Publications
307 results found
Brown CJ, McGinty J, Islam MT, et al., 2024, Integrated Continuous Process Design for Crystallisation, Spherical Agglomeration, and Filtration of Lovastatin, Journal of Pharmaceutical Innovation, Vol: 19, ISSN: 1872-5120
Purpose: This work seeks to improve the particle processability of needle-like lovastatin crystals and develop a small-footprint continuous MicroFactory for its production. Methods: General conditions for optimal spherical agglomeration of lovastatin crystals and subsequent product isolation are developed, first as batch processes, and then transferred to continuous MicroFactory operation. Results: Methyl isobutyl ketone is a suitable bridging liquid for the spherical agglomeration of lovastatin. Practical challenges including coupling unit operations and solvent systems; mismatched flow rates and inconsistent suspension solid loading were resolved. The successful continuous production of lovastatin spherical agglomerates (D50 = 336 µm) was achieved. Spherical agglomeration increased the density of the bulk lovastatin powder and improved product flowability from poor to good, whilst maintaining lovastatin tablet performance. Conclusion: A continuous, integrated MicroFactory for the crystallisation, spherical agglomeration, and filtration of lovastatin is presented with improved product particle processability. Up to 16,800 doses of lovastatin (60 mg) can be produced per day using a footprint of 23 m2.
Bernet T, Wehbe M, Febra SA, et al., 2024, Modeling the Thermodynamic Properties of Saturated Lactones in Nonideal Mixtures with the SAFT-γ Mie Approach., J Chem Eng Data, Vol: 69, Pages: 650-678, ISSN: 0021-9568
The prediction of the thermodynamic properties of lactones is an important challenge in the flavor, fragrance, and pharmaceutical industries. Here, we develop a predictive model of the phase behavior of binary mixtures of lactones with hydrocarbons, alcohols, ketones, esters, aromatic compounds, water, and carbon dioxide. We extend the group-parameter matrix of the statistical associating fluid theory SAFT-γ Mie group-contribution method by defining a new cyclic ester group, denoted cCOO. The group is composed of two spherical Mie segments and two association electron-donating sites of type e1 that can interact with association electron-accepting sites of type H in other molecules. The model parameters of the new cCOO group interactions (1 like interaction and 17 unlike interactions) are characterized to represent target experimental data of physical properties of pure fluids (vapor pressure, single-phase density, and vaporization enthalpy) and mixtures (vapor-liquid equilibria, liquid-liquid equilibria, solid-liquid equilibria, density, and excess enthalpy). The robustness of the model is assessed by comparing theoretical predictions with experimental data, mainly for oxolan-2-one, 5-methyloxolan-2-one, and oxepan-2-one (also referred to as γ-butyrolactone, γ-valerolactone, and ε-caprolactone, respectively). The calculations are found to be in very good quantitative agreement with experiments. The proposed model allows for accurate predictions of the thermodynamic properties and highly nonideal phase behavior of the systems of interest, such as azeotrope compositions. It can be used to support the development of novel molecules and manufacturing processes.
Lee YS, Jackson G, Galindo A, et al., 2023, A predictive model for the techno-economic assessment of CO<inf>2</inf> chemisorption processes applicable to a large number of amine solvents, Chemical Engineering Research and Design, Vol: 200, Pages: 615-636, ISSN: 0263-8762
With the growing need to reduce carbon dioxide (CO2) emissions, there have been substantial efforts to identify new solvents that can improve the overall performance of chemical-absorption CO2 capture processes. Given the large number of potential solvents, computer-aided molecular and process design (CAMPD) approaches can play a critical role in accelerating the search for optimal solvents by enabling the systematic exploration of solvent candidates and process conditions. One of the challenges in developing such a framework is the requirement for a process model that can be used to capture the interactions between process performance and solvent structure without significantly increasing its numerical complexity. In the current work, a model for the absorption–desorption of CO2 is developed using the predictive SAFT-γ-Mie group-contribution approach, allowing the performance of numerous solvents to be assessed without the need for extensive experimental data. In order to avoid convergence difficulties when solving this highly nonlinear model, a tailored initialization strategy is established, using an adapted inside-out algorithm to prime a nonlinear equation solver for each column, providing a good initial guess for the whole flowsheet. Tests on three solvents confirm the robustness of the approach. Building on this enhanced numerical stability, the model is validated by comparison against pilot-plant data, showing good accuracy. A detailed parametric study of the effect of the key process variables is undertaken; the important role of the CO2 capture rate, of the lean solvent temperature and loading, and of the desorber pressure is highlighted. The results of the parametric study are used to formulate an optimization problem which is successfully solved for four solvents. A large reduction in total annualized cost and energy requirements is achieved by tuning the operating conditions to each solvent considered. The predictive capability, robustness
Gui L, Yu Y, Oliyide TO, et al., 2023, Integrating model-based design of experiments and computer-aided solvent design, Computers & Chemical Engineering, Vol: 177, Pages: 1-15, ISSN: 0098-1354
Computer-aided molecular design (CAMD) methods can be used to generate promising solvents with enhanced reaction kinetics, given a reliable model of solvent effects on reaction rates. Herein, we use a surrogate model parameterised from computer experiments, more specifically, quantum-mechanical (QM) data on rate constants. The choice of solvents in which these computer experiments are performed is critical, considering the cost and difficulty of these QM calculations. We investigate the use of model-based design of experiments (MBDoE) to identify an information-rich solvent set and integrate this within a QM-CAMD framework. We find it beneficial to consider a wide range of solvents in designing the solvent set, using group contribution techniques to predict missing solvent properties. We demonstrate, via three case studies, that the use of MBDoE yields surrogate models with good statistics and leads to the identification of solvents with enhanced predicted performance with few iterations and at low computational cost.
Adjiman CSS, Ferguson ALL, 2023, New Editor-in-Chief and Deputy Editor-in-Chief for <i>MSDE:</i> reflections and vision, MOLECULAR SYSTEMS DESIGN & ENGINEERING, Vol: 8, Pages: 1095-1096, ISSN: 2058-9689
Lee YS, Galindo A, Jackson G, et al., 2023, Enabling the direct solution of challenging computer-aided molecular and process design problems: chemical absorption of carbon dioxide, Computers and Chemical Engineering, Vol: 174, Pages: 1-24, ISSN: 0098-1354
The search for improved CO₂ capture solvents can be accelerated by deploying computer-aided molecular and process design (CAMPD) techniques to explore large molecular and process domains systematically. However, the direct solution of the integrated molecular-process design problem is very challenging as nonlinear interactions between physical properties and process performance render a large proportion of the search space infeasible. We develop a methodology that enables the direct and reliable solution of CAMPD for absorption–desorption processes, using the state-of-the-art SAFT-γ Mie group contribution approach to predict phase and chemical equilibria. We develop new feasibility tests and show them to be highly efficient at reducing the search space, integrating them in an outer-approximation algorithm. The framework is applied to design an aqueous solvent and CO₂ chemical absorption–desorption process, with 150 CAMPD instances across three case studies solved successfully. The optimal solvents are more promising than those obtained with sequential molecular design approaches.
Kirkpatrick L, Adjiman C, ApSimon H, et al., 2023, Systems thinking for the transition to zero pollution, Systems thinking for the transition to zero pollution, www.imperial.ac.uk/grantham, Publisher: Grantham Institute, 40
Systems approaches are vital for coordinating decision-making in the face of complex issues because they provide the whole picture view needed to avoid negative unintended consequences and to generate genuine benefits. This paper explains how systems thinking can be used to address environmental pollution and support decision-makers in finding solutions.
Perdomo FA, Khalit SH, Graham EJ, et al., 2023, A predictive group-contribution framework for the thermodynamic modelling of CO absorption in cyclic amines, alkyl polyamines, alkanolamines and phase-change amines: New data and SAFT- Mie parameters, Fluid Phase Equilibria, Vol: 566, Pages: 1-27, ISSN: 0378-3812
A significant effort is under way to identify improved solvents for carbon dioxide (CO) capture by chemisorption. We develop a predictive framework that is applicable to aqueous solvent + CO mixtures containing cyclic amines, alkyl polyamines, and alkanolamines. A number of the mixtures studied exhibit liquid–liquid phase separation, a behaviour that has shown promise in reducing the energetic cost of CO capture. The proposed framework is based on the SAFT- Mie group-contribution (GC) approach, in which chemical reactions are described via physical association models that allow a simpler, implicit, treatment of the chemical speciation characteristic of these mixtures. We use previously optimized group interaction parameters between some amine groups and water (Perdomo et al., 2021), and develop new group interactions for the cNH, cN, NH2, NH, N, cCHNH, and cCHN groups with CO2; a set of second-order group parameters are also developed to account for proximity effects in some alkanolamines. A combination of literature data and new experimental measurements for the absorption of CO2 in aqueous cyclohexylamine systems obtained in our current work, are used to develop and test the proposed models. The SAFT- Mie GC approach is used to predict the thermodynamics of selected mixtures, including ternary phase diagrams and mixing properties relevant in the context of CO2 capture. The current work constitutes a substantial extension of the range of aqueous amine-based solvents that can be modelled and thus offers the most comprehensive thermodynamically consistent platform to date to screen novel candidate solvents for CO2 capture.
Gui L, Adjiman CS, Galindo A, et al., 2023, Uncovering the most kinetically influential reaction pathway driving the generation of HCN from oxyma/DIC adduct: a theoretical study, Industrial & Engineering Chemistry Research, Vol: 62, Pages: 874-880, ISSN: 0888-5885
The combination of ethyl (hydroxyimino)cyanoacetate (Oxyma) and diisopropylcarbodiimide (DIC) has demonstrated superior performance in amino acid activation for peptide synthesis. However, it was recently reported that Oxyma and DIC could react to generate undesired hydrogen cyanide (HCN) at 20 °C, raising safety concerns for the practical use of this activation strategy. To help minimize the risks, there is a need for a comprehensive investigation of the mechanism and kinetics of the generation of HCN. Here we show the results of the first systematic computational study of the underpinning mechanism, including comparisons with experimental data. Two pathways for the decomposition of the Oxyma/DIC adduct are derived to account for the generation of HCN and its accompanying cyclic product. These two mechanisms differ in the electrophilic carbon atom attacked by the nucleophilic sp2-nitrogen in the cyclization step and in the cyclic product generated. On the basis of computed “observed” activation energies, ΔGobs⧧, the mechanism that proceeds via the attack of the sp2-nitrogen at the oxime carbon is identified as the most kinetically favorable one, a conclusion that is supported by closer agreement between predicted and experimental 13C NMR data. These results can provide a theoretical basis to develop a design strategy for suppressing HCN generation when using Oxyma/DIC for amino acid activation.
Fayaz Torshizi M, Graham E, Adjiman C, et al., 2023, SAFT-Υ force field for the simulation of molecular fluids 9: Coarse-grained models for polyaromatic hydrocarbons describing thermodynamic, interfacial, structural, and transport properties, Journal of Molecular Liquids, Vol: 369, ISSN: 0167-7322
Coarse-grained models of polyaromatic hydrocarbons parametrized by employing the SAFT- Mie approach are presented and assessed by comparison with experimental data and all-atom models in their ability to describe liquid densities, isothermal compressibilities, thermal expansivities, viscosities, and interfacial tensions. The structural behaviour characterized by the center of mass and angular radial distribution functions are also benchmarked. The SAFT- Mie force field models are shown to deliver quantitatively accurate predictions while promising significant speedups in the computational cost of performing molecular dynamics simulations.
Tan BI, Bowskill DH, Keates A, et al., 2023, Improving Transferable Force-Fields for Describing Crystal Structures Containing Hydrogen-Bonds, Computer Aided Chemical Engineering, Pages: 1155-1160
In Crystal Structure Prediction, transferable force-fields (FF) offer an efficient means to construct cheap surrogates of more accurate models. FF development, however, demands large volumes of high-quality reference data which are scarce in experimental databases. In this work, we use periodic DFT-D generated reference data to parameterize a FF apt for predicting properties of crystals in which molecules interact via hydrogen-bonds. Tang-Toennies damping is applied to the underlying Buckingham potential, and several fitting schemes are tested. Our FF achieves good agreement with the energy and geometry training data, for structures with and without hydrogen-bonds. When applied to a validation set, the quality of property predictions are largely preserved, evidence of FF transferability. Testing the popular FIT FF on our data fails to achieve the same accuracy, likely since it was derived from different reference data and lacks the damping function.
Perdomo FA, Jackson G, Galindo A, et al., 2023, An approach for modelling simultaneous fluid-phase and chemical-reaction equilibria in multicomponent systems via Lagrangian duality: The Reactive HELD algorithm., Computer Aided Chemical Engineering, Pages: 973-978
An approach for the calculation of simultaneous phase and chemical-reaction equilibria and stability that does not require any assumptions on the number of stable phases and is applicable to any number of reactions is presented. It is based on an extension of the dual extremum principle concept for non-reactive systems (HELD) formulated in terms of the Helmholtz energy, where additional constraints are introduced that relate molar amounts and extents of reaction. The extended R-HELD algorithm is applied to reacting mixtures such as the esterification of acetic acid and the formation of methyl tert-butyl ether.
Muhieddine MH, Viswanath SK, Armstrong A, et al., 2022, Model-based solvent selection for the synthesis and crystallisation of pharmaceutical compounds, CHEMICAL ENGINEERING SCIENCE, Vol: 264, ISSN: 0009-2509
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- Citations: 4
Graham EJ, Forte E, Burger J, et al., 2022, Multi-objective optimization of equation of state molecular parameters: SAFT-VR Mie models for water, Computers and Chemical Engineering, Vol: 167, ISSN: 0098-1354
The determination of a suitable set of molecular interaction parameters for use with an equation of state (EoS) can be viewed as a multi-objective optimization (MOO) problem, where each objective quantifies the quality of the description for a particular type of thermodynamic property. We outline a methodology for the determination of a set of Pareto-optimal interaction parameters. The Pareto front is generated efficiently using a sandwich algorithm where one solves a sequence of weighted-sum scalarized single objective optimization problems. The algorithm presented can be used for any number of objective functions, allowing for the consideration of multiple thermodynamic property types as competing objectives in the MOO. The methodology is applied to the determination of suitable parameter sets for models of water within the SAFT-VR Mie framework. Three competing property targets are considered as objective functions: saturated-liquid density, vapour pressure and isobaric heat capacity. Two different types of molecular models are considered: spherical models of water, and non-spherical model of water. We analyse the two- and three-dimensional Pareto surfaces and parameter sets obtained for different property combinations in the MOO. The proposed methodology can be used to provide a rigorous comparison between different model types. Numerous Pareto-optimal parameter sets for SAFT-VR Mie water models are documented, and we recommend two new models (one spherical model and one non-spherical model) with an appropriate compromise between the competing objectives.
Sugden IJ, Francia NF, Jensen T, et al., 2022, Rationalising the difference in crystallisability of two sulflowers using efficient <i>in silico</i> methods, CRYSTENGCOMM, Vol: 24, Pages: 6830-6838
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- Citations: 1
Karia T, Adjiman C, Chachuat B, 2022, Assessment of a two-step approach for global optimization of mixed-integer polynomial programs using quadratic reformulation, Computers and Chemical Engineering, Vol: 165, ISSN: 0098-1354
This paper revisits the approach of transforming a mixed-integer polynomial program (MIPOP) into a mixed-integer quadratically-constrained program (MIQCP), in the light of recent progress in global solvers for this latter class of models. We automate this transformation in a new reformulation engine called CANON, alongside preprocessing strategies including local search and bounds tightening. We conduct comparative tests on a collection of 137 MIPOPs gathered from test libraries such as MINLPLib. The solver GUROBI gives the best performance on the reformulated MIQCPs and outperforms the generic global solvers BARON and SCIP. The MIQCP reformulation also improves the performance of SCIP compared to direct MIPOP solution, whereas the performance of BARON is comparable on the original MIPOPs and reformulated MIQCPs. Overall, these results establish the effectiveness of quadratic reformulation for MIPOP global optimization and support its integration into global solvers.
Sugden IJ, Braun DE, Bowskill DH, et al., 2022, Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization, CRYSTAL GROWTH & DESIGN, ISSN: 1528-7483
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- Citations: 7
Beran GJO, Sugden IJ, Greenwell C, et al., 2022, How many more polymorphs of ROY remain undiscovered, CHEMICAL SCIENCE, Vol: 13, Pages: 1288-1297, ISSN: 2041-6520
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- Citations: 25
Muhieddine MH, Viswanath SK, Armstrong A, et al., 2022, Multi-objective optimisation for early-stage pharmaceutical process development, Computer Aided Chemical Engineering, Pages: 2161-2166
The pharmaceutical industry is under constant pressure to deliver its products quickly and effectively while minimising development costs and pursuing green pharmaceutical manufacturing methods. Given the many considerations in process development, a model-based method that takes multiple performance metrics into account is proposed for early process development. Several key performance indicators are identified, namely environmental footprint, cost, and conversion, selectivity, and yield. We employ multi-objective optimisation to assess the trade-offs between capital cost as one objective, and selectivity or conversion as a second objective, while exploring the interdependencies between all performance indicators. The approach is applied to two multiphasic reactions, each occurring in a 6-stage cascade CSTR: the hydrogenation of 4-Isobutylacetophenone (4-IBAP) to 1-(4-Isobutylphenyl) ethanol (4-IBPE) and the carbonylation of 4-IBPE to Ibuprofen (IBP).
Jonuzaj S, Burcham CL, Galindo A, et al., 2022, Optimizing the selection of drug-polymer-water formulations for spray-dried solid dispersions in pharmaceutical manufacturing, Computer Aided Chemical Engineering, Pages: 2185-2190
In this work we present a systematic computer-aided design methodology for identifying optimal drug-polymer-water formulations with desired physical and chemical properties that are used in the spray drying of drug products. Within the proposed method, the UNIFAC model is employed to predict the solubility and miscibility of binary and ternary mixtures, whereas the Gordon-Taylor equation is used to estimate the glass transition temperature of a wide range of chemical blends. The design methodology is applied to the selection of optimal drug-polymer blends that maximize the loading of naproxen, while ensuring that stable formulations are designed. Finally, we explore the trade-offs between two competing objectives through multiobjective optimization, where the drug loading and water-content of API-polymer-water blends are maximized simultaneously. A ranked list of optimal solutions (mixtures with different chemicals and compositions) that can be used to guide experimental work is obtained by introducing integer cut inequalities into the model.
Gui L, Armstrong A, Galindo A, et al., 2022, Computer-aided solvent design for suppressing HCN generation in amino acid activation, Computer Aided Chemical Engineering, Pages: 607-612
A highly toxic compound, hydrogen cyanide (HCN), was discovered to result from the reaction between Ethyl cyano (hydroxyimino) acetate (Oxyma) and diisopropylcarbodiimide (DIC), a popular reagent combination for amino acid activation. The reaction solvent has been found to influence the amount of HCN produced so that judicious solvent choice offers a route to suppressing HCN formation. Given the safety implications and the time-demanding nature of experimental solvent selection, we employ a methodology of quantum mechanical computer-aided molecular design (QM-CAMD) to design a new reaction solvent in order to minimize the amount of HCN formed. In this work, we improve on the original QM-CAMD approach with an enhanced surrogate model to predict the reaction rate constant from several solvent properties. A set of solvents is selected for model regression using model-based design of experiments (MBDoE), where the determinant of the information matrix of the design, known as D-criterion, is maximized. The use of a model-based approach is especially beneficial here as it links the large discrete space of solvent molecules to the reduced space of solvent properties. The resulting surrogate model exhibits an improved adjusted coefficient of determination and leads to more accurate predicted rate constants than the model generated without using MBDoE. The proposed DoE-QM-CAMD algorithm reaches convergence in one iteration. In the future, the main reaction of amino acid activation will be considered to design a solvent that maintains the rate of the main reaction while minimizing HCN generation.
Muhieddine MH, Jonuzaj S, Viswanath SK, et al., 2022, Model-based solvent selection for integrated synthesis, crystallisation and isolation processes, Computer Aided Chemical Engineering, Pages: 601-606
We present a systematic process-wide solvent selection tool based on computer-aided mixture/blend design (CAMbD) (Gani, 2004) for the integrated synthesis, crystallisation and isolation of pharmaceutical compounds. The method proposed simultaneously identifies the solvent and/or anti-solvent mixture, mixture composition and process temperatures that optimise one or more key performance indicators. Additionally, the method entails comprehensive design specifications for the integrated process, such as the miscibility of the synthesis, crystallisation and wash solvents. The design approach is illustrated by identifying solvent mixtures for the synthesis, crystallisation and isolation of mefenamic acid. Furthermore, a multi-objective CAMbD problem is formulated and shows that a mefenamic acid with purity of 98.8% can be achieved without significant loss of process performance in terms of the solvent E-factor.
Lee YS, Jackson G, Galindo A, et al., 2022, Development of a Bi-Objective Optimisation Framework for Mixed-Integer Nonlinear Programming Problems and Application to Molecular Design, Computer Aided Chemical Engineering, Pages: 1225-1230
We present a novel algorithm (SDNBI) to tackle the numerical challenges associated with the solution of bi-objective mixed-integer nonlinear programming problems (BO- MINLPs), with a focus on the exploration of nonconvex regions of the Pareto front. The performance of the algorithm as measured by the accuracy of the resulting approximation of the Pareto front in the disconnected and nonconvex domain of Pareto points is assessed relative to two multi-objective optimisation (MOO) approaches: the sandwich algorithm (SD) and the modified normal boundary intersection (mNBI) method. The features of these MOO algorithms are evaluated using two published benchmark models and a molecular design problem. Initial results indicate that the new algorithm presented outperforms both the SD and the mNBI method in convex, nonconvex-continuous, combinatorial problems, both in terms of computational cost and of the overall quality of the Pareto-optimal set.
Papadopoulos AI, Perdomo FA, Tzirakis F, et al., 2021, Molecular engineering of sustainable phase-change solvents: From digital design to scaling-up for CO<sub>2</sub> capture, CHEMICAL ENGINEERING JOURNAL, Vol: 420, ISSN: 1385-8947
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- Citations: 16
Febra SA, Bernet T, Mack C, et al., 2021, Extending the SAFT-gamma Mie approach to model benzoic acid, diphenylamine, and mefenamic acid: Solubility prediction and experimental measurement, Fluid Phase Equilibria, Vol: 540, Pages: 1-20, ISSN: 0378-3812
The prediction of the solubility of active pharmaceutical ingredients (APIs) is a significant challenge whichis of importance in pharmaceutical applications and solvent selection. Here, we extend the table of groupinteractions (3 like interactions, 47 unlike interactions) of the SAFT-γ Mie group-contribution equation ofstate to model the phase behaviour and solubility of mefenamic acid, a nonsteroidal anti-inflammatorydrug, in a range of solvents. In addition to mefenamic acid, we also consider its molecular synthons:benzoic acid and diphenylamine. New experimental solubility data are presented for the three moleculesin a range of solvents, and three new SAFT-γ Mie functional groups are defined (aCCOOH, aCNHaC andCH3CO) and characterised, together with their interactions with solvent groups. Literature data for thevapour pressure, single-phase density, saturation density, vapourisation enthalpy, bubble temperature,dew temperature, and bubble pressure are used to characterise the new group interactions. Solubilitydata are used to characterise the new group-group interactions only if there are no other experimentaldata available. The transferability and predictive accuracy of the new models are assessed by comparing the theoretical predictions with the experimental solubility data. Our comparison includes alcohols,ketones, and esters as families of solvents and mixed-solvent solubility predictions.
Diamanti A, Ganase Z, Grant E, et al., 2021, Mechanism, kinetics and selectivity of a Williamson ether synthesis: elucidation under different reaction conditions, REACTION CHEMISTRY & ENGINEERING, Vol: 6, Pages: 1195-+, ISSN: 2058-9883
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- Citations: 8
Watson OL, Jonuzaj S, McGinty J, et al., 2021, Computer aided design of solvent blends for hybrid cooling and antisolvent crystallization of active pharmaceutical ingredients, Organic Process Research and Development, Vol: 25, Pages: 1123-1142, ISSN: 1083-6160
Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.
Gu B, Adjiman C, Xu X, 2021, Correlations for concentration polarisation and pressure drop in spacer-filled RO membrane modules based on CFD simulations, Membranes, Vol: 11, ISSN: 2077-0375
Empirical correlations for mass transfer coefficient and friction factor are often used in process models for reverse osmosis (RO) membrane systems. These usually involve four dimensionless groups, namely Reynolds number (Re), Sherwood number (Sh), friction factor (f), and Schmidt number (Sc), with the associated coefficients and exponents being obtained by fitting to experimental data. However, the range of geometric and operating conditions covered by the experiments is often limited. In this study, new dimensionless correlations for concentration polarization (CP) modulus and friction factor are presented, which are obtained by dimensional analysis and using simulation data from computational fluid dynamics (CFD). Two-dimensional CFD simulations are performed on three configurations of spacer-filled channels with 76 combinations of operating and geometric conditions for each configuration, covering a broad range of conditions encountered in RO membrane systems. Results obtained with the new correlations are compared with those from existing correlations in the literature. There is good consistency in the predicted CP with mean discrepancies less than 6%, but larger discrepancies for pressure gradient are found among the various friction factor correlations. Furthermore, the new correlations are implemented in a process model with six spiral wound modules in series and the predicted recovery, pressure drop, and specific energy consumption are compared with a reference case obtained by ROSA (Reverse Osmosis System Analysis, The Dow Chemical Company). Differences in predicted recovery and pressure drop are up to 5% and 83%, respectively, highlighting the need for careful selection of correlations when using predictive models in process design. Compared to existing mass transfer correlations, a distinct advantage of our correlations for CP modulus is that they can be directly used to estimate the impact of permeate flux on CP at a membrane surface without having to r
Adjiman CS, Sahinidis N, Vlachos DG, et al., 2021, Process Systems Engineering Perspective on the Design of Materials and Molecules, INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, Vol: 60, Pages: 5194-5206, ISSN: 0888-5885
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- Citations: 14
Bowskill DH, Sugden IJ, Konstantinopoulos S, et al., 2021, Crystal structure prediction methods for organic molecules: state of the art., Annual Review of Chemical and Biomolecular Engineering, Vol: 14, ISSN: 1947-5438
The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 12 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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