Borate-assisted liquid-phase selective oxidation of n-pentane

Oxidation of n-pentane with molecular oxygen to sec-pentanols was performed in the presence of a free radical initiator (di-tert-butyl peroxide) and a boron compound (sec-butyl metaborate), with in situ adsorption of water on molecular sieve 3A. Kinetics of the reaction was studied in a laboratory-scale batch reactor over a broad range of conditions (130‒150°C, 20‒30 bar, 5‒10 vol% "O" _"2" ) in order to establish the optimum parameters for maximising the selectivity and yield of sec-pentanols. Results show that the initiator markedly improves the rate of oxidation, and hence yield, compared to thermal oxidation without an initiator, while the boron species enhances the selectivity to sec-pentanols. Under the conditions investigated, maximum sec-pentanol selectivity is 56% with an alcohol-to-ketone ratio of 3.6:1 for the borate-assisted oxidation compared to 33% and 1.1:1, respectively, for the oxidation without borate. This work demonstrates the feasibility of oxyfunctionalization of n-pentane with industrially relevant selectivity and yield.ExxonMobi

Identification of strategic molecules for future circular supply chains using large reaction networks

Networks of chemical reactions represent relationships between molecules within chemical supply chains and promise to enhance planning of multi-step synthesis routes from bio-renewable feedstocks.</p

Teaching sustainability as complex systems approach: a sustainable development goals workshop

Purpose Approaches to solving sustainability problems require a specific problem-solving mode, encompassing the complexity, fuzziness and interdisciplinary nature of the problem. This paper aims to promote a complex systems’ view of addressing sustainability problems, in particular through the tool of network science, and provides an outline of an interdisciplinary training workshop. Design/methodology/approach The topic of the workshop is the analysis of the Sustainable Development Goals (SDGs) as a political action plan. The authors are interested in the synergies and trade-offs between the goals, which are investigated through the structure of the underlying network. The authors use a teaching approach aligned with sustainable education and transformative learning. Findings Methodologies from network science are experienced as valuable tools to familiarise students with complexity and to handle the proposed case study. Originality/value To the best of the authors’ knowledge, this is the first work which uses network terminology and approaches to teach sustainability problems. This work highlights the potential of network science in sustainability education and contributes to accessible material. </jats:sec

Closed-Loop Multitarget Optimization for Discovery of New Emulsion Polymerization Recipes

Self-optimization of chemical reactions enables faster optimization of reaction conditions or discovery of molecules with required target properties. The technology of self-optimization has been expanded to discovery of new process recipes for manufacture of complex functional products. A new machine-learning algorithm, specifically designed for multiobjective target optimization with an explicit aim to minimize the number of “expensive” experiments, guides the discovery process. This “black-box” approach assumes no a priori knowledge of chemical system and hence particularly suited to rapid development of processes to manufacture specialist low-volume, high-value products. The approach was demonstrated in discovery of process recipes for a semibatch emulsion copolymerization, targeting a specific particle size and full conversion.The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (EC FP7) Grant Agreement no. [NMP2-SL-2012-280827] and EPSRC project “Closed Loop Optimization for Sustainable Chemical Manufacture” [EP/L003309/1]

Continuous-Flow Synthesis and Derivatization of Aziridines through Palladium-Catalyzed C(sp 3 )−H Activation

A continuous‐flow synthesis of aziridines by palladium‐catalyzed C(sp3)−H activation is described. The new flow reaction could be combined with an aziridine‐ring‐opening reaction to give highly functionalized aliphatic amines through a consecutive process. A predictive mechanistic model was developed and used to design the C−H activation flow process and illustrates an approach towards first‐principles design based on novel catalytic reactions.We are grateful to the Department of Chemical Engineering and Biotechnology (J.Z.) and the EPSRC (A.P.S.) for studentships, and to the ERC and EPSRC (EP/100548X/1) for fellowships (M.J.G.). Mass spectroscopy data were acquired at the EPSRC UK National Mass Spectroscopy Facility at Swansea Universit

A new formulation for symbolic regression to identify physico-chemical laws from experimental data

A modification to the mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed with the aim of identification of physical models from noisy experimental data. In the proposed formulation, a binary tree in which equations are represented as directed, acyclic graphs, is fully constructed for a pre-defined number of layers. The introduced modification results in the reduction in the number of required binary variables and removal of redundancy due to possible symmetry of the tree formulation. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the numbers of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. Future work will focus on addressing the limitations of the present formulation and solver to enable extension of target problems to larger, more complex physical models.EPSRC EP/R009902/

Towards automation of chemical process route selection based on data mining

A methodology for chemical routes development and evaluation on the basis of data-mining is presented.This work was in part funded by EPSRC project “Terpene-based manufacturing for sustainable chemical feedstocks” EP/K014889

Recyclable supported Pd-NHC catalytic systems for the copper free Sonogashira cross-coupling in flow

A new family of well-defined NHC-Pd complexes immobilised onto silica, alumina and titania is reported. The catalysts display activity and recyclability in the Sonogashira cross-coupling reactions under batch and continuous flow conditions. Under batch conditions the new catalytic systems were recycled up to four times with yields over 80%. These catalysts have a broad substrate scope of different aryl bromides and alkynes. The titania-supported catalysts show good conversions under continuous flow conditions at least over 8 hours on stream using MeOH as solvent.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (EC FP7) Grant Agreement n. [NMP2-SL-2012-280827]. The authors are grateful to TECSPR14-1-0045; TECNIOspring programme, the Agency for Business Competitiveness of the Government of Catalonia, ACCIÓ, the Generalitat de Catalunya (2014SGR670). and the Spanish Ministerio de Economía y Competitividad (CTQ2016-75016-R) for financial support

Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas-to-liquids

Funder: Chinese Scholarship CouncilFunder: Cambridge Trust; Id: http://dx.doi.org/10.13039/501100003343Funder: National Research Foundation Singapore, CREATE: CARES, C4T ProjectAbstract: We propose a sequential sampling approach to training statistical digital twins. This approach is relevant for real‐world engineering problems with expensive data generation. Prerequisite for building surrogates is sufficient data; however, oversampling does not improve regression accuracy. The time for data generation may be reduced by: (a) applying a classifier to improve data quality and avoid evaluation of infeasible inputs, and (b) employing dynamic sampling linked to regression quality. In dynamic sampling, the initial sampling rate is large to generate enough data for surrogate regression in a few iterations; the sampling rate gradually slows down with the improvement of the iteratively refined surrogate. A dynamic process and a steady‐state process from the field of carbon capture and utilization are used as case studies: pressure swing adsorption (PSA) and gas‐to‐liquids (GTL). The computational costs for surrogates generation are reduced by 86% for PSA and 51% for GTL, compared with employing a static sampling rate

The concept of selectivity control by simultaneous distribution of the oxygen feed and wall temperature in a microstructured reactor

This paper explores the feasibility of controlling the selectivity of a partial oxidation reaction by simultaneous modulation of local oxygen concentration and coolant temperature along the length of a reactor. The microstructured membrane reactor (MMR) concept consists of an oxygen-permeable membrane for distributing the oxygen feed with the coolant channel divided into zones of different temperatures. The reactor concept was explored in simulation of the selective oxidation of o-xylene to phthalic anhydride (PA). A mathematical model of the reactor was developed and optimization performed with the objective of maximising PA selectivity at the reactor outlet. Dosing of oxygen at uniform wall temperature results in PA selectivity increase by 6.3%, albeit with a reduction in o-xylene conversion of about 8% compared to a conventional fixed bed reactor. However, simultaneous modulation of both reactor wall temperatures and local oxygen concentration results in an improved conversion of o-xylene without a detrimental effect on selectivity, thus giving maximum yield of PA.S.M. Aworinde and A.M. Schweidtmann gratefully acknowledge scholarship awards received from Cambridge Trust and the Ernest-Solvay-Foundation, respectively. Work on this paper was enabled in part by the National Research Foundation, Prime Minister’s Office, Singapore, under its CREATE programme