Exergy optimisation for cascaded thermal storage

Cascaded thermal storage, consisting of multiple Phase Change Materials (PCMs) with different melting temperatures, has been proposed to solve the problem of poor heat transfer caused by unavoidable decrease of temperature differences during heat exchange process. This paper conducts a theoretical study of the overall thermal performance for a cascaded thermal storage system. Both heat transfer rate and exergy efficiency are taken into account. The main findings are: the cascaded arrangement of PCMs enhances the heat transfer rate by up to 30%, whilst it does not always improve the exergy efficiency (-15 to +30%). Enhanced heat transfer and reduced exergy efficiency can both be attributed to the larger temperature differences caused by the cascaded arrangement. A new parameter hex (exergy transfer rate) has been proposed to measure the overall thermal performance. It is defined as the product of heat transfer rate and exergy efficiency, representing the transfer rate of the utilisable thermal energy. The simulation results indicate that the cascaded thermal storage has higher overall thermal performance than the single-staged storage despite of higher exergy efficiency loss.Peer reviewe

Statistics of the network of organic chemistry

A comprehensive analysis of statistical properties of a network of organic reactions reveals several generic traits. This knowledge can be used in the development of optimal reaction sequences.</p

Automatic discovery and optimization of chemical processes

This paper presents the first overview of recent developments in techniques and methods that enable closed-loop optimization, also sometimes called ‘self optimization’, as well as discovery in different areas of molecular sciences. The closed-loop experimental platforms offer tremendous new opportunities by significantly increasing productivity, as well as enabling completely new types of experiments to be performed. Such experiments involve three main enabling technology areas: automated experimental systems, analytical instruments connected to automated chemoinformatics software and optimization or decision-making algorithms. We review the most exciting developments concerning robotic experiments, 3D printed lab-ware, experimental systems with multiple analytical instruments and advanced optimization algorithms based on machine learning approaches. A range of different chemical problems is described, which show the breadth of potential applications of this emerging experimental approach.This work was in part funded by EPSRC project “Closed Loop Optimization for Sustainable Chemical Manufacture” [EP/L003309/1].This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.coche.2015.07.00

Biosynthesis of spathulenol and camphor stand as a competitive route to artemisinin production as revealed by a new chemometric convergence approach based on nine locations’ field-grown Artemesia annua L.

Since isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP) are the universal precursors of both essential oil components, and the antimalarial agent artemisinin and its derivatives in Artemesia annua L., this paper aims to correlate the spotted differences in their concentrations by screening Artemesia annua L. field-grown in nine locations around the world that may reveal the role of any these compounds as precursors or competitors in the biosynthetic pathway of the sesquiterpene lactone : artemisinin. Principal component analysis (PCA) revealed that artemisinin is positively correlated to β-pinene, 1.8-cineole, sabinene hydrate, borneol and 1-octen-3-ol; but negatively to artemisinic acid and β-caryophyllene oxide. Hierarchical cluster analysis (HCA) classified locations into two distinct groups in which artemisinin concentration stood as the main driving factor to build similarities between the locations. In parallel, an improved convergence approach based on idiosyncratic similarities able to capture heterogeneity across individuals is proposed, which was able to classify compounds into four distinct clusters. Artemisinin appeared to be cross-linked to p-cymene, cis-carvyle acetate, 4-terpinene-1-ol, β-caryophyllene, β-farnesene, β-selinene, α-selinene, β-caryophyllene oxide and α-costol. It is interesting to see how camphor and spathulenol behaved as a distinct cluster group, which suggests that biosynthesis of these two compounds follows a different but a competitive pathway ; thus limiting their production could be a key to control and enhance the production of artemisinin

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

Many engineering problems require the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are inadequate. To tackle this problem several algorithms have been developed using surrogates. However, these often have disadvantages such as the requirement of a priori knowledge of the output functions or exponentially scaling computational cost with respect to the number of objectives. In this paper a new algorithm is proposed, TSEMO, which uses Gaussian processes as surrogates. The Gaussian processes are sampled using spectral sampling techniques to make use of Thompson sampling in conjunction with the hypervolume quality indicator and NSGA-II to choose a new evaluation point at each iteration. The reference point required for the hypervolume calculation is estimated within TSEMO. Further, a simple extension was proposed to carry out batch-sequential design. TSEMO was compared to ParEGO, an expected hypervolume implementation, and NSGA-II on 9 test problems with a budget of 150 function evaluations. Overall, TSEMO shows promising performance, while giving a simple algorithm without the requirement of a priori knowledge, reduced hypervolume calculations to approach linear scaling with respect to the number of objectives, the capacity to handle noise and lastly the ability for batch-sequential usage

Hydrodynamic assembly of two-dimensional layered double hydroxide nanostructures.

Formation mechanisms of two-dimensional nanostructures in wet syntheses are poorly understood. Even more enigmatic is the influence of hydrodynamic forces. Here we use liquid flow cell transmission electron microscopy to show that layered double hydroxide, as a model material, may form via the oriented attachment of hexagonal nanoparticles; under hydrodynamic shear, oriented attachment is accelerated. To hydrodynamically manipulate the kinetics of particle growth and oriented attachment, we develop a microreactor with high and tunable shear rates, enabling control over particle size, crystallinity and aspect ratio. This work offers new insights in the formation of two-dimensional materials, provides a scalable yet precise synthesis method, and proposes new avenues for the rational engineering and scalable production of highly anisotropic nanostructures

Feasibility of Using 2,3,3,3-Tetrafluoropropene (R1234yf) as a Solvent for Solid–Liquid Extraction of Biopharmaceuticals

Tetrafluoropropene (R1234yf) is the most likely replacement for tetrafluoroethane (R134a), a widely used refrigerant, propellant, and solvent, characterized by a very high global warming potential. In this study, solvation properties of R1234yf were studied experimentally and computationally for solubility of artemisinin, a precursor to the important biopharmaceutical API, and extraction of artemisinin from biomass. R1234yf was shown to be a poorer solvent than R134a for artemisinin. COSMO-RS calculations of solvation in R1234yf suggest that the decrease in performance is likely due to entropic effects. However, R1234yf was effectively used in solid–liquid extraction of Artemisia annua. The new solvent has shown an increased selectivity to the target metabolite artemisinin. This should allow for design of more selective separation processes based on the new solvent molecule with a low global warming potential of 4 relative to CO2.This work was funded under iCON feasibility projects scheme of EPSRC “Centre for Continuous Manufacturing (CMAC)”, EP/IO33459/1. We acknowledge past funding from Medicine for Malaria Ventures for HPLC instrument and funding by University of Cambridge for the LCMS system