Modeling and control of ibuprofen crystal growth and size distribution

Tran, Anh

doi:10.1016/j.ces.2015.05.033

Cited by 36 publications

(27 citation statements)

References 42 publications

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“…initial concentration of molecule A (mol L −1 ) C A,W aqueous phase concentration of molecule A (mol L −1 ) C P specific heat capacity at constant pressure (J mol −1 K Investigating the feasibility and viability of CPM processes via process modelling and simulation is a rapid and costefficient methodology which can quantitatively evaluate the potential benefits of continuous processes for pharmaceutical production (Gerogiorgis and Ydstie, 2005;Gerogiorgis and Barton, 2009;Jolliffe and Gerogiorgis, 2016a;Teoh et al, 2016). Furthermore, elaborate design and optimisation of multiphase unit operations are also possible, given the vast computational power and robust software now available for high-fidelity simulations ; high-resolution model-based control studies can thus further advance the state of the art (Nayhouse et al, 2015).…”

Section: Nomenclaturementioning

confidence: 99%

Process modelling and simulation for continuous pharmaceutical manufacturing of artemisinin

Jolliffe¹,

Gerogiorgis²

2016

Chemical Engineering Research and Design

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Recent advances in pharmaceutical manufacturing technologies are showing the promise of continuous production: pharmaceutical firms currently rely on mature batch technology but increasingly evaluate the potential of Continuous Pharmaceutical Manufacturing (CPM). Continuous production methods have efficiency, cost, reliability and quality advantages: in this paper we evaluate and quantify these for the case of CPM of artemisinin, a key antimalarial Active Pharmaceutical Ingredient (API). Published reaction and unit operation data are analysed, static models are developed, and continuous plug flow reactors are designed for a reference case producing 100 kg of API per year. The small reactor volumes computed (19.72 mL and 78.72 mL) illustrate the capital expenditure and small footprint benefits of continuous manufacturing. Alternative CPM cases are also developed, whereby different continuous API recovery operations are evaluated in comparison to the base case; the latter employs a reported batch product recovery. Employing published solubility data as well as data estimated using the UNIFAC method, our systematic evaluation identifies ethanol (EtOH) and ethyl acetate (EtOAc) as promising candidate antisolvents for continuous crystallisation. For the same 100 kg per year production level of API, designs using continuous separation techniques can achieve significantly improved E-factor values (22.52 average) compared to the batch process (65.28), implying enhanced sustainability through reduced waste generation. This API of critical societal importance is a very promising candidate for CPM, with the future benefits of performing full analysis and technoeconomic optimisation evident.

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Section: Nomenclaturementioning

confidence: 99%

Process modelling and simulation for continuous pharmaceutical manufacturing of artemisinin

Jolliffe¹,

Gerogiorgis²

2016

Chemical Engineering Research and Design

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“…Apart from addressing the fundamental question of what causes GRD and how to develop models to explain it, there are practical reasons to research GRD as it causes the particle size distribution (PSD) of seeds to broaden over time resulting in poor product quality 1. There is still significant research occurring on the general topic of GRD, including work on measuring the extent of GRD in particular systems [5−11], empirical modeling of GRD in real systems, and use of GRD models with the population balance framework 12–14 as well as further attempts to find physical reasons for GRD 15–17.…”

Section: Introductionmentioning

confidence: 99%

“…Some recent modeling strategies have employed a probabilistic approach with some success. In one, experimental GRD data is used to parameterize a Monte Carlo model for GRD which is then used to control batch crystallization of ibuprofen more effectively than constant temperature or constant supersaturation control schemes 12. Another suggests that GRD is due to Brownian motion of solute molecules affecting local concentration and temperature and therefore supersaturation 15.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Surface Roughness, Internal Crystal Perfection, and Crystal Growth Rate

et al. 2016

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Potential mechanisms affecting growth rate dispersion (GRD) are investigated. Previous studies have identified surface roughness and internal lattice perfection as key mechanisms which are both evaluated with respect to GRD. Crystal growth of potassium dihydrogen phosphate was studied in two solvent mixtures, water and water‐ethanol. The surface roughness was analyzed by atomic force microscopy and the internal crystal perfection by X‐ray diffraction using a synchrotron source. The crystals grown at higher supersaturation have more pronounced and more frequent surface irregularities, supporting previous findings on a feedback mechanism between surface roughness and growth rate. No significant relationship was found between internal crystal perfection and growth rate, however, this is likely due to the size of the crystals analyzed herein and not the absence of any such mechanism in small crystals.

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“…In many studies, the existence of an accurate first-principals dynamic model of the process is assumed. Given such a model, approaches such as linearizing control, 2 and direct model predictive control (MPC) [4][5][6][7][8][9][10][11] can be applied. However, a large part of the reason that batch processes are so ubiquitous is that, compared to their continuous counter-parts, batch processes are relatively simple to design and implement.…”

Section: Introductionmentioning

confidence: 99%

Subspace identification for data‐driven modeling and quality control of batch processes

2016

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In this work, we present a novel, data-driven, quality modeling, and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting linear time-invariant (LTI), dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate polymerization reactor. Results for both disturbance rejection and set-point changes (i.e., new quality grades) are demonstrated.

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Modeling and control of ibuprofen crystal growth and size distribution

Cited by 36 publications

References 42 publications

Process modelling and simulation for continuous pharmaceutical manufacturing of artemisinin

Process modelling and simulation for continuous pharmaceutical manufacturing of artemisinin

Relationship between Surface Roughness, Internal Crystal Perfection, and Crystal Growth Rate

Subspace identification for data‐driven modeling and quality control of batch processes

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