253 results found
Jonuzaj S, Cui J, Adjiman CS, Computer-aided design of optimal environmentally benign solvent-based adhesive products, Computers and Chemical Engineering, ISSN: 0098-1354
The manufacture of improved adhesive products that meet specified target propertieshas attracted increasing interest over the last decades. In this work, a generalsystematic methodology for the design of optimal adhesive products with lowenvironmental impact is presented. The proposed approach integrates computer aideddesign tools and Generalised Disjunctive Programming (GDP), a logic-basedframework, to formulate and solve the product design problem. Key design decisions inproduct design (i.e., how many components should be included in the final product,which active ingredients and solvents compounds should be used and in whatproportions) are optimised simultaneously. This methodology is applied to the designof solvent-based acrylic adhesives, which are commonly used in construction. First,optimal product formulations are determined with the aim to minimize toxicity. Thisreveals that number of components in the product formulation does not correlate withperformance and that high performance can be achieved by investigating differentnumber of components as well as by optimising all ingredients simultaneously ratherthan sequentially. The relation between two competing objectives (product toxicity andconcentration of the active ingredient) is then explored by obtaining a set of Paretooptimal solutions. This leads to significant trade-offs and large areas of discontinuitydriven by discrete changes in the list of optimal ingredients in the product.
Sugden IJ, Adjiman C, Pantelides C, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part II: Smoothed intramolecular potentials, Acta Crystallographica Section B: Structural Science, ISSN: 0108-7681
The application of Crystal Structure Prediction (CSP) to industrially-relevant molecules requires the handling of increasingly large and flexible compounds. We present a revised model for the effect of molecular flexibility on the lattice energy that removes the discontinuities and non-differentiabilities present in earlier models (Sugden et al., 2016), with a view to improving the performance of CSP. The approach is based on the concept of computing a weighted average of local models, and has been implemented within the CrystalPredictor code. Through the comparative investigation of several compounds studied in earlier literature, we show that this new model results in large reductions in computational effort (of up to 65%) and in significant increases in reliability. The approach is further applied to investigate, for the first time, the computational polymorphic landscape of flufenamic acid for Z’=1 structures, resulting in the successful identification of all three experimentally resolved polymorphs within reasonable computational time.
Nerantzis D, Adjiman C, Tighter alphaBB relaxations through a refi nement scheme for the scaled Gerschgorin theorem, Journal of Global Optimization, ISSN: 0925-5001
Of central importance to theαBB algorithm is the calculation oftheαvalues that guarantee the convexity of the underestimator. Improve-ment (reduction) of these values can result in tighter underestimators andthus increase the performance of the algorithm. For instance, it was shown byWechsung et al. (J Glob Optim 58(3):429-438, 2014) that the emergence of thecluster effect can depend on the magnitude of theαvalues. Motivated by this,we present a refinement method that can improve (reduce) the magnitude ofαvalues given by the scaled Gerschgorin method and thus create tighter convexunderestimators for theαBB algorithm. We apply the new method and com-pare it with the scaled Gerschgorin on randomly generated interval symmetricmatrices as well as interval Hessians taken from test functions. As a measureof comparison, we use the maximal separation distance between the originalfunction and the underestimator. Based on the results obtained, we concludethat the proposed refinement method can significantly reduce the maximalseparation distance when compared to the scaled Gerschgorin method. Thisapproach therefore has the potential to improve the performance of theαBBalgorithm.
Watson OL, Galindo A, Jackson G, et al., 2019, Computer-aided Design of Solvent Blends for the Cooling and Anti-solvent Crystallisation of Ibuprofen, Computer Aided Chemical Engineering, Pages: 949-954
© 2019 Elsevier B.V. We present a general computer-aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallisation of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent and anti-solvent molecules, and solvent mixture composition. The SAFT-γ Mie equation of state is used for the first time in the design of crystallisation solvents; based on an equilibrium model, the formulation considers both the crystal yield and solvent consumption. This design formulation is implemented in gPROMS and successfully applied to the crystallisation of ibuprofen, showing that this more general approach to crystallisation design can be used effectively to optimise the desired metrics.
Kazepidis P, Papadopoulos AI, Seferlis P, et al., 2019, Optimal design of post combustion CO<inf>2</inf> capture processes based on phase-change solvents, Computer Aided Chemical Engineering, Pages: 463-468
© 2019 Elsevier B.V. The current work addresses the investigation of phase-change solvents behaviour during the design of post-combustion CO2 capture processes. The use of phase-change solvents leads to energetic gains due to their lower regeneration energy demands. The latter are enhanced in this work by the consideration of systematic structural and operating modifications imposed on a reference absorption/desorption flowsheet. Such modifications are realized with the help of a rigorous and flexible model that can represent the phase-change behaviour and includes stream redistribution options that aim to enhance the main process driving forces. An aqueous N-methylcyclohexylamine (MCA) solution is employed in an effort to exploit the solvent's phase separation behaviour towards the reduction of the total process cost and energy requirements.
Lee YS, Graham E, Jackson G, et al., 2019, A comparison of the performance of multi-objective optimization methodologies for solvent design, Computer Aided Chemical Engineering, Pages: 37-42
© 2019 Elsevier B.V. In this work, we present a systematic comparison of the performance of five mixed-integer non-linear programming (MINLP) multiobjective optimisation algorithms on a computer-aided solvent design problem. The five methods are designed to address the nonconvexity of the problem, with the aim of generating an accurate and complete approximation of the Pareto front. The approaches includes: a weighted sum approach with simulated annealing (SA), a weighted sum approach with multi level single linkage (MLSL), the sandwich algorithm with SA, the sandwich algorithm with MLSL and the non dominated sorting genetic algorithm-II. These five combinations of optimisation techniques are applied to the design of a solvent for chemical absorption of carbon dioxide (CO2). The results shows that the sandwich algorithm with MLSL can efficiently generate diverse Pareto points leading to a construction of more complete Pareto front.
Borhani T, Garcia-Munoz S, Luciani C, et al., A hybrid QSPR model for the prediction of the free energy of solvation of organic solute/solvent pairs, Physical Chemistry Chemical Physics, ISSN: 1463-9076
Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy s in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal/mol for the training set. The best PLS model includes six latent variables, and has a R2 value of 0.91 and a RMSE of 0.52 kcal/mol. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.
Chen Q, Paulavicius R, Adjiman CSJ, et al., 2018, An optimization framework to combine operable space maximization with design of experiments., AIChE Journal, Vol: 64, Pages: 3944-3957, ISSN: 0001-1541
The introduction of Quality by Design in the pharmaceutical industry stimulates practitioners to better understand the relationship of materials, processes and products. One way to achieve this is through the use of targeted experimentation. In this study, we present an optimization framework to design experiments that effectively leverage parameterized process models to maximize the space covered in the output variables while also obtaining an orthogonal bracketing study in the process input factors. The framework considers both multi‐objective and bilevel optimization methods for relating the two maximization objectives. Results are presented for two case studies—a spray coating process and a continuously stirred reactor cascade—demonstrating the ability to generate and identify efficient designs with fit‐for‐purpose trade‐offs between bracketed orthogonality in the input factors and volume explored in the process output space.
Addicoat M, Adjiman CS, Arhangelskis M, et al., 2018, Crystal structure evaluation: calculating relative stabilities and other criteria: general discussion, FARADAY DISCUSSIONS, Vol: 211, Pages: 325-381, ISSN: 1359-6640
Adjiman CSJ, Pantelides C, Gatsiou CA, 2018, Repulsion-dispersion parameters for the modelling of organic molecular crystals containing N, O, S and Cl, Faraday Discussions, Vol: 211, Pages: 297-323, ISSN: 1359-6640
In lattice energy models that combine ab initio and empirical components, it is important to ensureconsistency between these components so that meaningful quantitative results are obtained. Amethod for deriving parameters of atom-atom repulsion dispersion potentials for crystals, tailoredto different ab initio models is presented. It is based on minimization of the sum of squared de-viations between experimental and calculated structures and energies. The solution algorithmis designed to avoid convergence to local minima in the parameter space by combining a deter-ministic low-discrepancy sequence for the generation of multiple initial parameter guesses withan efficient local minimization algorithm. The proposed approach is applied to derive transferableexp-6 potential parameters suitable for use in conjunction with a distributed multipole electrostaticsmodel derived from isolated molecule charge densities calculated at the M06/6-31G(d,p) level oftheory. Data for hydrocarbons, azahydrocarbons, oxohydrocarbons, organosulphur compoundsand chlorohydrocarbons are used for the estimation. A good fit is achieved for the new set ofparameters with a mean absolute error in sublimation enthalpies of 4.1 kJ/mol and an averagermsd15of 0.31 Å. The parameters are found to perform well on a separate cross-validation set of39 compounds.
Adjiman CS, Brandenburg JG, Braun DE, et al., 2018, Applications of crystal structure prediction - organic molecular structures: general discussion, FARADAY DISCUSSIONS, Vol: 211, Pages: 493-539, ISSN: 1359-6640
Jonuzaj S, Gupta A, Adjiman CSJ, 2018, The design of optimal mixtures from atom groups using Generalized Disjunctive Programming, Computers and Chemical Engineering, Vol: 116, Pages: 401-421, ISSN: 1873-4375
A comprehensive computer-aided mixture/blend design methodology for formulating a gen-eral mixture design problem where the number, identity and composition of mixture constituentsare optimized simultaneously is presented in this work. Within this approach, Generalized Dis-junctive Programming (GDP) is employed to model the discrete decisions (number and identitiesof mixture ingredients) in the problems. The identities of the components are determined bydesigning molecules from UNIFAC groups. The sequential design of pure compounds and blends,and the arbitrary pre-selection of possible mixture ingredients can thus be avoided, making itpossible to consider large design spaces with a broad variety of molecules and mixtures. Theproposed methodology is first applied to the design of solvents and solvent mixtures for max-imising the solubility of ibuprofen, often sought in crystallization processes; next, antisolventsand antisolvent mixtures are generated for minimising the solubility of the drug in drowning outcrystallization; and finally, solvent and solvent mixtures are designed for liquid-liquid extraction.The GDP problems are converted into mixed-integer form using the big-M approach. Integercuts are included in the general models leading to lists of optimal solutions which often containa combination of pure and mixed solvents.
Kazazakis N, Adjiman CSJ, 2018, Arbitrarily tight aBB underestimators of general non-linear functions over sub-optimal domains, Journal of Global Optimization, Vol: 71, Pages: 815-844, ISSN: 0925-5001
In this paper we explore the construction of arbitrarily tight αBB relaxations of C2 general non-linear non-convex functions. We illustrate the theoretical challenges of building such relaxations by deriving conditions under which it is possible for an αBB underestimator to provide exact bounds. We subsequently propose a methodology to build αBB underestimators which may be arbitrarily tight (i.e., the maximum separation distance between the original function and its underestimator is arbitrarily close to 0) in some domains that do not include the global solution (defined in the text as “sub-optimal”), assuming exact eigenvalue calculations are possible. This is achieved using a transformation of the original function into a μ-subenergy function and the derivation of αBB underestimators for the new function. We prove that this transformation results in a number of desirable bounding properties in certain domains. These theoretical results are validated in computational test cases where approximations of the tightest possible μ-subenergy underestimators, derived using sampling, are compared to similarly derived approximations of the tightest possible classical αBB underestimators. Our tests show that μ-subenergy underestimators produce much tighter bounds, and succeed in fathoming nodes which are impossible to fathom using classical αBB.
Bowskill D, Sugden I, Gatsiou C-A, et al., 2018, New potentials for accurate and efficient ab initio crystal structure prediction methods, Publisher: INT UNION CRYSTALLOGRAPHY, Pages: E362-E362, ISSN: 2053-2733
Cui J, Jonuzaj S, Adjiman C, 2018, A Comprehensive Approach for the Design of Solvent-based Adhesive Products using Generalized Disjunctive Programming, Amsterdam, Netherlands, 13th International Symposium on Process Systems Engineering (PSE 2018), Publisher: Elsevier B.V., ISSN: 1570-7946
In this work, we present a comprehensive and systematic methodology for the design of optimal adhesive products within the computer-aided product design (CAPD) framework. In the proposed approach, the optimal number, identities and compositions of active ingredients and solvents in the final product are determined simultaneously. Generalised Disjunctive Programming (GDP) is employed to formulate the main design decisions of the problem (i.e., how many ingredients should be included, which active ingredients and solvents compounds should be used and in what proportions). The design methodology has been applied to identifying cheap and environmentally friendly acrylic adhesives, which are commonly used in construction.
Carbon capture and storage (CCS) is broadly recognised as having the potential to play a key role in meeting climate change targets, delivering low carbon heat and power, decarbonising industry and, more recently, its ability to facilitate the net removal of CO2 from the atmosphere. However, despite this broad consensus and its technical maturity, CCS has not yet been deployed on a scale commensurate with the ambitions articulated a decade ago. Thus, in this paper we review the current state-of-the-art of CO2 capture, transport, utilisation and storage from a multi-scale perspective, moving from the global to molecular scales. In light of the COP21 commitments to limit warming to less than 2 °C, we extend the remit of this study to include the key negative emissions technologies (NETs) of bioenergy with CCS (BECCS), and direct air capture (DAC). Cognisant of the non-technical barriers to deploying CCS, we reflect on recent experience from the UK's CCS commercialisation programme and consider the commercial and political barriers to the large-scale deployment of CCS. In all areas, we focus on identifying and clearly articulating the key research challenges that could usefully be addressed in the coming decade.
Grant E, Pan Y, Richardson J, et al., 2018, Multi-Objective Computer-Aided Solvent Design for Selectivity and Rate in Reactions, Computer Aided Chemical Engineering, Pages: 2437-2442
© 2018 Elsevier B.V. A hybrid empirical computer-aided methodology to design the solvent for a reaction, incorporating both selectivity and rate, is presented. A small initial set of diverse solvents is used, for which experimental, in situ kinetic data are obtained. A surrogate model is utilized to correlate the reaction kinetics with solvent properties and a computer-aided molecular design (CAMD) multi-objective optimization problem is then formulated to identify solvents with improved performance compared with the initial solvent set. This methodology is applied to an S N Ar reaction of 2,4-difluoroacetophenone with pyrrolidine, which demonstrates an interesting effect of solvent on both the selectivity of the ortho-:para-substitution ratio and the overall rate of the reaction. A set of Pareto optimal solutions is identified, highlighting the trade-off between reaction rate and selectivity.
Hutacharoen P, Dufal S, Papaioannou V, et al., 2017, Predicting the solvation of organic compounds in aqueous environments: from alkanes and alcohols to pharmaceuticals, Industrial and Engineering Chemistry Research, Vol: 56, Pages: 10856-0876, ISSN: 0888-5885
The development of accurate models to predict the solvation, solubility, and partitioning of nonpolar and amphiphilic compounds in aqueous environments remains an important challenge. We develop state-of-the-art group-interaction models that deliver an accurate description of the thermodynamic properties of alkanes and alcohols in aqueous solution. The group-contribution formulation of the statistical associating fluid theory based on potentials with a variable Mie form (SAFT-γ Mie) is shown to provide accurate predictions of the phase equilibria, including liquid–liquid equilibria, solubility, free energies of solvation, and other infinite-dilution properties. The transferability of the model is further exemplified with predictions of octanol–water partitioning and solubility for a range of organic and pharmaceutically relevant compounds. Our SAFT-γ Mie platform is reliable for the prediction of challenging properties such as mutual solubilities of water and organic compounds which can span over 10 orders of magnitude, while remaining generic in its applicability to a wide range of compounds and thermodynamic conditions. Our work sheds light on contradictory findings related to alkane–water solubility data and the suitability of models that do not account explicitly for polarity.
Adjiman CS, Harrison NM, Weider SZ, 2017, Molecular science and engineering: a powerful transdisciplinary approach to solving grand challenges, Briefing paper, 1
The concept of molecular science and engineering – melding a deep understanding of molecular science with an engineering mind-set – is emerging as a powerful way to create novel, effective and sustainable solutions to global grand challenges, such as the growing threat of antimicrobial resistance. By blurring the boundaries between scientificand engineering disciplines, in this holistic approach, final function and end-use requirements become an integral part of the underlying scientific research. Commercially ready materials can thus become a reality in an accelerated, flexible and economic manner. In other words, molecular science and engineering can fundamentally alter the way molecules are identified and designed for real-world usage. It is not enough to simply make molecules; we must make molecules work for a complex world.The notion of bringing researchers, industry and government communities together to work on grand challenges has a long and illustrious history – think, for instance, of the Manhattan Project, the industrial scale-up of penicillin and the Moon landings. More recently, the idea of ‘convergence’ – tackling grand challenges with a multifaceted array of scientists, engineers, clinicians and beyond – has become more formally recognised as a valuable way to stimulate societally important and ground-breaking research. Molecular science and engineering is a specific, yet far-reaching, part of this convergence landscape.Within the growing worldwide molecular science and engineering community, the Institute for Molecular Science and Engineering (IMSE) was founded in 2015 as Imperial College London’s newest Global Institute. The Institute’s overarching aim is to bring the College’s engineers, scientists, medics and business researchers together with awide array of external stakeholders – and to remove the boundaries between these disciplines – to find innovative molecular-based scie
Adjiman C, Bardow A, 2017, Editorial to iCAMD special issue, Chemical Engineering Science, Vol: 159, Pages: 1-2, ISSN: 0009-2509
Gu B, Adjiman CS, Xu XY, 2016, The effect of feed spacer geometry on membrane performance and concentration polarisation based on 3D CFD simulations, Journal of Membrane Science, Vol: 527, Pages: 78-91, ISSN: 1873-3123
Feed spacers are used in spiral wound reverse osmosis (RO) membrane modules to keep the membrane sheets apart as well as to enhance mixing. They are beneficial to membrane performance but at the expense of additional pressure loss. In this study, four types of feed spacer configurations are investigated, with a total of 20 geometric variations based on commercially available spacers and selected filament angles. The impact of feed spacer design on membrane performance is investigated by means of three-dimensional (3D) computational fluid dynamics (CFD) simulations, where the solution-diffusion model is employed for water and solute transport through RO membranes. Numerical simulation results show that, for the operating and geometric conditions examined, fully woven spacers outperform other spacer configurations in mitigating concentration polarisation (CP). When designed with a mesh angle of 60°, fully woven spacers also deliver the highest water flux, although the associated pressure drops are slightly higher than their nonwoven counterparts. Middle layer geometries with a mesh angle of 30° produce the lowest water flux. On the other hand, spacers with a mesh angle of 90° show the lowest pressure drop among all the filament arrangements examined. Furthermore, the computational model presented here can also be used to predict membrane performance for a given feed spacer type and geometry.
Diamanti A, Adjiman CS, Piccione PM, et al., 2016, Development of Predictive Models of the Kinetics of a Hydrogen Abstraction Reaction Combining Quantum-Mechanical Calculations and Experimental Data, Industrial & Engineering Chemistry Research, Vol: 56, Pages: 815-831, ISSN: 0888-5885
The importance of developing accurate modeling tools for the prediction of reaction kinetics is well recognized. In this work, a thorough investigation of the suitability of quantum mechanical (QM) calculations to predict the effect of temperature on the rate constant of the reaction between ethane and the hydroxyl radical is presented. Further, hybrid models that combine a limited number of QM calculations and experimental data are developed in order to increase their reliability. The activation energy barrier of the reaction is computed using various computational methods, such as B3LYP, M05-2X, M06-2X, MP2 and PMP2, CBS-QB3, and W1BD, with a selection of basis sets. A broad range of values is obtained, including negative barriers for all of the calculations with B3LYP. The rate constants are also obtained for each method, using conventional transition state theory, and are compared with available experimental values at 298 K. The best agreement is achieved with the M05-2X functional with cc-pV5Z basis set. Rate constants calculated at this level of theory are also found to be in good agreement with experimental values at different temperatures, resulting in a mean absolute error of the logarithm (MAEln) of the calculated values of 0.213 over a temperature range of 200–1250 K and 0.108 over a temperature range of 300–499 K. Tunnelling and vibrational anharmonicities are identified as important sources of discrepancies at low and high temperatures, respectively. Hybrid models are proposed and found to provide good correlated rate-constant values and to be competitive with conventional kinetic models, i.e., the Arrhenius and the three-parameter Arrhenius models. The combination of QM-calculated and experimental data sources proves particularly beneficial when fitting to scarce experimental data. The parameters of the model built on the hybrid strategy have a significantly reduced uncertainty (reflected in the much narrower 95% confidence intervals) compa
Sugden IJ, Adjiman CSA, Pantelides C, 2016, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part I: Adaptive local approximate models, Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, Vol: 72, Pages: 864-874, ISSN: 2052-5206
The global search stage of Crystal Structure Prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II (Habgood, M., Sugden, I. J., Kazantsev, A. V., Adjiman, C. S. & Pantelides, C. C. (2015). J Chem Theory Comput 11, 1957-1969) program is presented, where the most efficient use of computational effort is ensured via the use of adaptive Local Approximate Model (LAM) placement. The entire search space of relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimise the computational effort expended in high energy regions whilst maximising the accuracy in low energy regions. As the size, complexity, and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule XXVI from the sixth blind test (Reilly et al., (2016). Acta Cryst. B, 72, 439-459), which is challenging due to its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions.
Eriksen DK, Lazarou G, Galindo A, et al., 2016, Development of intermolecular potential models for electrolyte solutions using an electrolyte SAFT-VR Mie equation of state, Molecular Physics, Vol: 114, Pages: 2724-2749, ISSN: 1362-3028
We present a theoretical framework and parameterisation of intermolecular potentials for aqueous electrolyte solutions using the statistical associating fluid theory based on the Mie interaction potential (SAFT-VR Mie), coupled with the primitive, non-restricted mean-spherical approximation (MSA) for electrolytes. In common with other SAFT approaches, water is modelled as a spherical molecule with four off-centre association sites to represent the hydrogen-bonding interactions; the repulsive and dispersive interactions between the molecular cores are represented with a potential of the Mie (generalised Lennard-Jones) form. The ionic species are modelled as fully dissociated, and each ion is treated as spherical: Coulombic ion–ion interactions are included at the centre of a Mie core; the ion–water interactions are also modelled with a Mie potential without an explicit treatment of ion–dipole interaction. A Born contribution to the Helmholtz free energy of the system is included to account for the process of charging the ions in the aqueous dielectric medium. The parameterisation of the ion potential models is simplified by representing the ion–ion dispersive interaction energies with a modified version of the London theory for the unlike attractions. By combining the Shannon estimates of the size of the ionic species with the Born cavity size reported by Rashin and Honig, the parameterisation of the model is reduced to the determination of a single ion–solvent attractive interaction parameter. The resulting SAFT-VRE Mie parameter sets allow one to accurately reproduce the densities, vapour pressures, and osmotic coefficients for a broad variety of aqueous electrolyte solutions; the activity coefficients of the ions, which are not used in the parameterisation of the models, are also found to be in good agreement with the experimental data. The models are shown to be reliable beyond the molality range considered during parameter estimatio
Smit B, Styring P, Wilson G, et al., 2016, Modelling - from molecules to megascale: general discussion, Faraday Discussions, Vol: 192, Pages: 493-509, ISSN: 1359-6640
Brand CV, Graham E, Rodriguez J, et al., 2016, On the use of molecular-based thermodynamic models to assess theperformance of solvents for CO₂capture processes:monoethanolamine solutions, Faraday Discussions, Vol: 192, Pages: 337-390, ISSN: 1364-5498
Predictive models play an important role in the design of post-combustion processes for the capture of carbon dioxide (CO2) emitted from power plants. A rate-based absorber model is presented to investigate the reactive capture of CO2 using aqueous monoethanolamine (MEA) as a solvent, integrating a predictive molecular-based equation of state: SAFT-VR SW (Statistical Associating Fluid Theory-Variable Range, Square Well). A distinctive physical approach is adopted to model the chemical equilibria inherent in the process. This eliminates the need to consider reaction products explicitly and greatly reduces the amount of experimental data required to model the absorber compared to the more commonly employed chemical approaches. The predictive capabilities of the absorber model are analyzed for profiles from 10 pilot plant runs by considering two scenarios: (i) no pilot-plant data are used in the model development; (ii) only a limited set of pilot-plant data are used. Within the first scenario, the mass fraction of CO2 in the clean gas is underestimated in all but one of the cases, indicating that a best-case performance of the solvent can be obtained with this predictive approach. Within the second scenario a single parameter is estimated based on data from a single pilot plant run to correct for the dramatic changes in the diffusivity of CO2 in the reactive solvent. This parameter is found to be transferable for a broad range of operating conditions. A sensitivity analysis is then conducted, and the liquid viscosity and diffusivity are found to be key properties for the prediction of the composition profiles. The temperature and composition profiles are sensitive to thermodynamic properties that correspond to major sources of heat generation or dissipation. The proposed modelling framework can be used as an early assessment of solvents to aid in narrowing the search space, and can help in determining target solvents for experiments and more detailed modelling.
Struebing H, Obermeier S, Siougkrou E, et al., 2016, A QM-CAMD approach to solvent design for optimal reaction rates, Chemical Engineering Science, Vol: 159, Pages: 69-83, ISSN: 1873-4405
The choice of solvent in which to carry out liquid-phase organic reactions often has a largeimpact on reaction rates and selectivity and is thus a key decision in process design. A systematicmethodology for solvent design that does not require any experimental data on the effect ofsolvents on reaction kinetics is presented. It combines quantum mechanical computations forthe reaction rate constant in various solvents with a computer-aided molecular design (CAMD)formulation. A surrogate model is used to derive an integrated design formulation that combineskinetics and other considerations such as phase equilibria, as predicted by group contributionmethods. The derivation of the mixed-integer nonlinear formulation is presented step-by-step.In the application of the methodology to a classic SN2 reaction, the Menschutkin reaction,the reaction rate is used as the key performance objective. The results highlight the tradeoffsbetween different chemical and physical properties such as reaction rate constant, solventdensity and solid reactant solubility and lead to the identification of several promising solventsto enhance reaction performance.
Gu B, Xu XY, Adjiman CS, 2016, A predictive model for spiral wound reverse osmosis membrane modules: The effect of winding geometry and accurate geometric details, Computers and Chemical Engineering, Vol: 96, Pages: 248-265, ISSN: 1873-4375
A new one-dimensional predictive model for spiral wound modules (SWMs) applied to reverse osmosis membrane systems is developed by incorporating a detailed description of the geometric features of SWMs and considering flow in two directions. The proposed model is found to capture existing experimental data well, with similar accuracy to the widely-used plate model in which the SWM is assumed to consist of multiple thin rectangular channels. However, physical parameters that should in principle be model-independent, such as membrane permeability, are found to differ significantly depending on which model is used, when the same data sets are used for parameter estimation. Conversely, when using the same physical parameter values in both models, the water recovery predicted by the plate-like model is 12–20% higher than that predicted by the spiral model. This discrepancy is due to differences in the description of geometric features, in particular the active membrane area and the variable channel heights through the module, which impact on predicted performance and energy consumption. A number of design variables – the number of membrane leaves, membrane dimensions, centre pipe radius and the height of feed and permeate channels – are varied and their effects on performance, energy consumption and calculated module size are analysed. The proposed spiral model provides valuable insights into the effects of complex geometry on the performance of the SWM as well as of the overall system, at a low computational cost.
Papadopoulos AI, Badr S, Chremos A, et al., 2016, Computer-aided molecular design and selection of CO2 capture solvents based on thermodynamics, reactivity and sustainability, Molecular Systems Design & Engineering, Vol: 1, Pages: 313-334, ISSN: 2058-9689
The identification of improved carbon dioxide (CO2) capture solvents remains a challenge due to the vast number of potentially-suitable molecules. We propose an optimization-based computer-aided molecular design (CAMD) method to identify and select, from hundreds of thousands of possibilities, a few solvents of optimum performance for CO2 chemisorption processes, as measured by a comprehensive set of criteria. The first stage of the approach involves a fast screening stage where solvent structures are evaluated based on the simultaneous consideration of important pure component properties reflecting thermodynamic, kinetic, and sustainability behaviour. The impact of model uncertainty is considered through a systematic method that employs multiple models for the prediction of performance indices. In the second stage, high-performance solvents are further selected and evaluated using a more detailed thermodynamic model, i.e. the group-contribution statistical associating fluid theory for square well potentials (SAFT-γ SW), to predict accurately the highly non-ideal chemical and phase equilibrium of the solvent–water–CO2 mixtures. The proposed CAMD method is applied to the design of novel molecular structures and to the screening of a data set of commercially available amines. New molecular structures and commercially-available compounds that have received little attention as CO2 capture solvents are successfully identified and assessed using the proposed approach. We recommend that these solvents should be given priority in experimental studies to identify new compounds.
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