Effectiveness of evolutionary algorithms for optimization of heat exchangers

This paper comprehensively investigates performance of evolutionary algorithms for design optimization of shell and tube heat exchangers (STHX). Genetic algorithm (GA), firefly algorithm (FA), and cuckoo search (CS) method are implemented for finding the optimal values for seven key design variables of the STHX model. ε-NTU method and Bell-Delaware procedure are used for thermal modeling of STHX and calculation of shell side heat transfer coefficient and pressure drop. The purpose of STHX optimization is to maximize its thermal efficiency. Obtained results for several simulation optimizations indicate that GA is unable to find permissible and optimal solutions in the majority of cases. In contrast, design variables found by FA and CS always lead to maximum STHX efficiency. Also computational requirements of CS method are significantly less than FA method. As per optimization results, maximum efficiency (83.8%) can be achieved using several design configurations. However, these designs are bearing different dollar costs. Also it is found that the behavior of the majority of decision variables remains consistent in different runs of the FA and CS optimization processes.

Sodium ion dynamics in a sulfonate based ionomer system studied by 23Na solid-state nuclear magnetic resonance and impedance spectroscopy

A poly(2-acrylamido-2-methyl-1-propane-sulphonate) (PAMPS) ionomer containing both sodium and quaternary ammonium cations functionalised with an ether group, has been characterised in terms of its thermal properties, ionic conductivity and sodium ion dynamics. The ether oxygen was incorporated to reduce the Na+ association with the anionic sulfonate groups tethered to the polymer backbone, thereby promoting ion dissociation and ultimately enhancing the ionic conductivity. This functionalised ammonium cation led to a significant reduction in the ionomer Tg compared to an analogue system without an ether group, resulting in an increase in ionic conductivity of approximately four orders of magnitude. The sodium ion dynamics were probed by 23Na solid-state NMR, which allowed the signals from the dissociated (mobile) and bound Na+ cations to be distinguished. This demonstrates the utility of 23Na solid-state NMR as a probe of sodium dynamics in ionomer systems.

Increased ion conduction in dual cation [sodium][tetraalkylammonium] poly[4-styrenesulfonyl(trifluoromethylsulfonyl)imide-co-ethylacrylate] ionomers

© The Royal Society of Chemistry. Solid-state polymer electrolytes, as an alternative to traditional liquid electrolytes, have been intensively investigated for energy conversion and storage devices. The transport rate of single ions is the key to their high performance. For application in emerging sodium batteries, we have developed three dual-cation polymeric ionomers, which contain bulky tetraalkylammonium ions in addition to the sodium ion. The sizes and relative contents of the ammonium ions vary relative to the sodium ion contents. Comparative studies of ion dynamics, thermal properties, phase behaviours and ionic conductivities were carried out, taking advantage of various spectroscopic and thermal chemistry methods. The ion conductivities of the ionomers are greatly enhanced by the introduction of bulky counterions, as a result of the additional free volume and decreased sodium ion association. Raman spectroscopy and thermal analysis as well as the solid-state nuclear magnetic resonance studies are used to probe the conductivity behaviour.

Lingo: linearized grassmannian optimization for nuclear norm minimization

As a popular heuristic to the matrix rank minimization problem, nuclear norm minimization attracts intensive research attentions. Matrix factorization based algorithms can reduce the expensive computation cost of SVD for nuclear norm minimization. However, most matrix factorization based algorithms fail to provide the theoretical guarantee for convergence caused by their non-unique factorizations. This paper proposes an efficient and accurate Linearized Grass-mannian Optimization (Lingo) algorithm, which adopts matrix factorization and Grassmann manifold structure to alternatively minimize the subproblems. More specially, linearization strategy makes the auxiliary variables unnecessary and guarantees the close-form solution for low periteration complexity. Lingo then converts linearized objective function into a nuclear norm minimization over Grass-mannian manifold, which could remedy the non-unique of solution for the low-rank matrix factorization. Extensive comparison experiments demonstrate the accuracy and efficiency of Lingo algorithm. The global convergence of Lingo is guaranteed with theoretical proof, which also verifies the effectiveness of Lingo.