236 results found
Adjiman CSJ, Chen Q, Paulavicius R, et al., 2018, An optimization framework to combine operable space maximization with design of experiments., AIChE Journal, 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. This article is protected by copyright. All rights reserved.
Jonuzaj S, Gupta A, Adjiman CS, 2018, The design of optimal mixtures from atom groups using Generalized Disjunctive Programming, Computers and Chemical Engineering, ISSN: 0098-1354
© 2018 The Authors. A comprehensive computer-aided mixture/blend design methodology for formulating a general mixture design problem where the number, identity and composition of mixture constituents are optimized simultaneously is presented in this work. Within this approach, Generalized Disjunctive Programming (GDP) is employed to model the discrete decisions (number and identities of mixture ingredients) in the problems. The identities of the components are determined by designing 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 it possible to consider large design spaces with a broad variety of molecules and mixtures. The proposed methodology is first applied to the design of solvents and solvent mixtures for maximising the solubility of ibuprofen, often sought in crystallization processes; next, antisolvents and antisolvent mixtures are generated for minimising the solubility of the drug in drowning out crystallization; 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. Integer cuts are included in the general models leading to lists of optimal solutions which often contain a combination of pure and mixed solvents.
Kazazakis N, Adjiman CS, 2018, Arbitrarily tight αBB underestimators of general non-linear functions over sub-optimal domains, Journal of Global Optimization, Pages: 1-30, ISSN: 0925-5001
© 2018 The Author(s) In this paper we explore the construction of arbitrarily tight (Formula presented.)BB relaxations of (Formula presented.) 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 (Formula presented.)BB underestimator to provide exact bounds. We subsequently propose a methodology to build (Formula presented.)BB 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 (Formula presented.)-subenergy function and the derivation of (Formula presented.)BB 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 (Formula presented.)-subenergy underestimators, derived using sampling, are compared to similarly derived approximations of the tightest possible classical (Formula presented.)BB underestimators. Our tests show that (Formula presented.)-subenergy underestimators produce much tighter bounds, and succeed in fathoming nodes which are impossible to fathom using classical (Formula presented.)BB.
Adjiman C, Bardow A, 2017, Editorial to iCAMD special issue, Chemical Engineering Science, Vol: 159, Pages: 1-2, ISSN: 0009-2509
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
Diamanti A, Adjiman CS, Piccione PM, et al., 2017, 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
Gu B, Adjiman CS, Xu XY, 2017, 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: 0376-7388
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.
Jonuzaj S, Adjiman CS, 2017, Designing optimal mixtures using generalized disjunctive programming: Hull relaxations, CHEMICAL ENGINEERING SCIENCE, Vol: 159, Pages: 106-130, ISSN: 0009-2509
Nerantzis D, Adjiman CS, 2017, Enclosure of all index-1 saddle points of general nonlinear functions, JOURNAL OF GLOBAL OPTIMIZATION, Vol: 67, Pages: 451-474, ISSN: 0925-5001
Nerantzis D, Adjiman CS, 2017, An interval-matrix branch-and-bound algorithm for bounding eigenvalues, OPTIMIZATION METHODS & SOFTWARE, Vol: 32, Pages: 872-891, ISSN: 1055-6788
Struebing H, Obermeier S, Siougkrou E, et al., 2017, A QM-CAMD approach to solvent design for optimal reaction rates, CHEMICAL ENGINEERING SCIENCE, Vol: 159, Pages: 69-83, ISSN: 0009-2509
Brand CV, Graham E, Rodriguez J, et al., 2016, On the use of molecular-based thermodynamic models to assess the performance of solvents for CO2 capture processes: monoethanolamine solutions, FARADAY DISCUSSIONS, Vol: 192, Pages: 337-390, ISSN: 1359-6640
Chremos A, Forte E, Papaioannou V, et al., 2016, Modelling the phase and chemical equilibria of aqueous solutions of alkanolamines and carbon dioxide using the SAFT-gamma SW group contribution approach, FLUID PHASE EQUILIBRIA, Vol: 407, Pages: 280-297, ISSN: 0378-3812
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: 0026-8976
Gopinath S, Galindo A, Jackson G, et al., 2016, A feasibility-based algorithm for Computer Aided Molecular and Process Design of solvent-based separation systems, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 73-78, ISSN: 1570-7946
Gopinath S, Jackson G, Galindo A, et al., 2016, Outer Approximation Algorithm with Physical Domain Reduction for Computer-Aided Molecular and Separation Process Design, AICHE JOURNAL, Vol: 62, Pages: 3484-3504, ISSN: 0001-1541
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.
Jonuzaj S, Adjiman CS, 2016, A Convex Hull Formulation for the Design of Optimal Mixtures, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 2325-2330, ISSN: 1570-7946
Jonuzaj S, Akula PT, Kleniati P-M, et al., 2016, The Formulation of Optimal Mixtures with Generalized Disjunctive Programming: A Solvent Design Case Study, AICHE JOURNAL, Vol: 62, Pages: 1616-1633, ISSN: 0001-1541
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
Papaioannou V, Calado F, Lafitte T, et al., 2016, Application of the SAFT-gamma Mie group contribution equation of state to fluids of relevance to the oil and gas industry, FLUID PHASE EQUILIBRIA, Vol: 416, Pages: 104-119, ISSN: 0378-3812
Paulavicius R, Kleniati P-M, Adjiman CS, 2016, Global optimization of nonconvex bilevel problems: implementation and computational study of the Branch-and-Sandwich algorithm, 26th European Symposium on Computer Aided Process Engineering (ESCAPE), Publisher: ELSEVIER SCIENCE BV, Pages: 1977-1982, ISSN: 1570-7946
Reilly AM, Cooper RI, Adjiman CS, et al., 2016, Report on the sixth blind test of organic crystal structure prediction methods, ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS, Vol: 72, Pages: 439-459, ISSN: 2052-5206
Sadeqzadeh M, Papaioannou V, Dufal S, et al., 2016, The development of unlike induced association-site models to study the phase behaviour of aqueous mixtures comprising acetone, alkanes and alkyl carboxylic acids with the SAFT-gamma Mie group contribution methodology, FLUID PHASE EQUILIBRIA, Vol: 407, Pages: 39-57, ISSN: 0378-3812
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
Sugden I, Adjiman CS, Pantelides CC, 2016, Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. I. Adaptive local approximate models, ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS, Vol: 72, Pages: 864-874, ISSN: 2052-5206
Burger J, Papaioannou V, Gopinath S, et al., 2015, A hierarchical method to integrated solvent and process design of physical CO2 absorption using the SAFT- Mie approach, AICHE JOURNAL, Vol: 61, Pages: 3249-3269, ISSN: 0001-1541
Gopinath S, Galindo A, Jackson G, et al., 2015, Computer aided molecular and process design using complex process and thermodynamic models: A screening based approach, Pages: 107-109
Copyright © American Institute of Chemical Engineers. All rights reserved. The design of optimal processing materials (molecules) and optimal process variables for a given process is referred to as Computer Aided Molecular and Process Design (CAMPD). Processing materials used to achieve process goals include mass separating agents (such as solvents for absorption, extraction, leaching and adsorbents), catalysts, heat transfer fluids and reaction medium solvents. Choosing processing molecules influences the optimal process variables and vice versa. Molecular and process decision variables are linked, interacting with each other in a complex manner. Hence, neither of these decisions can be made in isolation.
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