265 results found
Kohns M, Lazarou G, Forte E, et al., 2020, Predictive models for the phase behaviour and solution properties of weak electrolytes: nitric, sulfuric and carbonic acid, Physical Chemistry Chemical Physics, Vol: 22, Pages: 15248-15269, ISSN: 1463-9076
The distribution of ionic species in electrolyte systems is important in many fields of science and engineering, ranging from the study of degradation mechanisms to the design of systems for electrochemical energy storage. Often, other phenomena closely related to the ionic speciation, such as ion pairing, clustering and hydrogen bonding, which are difficult to investigate experimentally, are also of interest. Here, we develop an accurate molecular approach, accounting for reactions as well as association and ion pairing, to deliver a predictive framework that helps validate experiment and guides future modelling of speciation phenomena of weak electrolytes. We extend the SAFT-VRE Mie equation of state [D. K. Eriksen et al., Mol. Phys., 2016, 114, 2724–2749] to study aqueous solutions of nitric, sulphuric and carbonic acid, considering complete and partially dissociated models. In order to incorporate the dissociation equilibria, correlations to experimental data for the relevant thermodynamic equilibrium constants of the dissociation reactions are taken from the literature and are imposed as a boundary condition in the calculations. The models for water, the hydronium ion, and carbon dioxide are treated as transferable and are taken from our previous work. Here we present new molecular models for nitric acid, and the nitrate, bisulfate, sulfate, and bicarbonate anions. The resulting framework is used to predict a range of phase behaviour and solution properties of the aqueous acids over wide ranges of concentration and temperature, including the degree of dissociation, as well as the activity coefficients of the ionic species, and the activity of water and osmotic coefficient, density, and vapour pressure of the solutions. The SAFT-VRE Mie models obtained in this manner provide a means of elucidating the mechanisms of association and ion pairing in the systems studied, complementing the experimental observations reported in the literature.
Di Lecce S, Lazarou G, Khalit SH, et al., 2020, Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-gamma Mie (vol 9, pg 38017, 2019), RSC ADVANCES, Vol: 10, Pages: 19463-19465
Lee L, Graham E, Galindo A, et al., 2020, A comparative study of multi-objective optimization methodologies for molecular and process design, Computers and Chemical Engineering, Vol: 136, ISSN: 0098-1354
The need to consider multiple objectives in molecular design, whether based on techno-economic, environmental or health and safety metrics is increasingly recognized. There is, however, limited understanding of the suitability of different multi-objective optimization algorithm for the solution of such design problems. In this work, we present a systematic comparison of the performance of five mixed-integer non-linear programming (MINLP) multi-objective optimization algorithms on the selection of computer-aided molecular design (CAMD) and computer-aided molecular and process design (CAMPD) problems. The five methods are designed to address the discrete and nonlinear nature of the problem, with the aim of generating an accurate approximation of the Pareto front. They include: a weighted sum approach without global search phases (SWS), a weighted sum approach with simulated annealing (SA), a weighted sum approach with multi level single linkage (MLSL), the sandwich algorithm with MLSL and the non dominated sorting genetic algorithm-II (NSGA-II). The algorithms are compared systematically in two steps. The effectiveness of the global search methods is evaluated with SWS, WSSA and WSML. WSML is found to be most effective and a comparative analysis of WSML, SDML and NSGA-II is then undertaken. As a test set of these optimization techniques, two of CAMD and one CAMPD problems of varying dimensionality are formulated as case studies. The results show that the sandwich algorithm with MLSL provides the most efficient generation of a diverse set of Pareto points, leading to the construction of an approximate Pareto front close to exact Pareto front.
Papadopoulos A, Shavalieva G, Papadokonstantakis S, et al., 2020, An approach for simultaneous computer-aided molecular design with holistic sustainability assessment: Application to phase-change CO2 capture solvents, COMPUTERS & CHEMICAL ENGINEERING, Vol: 135, ISSN: 0098-1354
Bowskill DHH, Tropp UE, Gopinath S, et al., 2020, Beyond a heuristic analysis: integration of process and working-fluid design for organic Rankine cycles, MOLECULAR SYSTEMS DESIGN & ENGINEERING, Vol: 5, Pages: 493-510, ISSN: 2058-9689
Paulavicius R, Adjiman CS, 2020, New bounding schemes and algorithmic options for the Branch-and-Sandwich algorithm, JOURNAL OF GLOBAL OPTIMIZATION, Vol: 77, Pages: 197-225, ISSN: 0925-5001
Paulavičius R, Gao J, Kleniati P-M, et al., 2020, BASBL: Branch-And-Sandwich BiLevel solver. Implementation and computational study with the BASBLib test set, Computers & Chemical Engineering, Vol: 132, Pages: 1-23, ISSN: 0098-1354
We describe BASBL, our implementation of the deterministic global optimization algorithm Branch-and-Sandwich for a general class of nonconvex/nonlinear bilevel problems, within the open-source MINOTAUR framework. The solver incorporates the original Branch-and-Sandwich algorithm and modifications proposed in (Paulavičius and Adjiman, J. Glob. Opt., 2019, Submitted). We also introduce BASBLib, an extensive online library of bilevel benchmark problems collected from the literature and designed to enable contributions from the bilevel optimization community. We use the problems in the current release of BASBLib to analyze the performance of BASBL using different algorithmic options and we identify a set of default options that provide good overall performance. Finally, we demonstrate the application of BASBL to a set of flexibility index problems including linear and nonlinear constraints.
Jonuzaj S, Watson OL, Ottoboni S, et al., 2020, Computer-aided Solvent Mixture Design for the Crystallisation and Isolation of Mefenamic Acid, Computer Aided Chemical Engineering, Pages: 649-654
© 2020 Elsevier B.V. We present a systematic computer-aided methodology for the integrated design of solvent blends used in the purification (i.e., crystallisation and isolation) of pharmaceutical compounds. In particular, we investigate the design of optimal solvent mixtures for combined cooling and antisolvent crystallisation, taking into account interlinked design decisions across both crystallisation and isolation (washing) stages. Within the proposed approach, the optimal solvents, antisolvents, the best mixture composition and the optimal process temperatures are determined simultaneously. Furthermore, comprehensive design specifications for both crystallisation and isolation units, such as the miscibility of crystallisation and wash solvents, their environmental impact, and health and safety metrics, are investigated. The design method is applied to identifying potential high-performance solvent blends for the purification of mefenamic acid, while removing an impurity, chlorobenzoic acid, from the system.
Bowskill DH, Sugden IJ, George N, et al., 2020, Efficient Parameterization of a Surrogate Model of Molecular Interactions in Crystals, Computer Aided Chemical Engineering, Pages: 493-498
© 2020 Elsevier B.V. We propose a surrogate model for lattice energy that allows the accurate prediction of the crystal structures formed by a given molecule and their relative stability ranking. The model is derived from a combination of isolated-molecule quantum mechanical calculations and a relatively small number of more expensive solid-state DFT-D computations. The surrogate model provides an effective mechanism for refining the crystal structure landscape predicted by current Crystal Structure Prediction methodologies. Applied to the agrochemical Chlorothalonil, the approach is shown to be highly accurate whilst reducing the computational costs by approximately a factor of 20 compared to refinement of all structures using solid-state DFT.
Golbert J, Adjiman CS, Brandon NP, 2007, Micro-structural Modelling of SOFC Anodes, ECS Transactions, Vol: 7, Pages: 2041-2047
Di Lecce S, Galindo A, Khalit SH, et al., 2019, Modelling and prediction of the thermophysical properties of aqueous mixtures of Choline Geranate and Geranic acid (CAGE) using SAFT-g Mie, RSC Advances: an international journal to further the chemical sciences, Vol: 9, Pages: 38017-38031, ISSN: 2046-2069
Deep eutectic solvents and room temperature ionic liquids are increasingly recognised as appro-priate materials for use as active pharmaceutical ingredients and formulation additives. Aque-ous mixtures of choline and geranate (CAGE), in particular, have been shown to offer promisingbiomedical properties but the understanding of the thermophysical behaviour of these mixturesremains limited. Here, we develop interaction potentials for use in the SAFT–γgroup–contributionapproach, to study the thermodynamic properties and phase behaviour of aqueous mixtures ofcholine geranate and geranic acid. The determination of the interaction parameters betweenchemical functional groups is carried out in a successive fashion, characterising each group basedon those previously developed. The parameters of the groups relevant to geranic acid are esti-mated using experimental phase–equilibrium data such as vapour pressure and saturated–liquiddensity of simple pure components (n–alkenes, branched alkenes and carboxylic acids) and thephase equilibrium data of mixtures (aqueous solutions of branched alkenes and of carboxylicacids). Geranate is represented by further incorporating the anionic carboxylate group, COO−,which is characterised using aqueous solution data of sodium carboxylate salts, assuming fulldissociation of the salt in water. Choline is described by incorporating the cationic quaternaryammonium group, N+, using data on choline choride solutions. The osmotic pressure of aque-ous mixtures of CAGE at several concentrations is predicted and compared to experimental dataobtained as part of our work to assess the accuracy of the modelling platform. The SAFT–γMieapproach is shown to be predictive, providing a good description of the measured data for a widerange of mixtures and properties. Furthermore, the new group interaction parameters neededto represent CAGE extend the set of functional group
Jonuzaj S, Cui J, Adjiman CS, 2019, Computer-aided design of optimal environmentally benign solvent-based adhesive products, Computers and Chemical Engineering, Vol: 130, ISSN: 0098-1354
The manufacture of improved adhesive products that meet specified target properties has attracted increasing interest over the last decades. In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming (GDP), a logic-based framework, to formulate and solve the product design problem. Key design decisions in product design (i.e., how many components should be included in the final product, which active ingredients and solvent compounds should be used and in what proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that number of components in the product formulation does not correlate with performance and that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives (product toxicity and concentration of the active ingredient) is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal ingredients in the product.
Febra SA, Aasen A, Adjiman CS, et al., 2019, Intramolecular bonding in a statistical associating fluid theory of ring aggregates, MOLECULAR PHYSICS, ISSN: 0026-8976
Borhani T, Garcia-Munoz S, Luciani C, et al., 2019, Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs, Physical Chemistry Chemical Physics, Vol: 21, Pages: 13706-13720, 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.
Sugden IJ, Adjiman C, Pantelides C, 2019, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. II. Smoothed intramolecular potentials, Acta Crystallographica Section B: Structural Science, Vol: 75, Pages: 423-433, 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, 2019, Tighter αBB relaxations through a refi nement scheme for the scaled Gerschgorin theorem, Journal of Global Optimization, Vol: 73, Pages: 467-483, ISSN: 0925-5001
Of central importance to the αBB algorithm is the calculation of the α values that guarantee the convexity of the underestimator. Improvement (reduction) of these values can result in tighter underestimators and thus increase the performance of the algorithm. For instance, it was shown by Wechsung et al. (J Glob Optim 58(3):429-438, 2014) that the emergence of the cluster effect can depend on the magnitude of the α values. Motivated by this, we present a refinement method that can improve (reduce) the magnitude of α values given by the scaled Gerschgorin method and thus create tighter convex underestimators for the αBB algorithm. We apply the new method and compare it with the scaled Gerschgorin on randomly generated interval symmetric matrices as well as interval Hessians taken from test functions. As a measure of comparison, we use the maximal separation distance between the original function and the underestimator. Based on the results obtained, we conclude that the proposed refinement method can significantly reduce the maximal separation distance when compared to the scaled Gerschgorin method. This approach therefore has the potential to improve the performance of the αBB algorithm.
Kazepidis P, Papadopoulos AI, Seferlis P, et al., 2019, Optimal design of post combustion CO2 capture processes based on phase-change solvents, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 463-468, ISBN: 978-0-12-819939-8
Lee YS, Graham E, Jackson G, et al., 2019, A comparison of the performance of multi-objective optimization methodologies for solvent design, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 37-42, ISBN: 978-0-12-819939-8
Watson OL, Galindo A, Jackson G, et al., 2019, Computer-aided Design of Solvent Blends for the Cooling and Anti-solvent Crystallisation of Ibuprofen, Editors: Kiss, Zondervan, Lakerveld, Ozkan, Publisher: ELSEVIER SCIENCE BV, Pages: 949-954, ISBN: 978-0-12-819939-8
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 SNAr 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.
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