removal (PMSMPR) crystallisation process is demonstrated for the first time viz single- and multi-stage cascaded operations. This method of operation involves the periodic transfer of slurry (addition and withdrawal) at high flow rates from either a single stirred vessel or between a number of stirred vessels arranged in series. The PMSMPR is therefore characterised by periodic withdrawals of product slurry. Similar to the MSMPR, the product withdrawn from a PMSMPR has exactly the same composition as the vessel at the time of removal. The rapid withdrawal of slurry at high flow rates in PMSMPR operation leads to the prevention of particle sedimentation and blockage of transfer lines. The transfer of slurry (to/from) the PMSMPR is followed by a holding (or pause) period when no addition or withdrawal of slurry takes place. The holding period extends the mean residence time of the PMSMPR relative to a typical MSMPR, thereby increasing the yield and productivity of crystallisation as more time is allowed for consumption of available supersaturation viz crystal growth and nucleation. A state of controlled operation (SCO) in the periodic flow process, defined as a state of the system that maintains itself despite regular, but controlled disruptions was characterised using the PAT tools and CryPRINS within an intelligent decision support (IDS) framework. The crystallisation of paracetamol (PCM) from isopropyl alcohol (IPA) using different configurations of a single-stage continuous MSMPR crystalliser that incorporated continuous recycle and recirculation loop, and a novel design with baffled heat exchanger was investigated. Crystallisations of PCM-IPA carried out in the MSMPR without heat exchanger suffered from severe fouling, encrustation and blockage problems due to the high level of supersaturation (S = 1.39) in the crystalliser, which was required for the initial burst of nucleation to generate enough particles for later growth, as well as the large temperature difference between the incoming feed (45 oC) and the crystalliser (10 oC). Using the modified MSMPR design with baffled heat exchanger, the challenges of fouling, encrustation and blockage were significantly reduced due to the rapid lowering of the feed stream temperature prior to entering the crystalliser. In addition, the closed loop system led to conservation of material, which is a great benefit since large amounts of materials would otherwise be required if the MSMPR was operated with continuous product removal. This design is great for research purposes, in particular, to investigate process design and optimisation. Continuous crystallisation of PCM in the presence of hydroxyl propyl methyl cellulose (HPMC) additive was investigated in the modified MSMPR design with heat exchanger. HPMC was found to improve the crystallisation performance, leading to complete avoidance of fouling, encrustation and blockages at a concentration of 0.05 wt%. However, the yield of crystallisation was significantly reduced (28.0 %) compared to a control experiment (98.8 %, biased due to fouling/encrustation) performed without additive addition. Regardless, the productivity of crystallisation was more than four times that achieved in batch linear cooling (LC) (0.62 0.86 g/L-min) and batch automated dynamic nucleation control (ADNC) (0.24 0.25 g/L-min) runs. Aspects of the periodic flow crystallisation of single- and multi-component (co-crystals) molecular systems have also been examined to demonstrate the concept of state of controlled operation . The single component systems studied were PCM and glycine (GLY), each representative of compounds with slow and fast growth kinetics, respectively. The co-crystal systems investigated were urea-barbituric acid (UBA) and p Toluenesulfonamide-Triphenylphosphine oxide (p-TSA-TPPO). UBA is a polymorphic co-crystal system with three known forms (I, II and III). Form I UBA was successfully isolated in a three-stage periodic flow PMSMPR crystalliser. This study demonstrates the capability of periodic flow crystallisation for isolation of a desired polymorph from a mixture. p-TSA-TPPO exists in two known stoichiometric co-crystal forms, 1:1 and 3:2 mole ratio p-TSA-TPPO, respectively. The two crystalline forms exhibit solution mediated transformation, which proves to be a difficulty for separation. For this study, the implementation of temperature cycles in batch and flow control in semi-batch and periodic PMSMPR crystallisers were investigated to isolate pure 1:1 and 3:2 p-TSA-TPPO, respectively. Different regions of the ternary diagram of p-TSA, TPPO and acetonitrile (MeCN) were investigated. The desired co-crystal form was isolated all crystallisation platforms investigated. However, greater consistency was observed in the semi-batch and PMSMPR operations respectively. Periodic flow crystallisation in PMSMPR is a promising alternative to conventional continuous MSMPR operation, affording greater degrees of freedom operation, slightly narrower RTD profiles, consistent product crystal quality (size, shape and distribution), longer mean residence times, higher yield and productivity and significant reduction in fouling, encrustation and transfer line blockages over prolonged operating periods. Furthermore, the PMSMPR is a versatile platform that can be used to investigate a range of different molecular systems. Relative to batch operation, the PMSMPR can operate close to equilibrium, however, this is dependent on the system kinetics. In addition, retrofitting of batch crystallisers to operate as PMSMPRS fairly simple and require only subtle changes to the existing design space. The integrated array of PAT sensors consisted of attenuated total reflectance ultra violet/visible spectroscopy (ATR-UV/vis), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), focused beam reflectance measurement (FBRM), particle vision microscopy (PVM) and Raman spectroscopy. The results from the studies reported here illustrate very well the use of PAT and information system tools together to determine when the continuous and periodic MSMPR operations reaches a steady-state or state of controlled operation (i.e. periodic steady-state). These tools provided a better understanding of the variables and operating procedures that influence the two types of operations

Process analytical technology based approaches for the monitoring and control of size and polymorphic form in pharmaceutical crystallisation processes

Pharmaceutical crystallisation operation is often critical because it determines product properties, such as the crystal size distribution (CSD) and polymorphic form, that can influence the subsequent downstream operations and the product therapeutic performance. Driven by the United States Food and Drug Administration s (FDA) Process Analytical Technology (PAT) initiative and the Quality-by-Design (QbD) concept, the development of control approaches, which can improve the manufacturing of products with desired properties, has become of significant interest. This thesis presents the development and application of PAT-based approaches for the monitoring and control of pharmaceutical crystallisation operations that will ensure consistent production of active pharmaceutical ingredients (APIs) with the desired size and polymorphic form. The approaches utilised Lasentec focused beam reflectance measurement (FBRM) and attenuated total reflectance ultraviolet (ATR-UV) spectroscopy as the in situ monitoring and control tools. Crystallisations of the APIs that posses multiple polymorphs are both critical and challenging. This was illustrated in this work by the crystallisations of sulfathiazole polymorphs using literature methods. The processes were monitored using FBRM and ATR-UV spectroscopy to define the design range of the process parameters. The defined range could be used as a recipe to reproduce the same quality of crystals. The obtained crystals were characterised using various techniques (optical microscopy, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetry, hot-stage microscopy (HSM), Fourier Transform infrared spectroscopy and powder X-ray diffractometry) to assess the success of the crystallisation processes. The combined results of the techniques showed that all methods were able to produce the desired pure polymorphs. As a contribution to the technique of investigating polymorphism, a combined approach of DSC-HSM with image analysis, was introduced. Results show the capability of the approach to provide a unique insight into the polymorphic transformations and thermal behaviour exhibited by the model compound. The novel direct nucleation control (DNC) approach was introduced to control the CSD. The approach utilises information on nucleation, provided by FBRM, in a feedback control strategy that adapts the process variables, so that the desired CSD of product is achieved. It also provides in situ fines removal through the operating policy, rather than having additional equipment and external recycle loops. The approach does not require concentration measurement and has the advantage of being a model-free approach, requiring no information on nucleation or growth kinetics in order to design an operating curve; the system automatically and adaptively detects the boundary of the operating curve. Experimental results, using glycine in water-ethanol mixture as a model system, show the benefits of DNC to produce larger crystals with narrower CSD compared to uncontrolled operations. The capability of seeded cooling crystallization with temperature cycling approach to control crystal size uniformity and polymorphic purity was evaluated. Using sulfathiazole in n-propanol and in water as model systems, the method was found to accelerate the growth and enhance the size uniformity of the crystals, in comparison with runs using a linear temperature profile, by promoting Ostwald ripening. Although the approach is conceptually capable of controlling polymorphic purity of a system, the effect of solvent-mediated nucleation/growth can be more dominant, as shown by the results of the experiments. The insights into this behaviour of sulfathiazole crystals were captured very well by the FBRM. The study also demonstrated the successful use of a simple non-linear function as a calibration model to relate temperature and absorbance data, obtained using the ATR-UV spectroscopy, to solute concentration during the crystallisation process. The effect of temperature cycling, performed during seeded cooling crystallisation, on the surface features of sulfathiazole crystals was investigated using FBRM and ex situ optical microscopy, SEM and atomic force microscopy. It was observed during the initial stage of the process, the heating phases produced crystals with smooth surfaces, whilst the cooling phases promoted growth of features on the surfaces. These changes detected by the FBRM as an increase in the number of coarse counts during heating and a drop during cooling. Laser beam spreading caused by the surface roughness, and signal/chord splitting due to sharp edges are offered as an explanation for the FBRM results. This shows the capability of the FBRM to provide useful information about the changes on the surface of the crystalline products. The information can be used to avoid problems in the downstream operations, or in the final product property due to variations in flowability and friability, which are influenced by the surface property.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multi-objective optimization and model-based predictive control using state feedback linearization for crystallization

The ongoing Quality-by-Design paradigm shift in the pharmaceutical industry has sparked a new interest in exploring advanced process control techniques to aid the efficient manufacture of high value products. One important process in the manufacturing is crystallization, a key process in purification of active pharmaceutical ingredients (APIs). There has been little crystallization control research in the area of global input/output linearization, otherwise referred to as state-feedback linearization (SFL). The global linearization allows a nonlinear model to be linearized over the whole domain for which the model is valid and can be embedded into a model predictive controller (MPC). MPC allows the control of a process based on a model which captures the physical understanding and constraints, but a widely reported challenge with the SFL technique is the poor ability of explicitly handling the plant constraints, which is not ideal for a highly regulated production environment such as pharmaceutical manufacturing. Therefore, the first purpose of this research is to explore the use of SFL and how it can be applied to controlling batch and continuous MSMPR crystallization processes with the incorporation of plant constraints in the MPC (named SFL-Plant constraints). The contribution made from this research is the exploration of the SFL MPC technique with successful implementation of SFL-Plant constraints. The novelty in this method is that the technique builds on existing SFL-MPC frameworks to incorporate a nonlinear constraints routine which handles plant constraints. The technique is applied on numerous scenarios of batch and continuous mixed suspension mixed product removal (MSMPR) supersaturation control of paracetamol in water, both seeded and unseeded, which all show that the SFL-Plant constraints technique indeed produces feasible control over crystallization subject to constraints imposed by limitations such as heat transfer. The SFL-MPC with SFL-Plant constraints was applied to single-input single-output (SISO) and multiple-input multipleoutput (MIMO) systems, demonstrating consistent success across both schemes of control. It was also determined that the SFL-Plant constraints do increase the computational demand by 2 to 5 times that of the SFL when unconstrained. However, the difference in absolute time is not so significant, typically an MPC which acted on a system each minute required less than 5 seconds of computation time with inclusion of SFL-Plant constraints. This technique 5 presents the opportunity to use the SFL-MPC with real system constraints with little additional computation effort, where otherwise this may have not been possible. A further advancement in this research is the comparison between the SFL-MPC technique to an MPC with a data-driven model - AutoRegression model with eXogenous input (ARX) – which is widely used in industry. An ARX model was identified for batch supersaturation control using a batch crystallization model of paracetamol in isopropyl alcohol (IPA) in gPROMS Formulated Products as the plant, and an ARX model developed in an industrial software for advanced process control – PharmaMV. The ARX-MPC performance was compared with SFL-MPC performance and it was found that although the ARX-MPC performed well when controlling a process which operated around the point the ARX-MPC was initially identified, the capability of tracking the supersaturation profile deteriorated when larger setpoints were targeted. SFL-MPC, on the other hand, saw some deterioration in performance quantified through an increase in output tracking error, but remained robust at tracking a wide range of supersaturation targets, thus outperforming the ARX-MPC for trajectory tracking control. Finally, single-objective and multi-objective optimization of a batch crystallization process is investigated to build on the existing techniques. Two opportunities arose from the literature review. The first was the use of variable-time decision variables in optimization, as it appears all pre-existing crystallization optimization problems to determine the ideal crystallization temperature trajectory for maximising mean-size are constructed of piecewise-constant or piecewise-continuous temperature profiles with a fixed time step. In this research the timestep was added as a decision variable to the optimization problem for each piecewise continuous ramp in the crystallization temperature profile and the results showed that for the maximisation of mean crystal length in a 300-minute batch simulation, when using 10 temperature ramps each of variable length resulted in a 20% larger mean size than that of 10 temperature ramps, each over a fixed time length. The second opportunity was to compare the performance of global evolution based Nondominated Sorting Genetic Algorithm – II (NSGA-II) with a deterministic SQP optimization method and a further hybrid approach utilising first the NSGA-II and then the SQP algorithm. It was found that for batch crystallization optimization, it is possible for the SQP to converge a global solution, and the convergence can be guaranteed in the shortest time with little compromise using the hybrid 6 method if no information is known about the process. The NSGA-II alone required excessive time to reach a solution which is less refined. Finally, a multi-objective optimization problem is formed to assess the ability to gain insight into crystallization operation when there are multiple competing objectives such as maximising the number weighted mean size and minimizing the number weighted coefficient of variation in size. The insight gained from this is that it is more time efficient to perform single-objective optimization on each objective first and then initialize the multi-objective NSGA-II algorithm with the single-objective optimal profiles, because this results in a greatly refined solution in significantly less time than if the NSGA-II algorithm was to run without initialization, the results were approximately 20% better for both mean size and coefficient of variation in 10% of the time with initialization

Digital process design to define and deliver pharmaceutical particle attributes

A digital-first approach to produce quality particles of an active pharmaceutical ingredient across crystallisation, washing and drying is presented, minimising material requirements and experimental burden during development. To demonstrate current predictive modelling capabilities, the production of two particle sizes (D90 = 42 and 120µm) via crystallisation was targeted to deliver a predicted, measurable difference in in vitro dissolution performance. A parameterised population balance model considering primary nucleation, secondary nucleation, and crystal growth was used to select the modes of production for the different particle size batches. Solubility prediction aided solvent selection steps which also considered manufacturability and safety selection criteria. A wet milling model was parameterised and used to successfully produce a 90g product batch with a particle size D90 of 49.3µm, which was then used as the seeds for cooling crystallisation. A rigorous approach to minimising physical phenomena observed experimentally was implemented, and successfully predicted the required conditions to produce material satisfying the particle size design objective of D90 of 120µm in a seeded cooling crystallisation using a 5-stage MSMPR cascade. Product material was isolated using the filtration and washing processes designed, producing 71.2g of agglomerated product with a primary particle D90 of 128µm. Based on experimental observations, the population balance model was reparametrised to increase accuracy by inclusion of an agglomeration terms for the continuous cooling crystallisation. The dissolution performance for the two crystallised products is also demonstrated, and after 45minutes 104.0mg of the D90 of 49.3µm material had dissolved, compared with 90.5mg of the agglomerated material with D90 of 128µm. Overall, 1513g of the model compound was used to develop and demonstrate two laboratory scale manufacturing processes with specific particle size targets. This work highlights the challenges associated with a digital-first approach and limitations in current first-principles models are discussed that include dealing ab initio with encrustation, fouling or factors that affect dissolution other than particle size