An analysis approach to handle uncertain multi-criteria group decision problems in the framework of interval type-2 fuzzy sets theory

Neural Computing and Applications - Uncertainty is one of the most important aspects of any decision-making process. One of the tools lately used in uncertain decision making is interval type-2...     

Oxidation of Methylene via Sn-adsorbed Boron Nitride Nanocage (B30N30): DFT Investigation

Silicon - In this study, by using of density functional theory calculations, the oxidation of methylene on surface of Tin-doped boron nitride nanocage via Langmuir Hinshelwood and Eley Rideal...     

Robot selection by a multiple criteria complex proportional assessment method under an interval-valued fuzzy environment

Recent research is recognizing that multiple criteria analysis should take account of the concepts of uncertainty and risk. In some cases, precise determination of the exact value of alternatives and weights of criteria is difficult. Consequently, to deal with these potential problems, their values are regarded as fuzzy and intervals. This paper proposes an interval-valued fuzzy multiple criteria complex proportional assessment (IVF-COPRAS) method that can reflect both a subjective judgment and objective information in real-life situations. In this method, the performance rating values versus selected criteria as well as the weights of conflicting criteria are linguistic variables represented by interval-valued triangular fuzzy numbers. Moreover, performances of alternatives against subjective criteria are described via linguistic variables and then transformed into interval-valued triangular fuzzy numbers. Finally, an application example for the robot selection problem is given in this paper to show this decision-making steps and the effectiveness of the proposed method in manufacturing companies.     

Thermodynamic design of a cascade refrigeration system of liquefied natural gas by applying mixed integer non-linear programming

Liquefied natural gas (LNG) is the most economical way of transporting natural gas (NG) over long distances. Liq-uefaction of NG using vapor compression refrigeration system requires high operating and capital cost. Due to lack of systematic design methods for multistage refrigeration cycles, conventional approaches to determine op-timal cycle are largely trial-and-error. In this paper a novel mixed integer non-linear programming (MINLP) model is introduced to select optimal synthesis of refrigeration systems to reduce both operating and capital costs of an LNG plant. Better conceptual understanding of design improvement is illustrated on composite curve (CC) and exergetic grand composite curve (EGCC) of pinch analysis diagrams. In this method a superstruc-ture representation of complex refrigeration system is developed to select and optimize key decision variables in refrigeration cycles (i.e. partition temperature, compression configuration, refrigeration features, refrigerant flow rate and economic trade-off). Based on this method a program (LNG-Pro) is developed which integrates VBA, Refprop and Excel MINLP Solver to automate the methodology. Design procedure is applied on a sample LNG plant to illustrate advantages of using this method which shows a 3.3% reduction in total shaft work consumption.     

Geometry of fiber distribution in Marl yarn using the computational method

In this research, the structure of a type of bi-component composite yarns so called Marl yarn was studied. Fibers packing density factors within the yarn cross section as well as the effect of twisting on the resultant yarn were investigated. Samples of Marl acrylic yarns were provided using different twisting levels. Images of the cross sections of the produced yarn were obtained using the transverse sectioning method. The images were captured using the optical microscopy. The prepared images of the yarn cross section were then analyzed using image processing methods. In the first stage, yarn central coordinates and radiuses were calculated. In order to investigate the uniformity of fibers distribution and packing density within the yarn cross section, the fiber distances from the yarn center within the small radial and polar elements were calculated and the coefficient of variations (CV %) was determined. The maximum value of the fibers packing density was determined. The results showed that the fibers were mostly concentrated within the central region of the yarn; this was followed by the decrease in the fibers packing density while moving into the yarn’s outer layers. It was also concluded that increasing the intensity of the applied twist would result in higher fibers packing as well as the decrease in the yarn radius.     

Characterization of 1050Al/Al3Ni/ZrO2 hybrid surface composites fabricated by friction stir processing

Al/Al₃Ni and Al/nano-ZrO₂ mono and Al/Al₃Ni/ZrO₂ hybrid composites were produced by one- and four-pass friction stir processing (FSP). Then, the microstructure, hardness, and wear performance of the surface composites were evaluated. Results showed that the incorporation of Ni particles into the Al surface and their in situ reaction with the substrate resulted in the development of Al₃Ni particles in the stir zone. The formation mechanism of these particles was deeply studied from both thermodynamics and kinetics aspects. Similarly, the four-pass FSP led to the distribution of ZrO₂ nanoparticles and the formation of Al/ZrO₂ composites. With the addition of both Ni and ZrO₂ particles, a hybrid Al/Al₃Ni/ZrO₂ composite was produced. This caused a 60% improvement in hardness and a 35% improvement in wear resistance of Al substrate. In the case of monolithic composites, both abrasion and adhesion were responsible for the material removal during the wear test, whereas adhesion was specified as the dominant wear mechanism in the hybrid composite.     

Mechanical properties of transparent poly(methyl methacrylate) nanocomposites reinforced with core–shell polyacrylonitrile/poly(methyl methacrylate) nanofibers

Having considered the mechanical and optical properties related to microstructure, the authors of the present work did a study of the in situ interface formation between polyacrylonitrile/poly(methyl methacrylate) (PAN/PMMA) core–shell nanofibers and PMMA resin so as to prepare reinforced PMMA nanocomposites (NCs). The NCs were produced using the dip-coating method. The core–shell nanofibers were generated via phase separation of PAN/PMMA solution during the conventional electrospinning. The results of attenuated total reflection-Fourier transform infrared spectroscopy, transmission electron microscope, and energy dispersive X-ray spectrometer confirmed the formation of core–shell structure of the PAN/PMMA nanofibers. According to the findings of the study, the NCs reinforced with 1.7% volume fractions (v _f) of the core–shell nanofibers, having the composition of 50/50 (PAN/PMMA), had the highest tensile and bending properties. The obtained results showed that by increasing the v _f of nanofibers from 1.7 to 2.9%, the tensile and bending moduli increased by 29.9 and 44.2%, respectively. Increasing v _f to 5.7% decreased the just-mentioned properties. Moreover, the transparency of NCs decreased by less than 1, 10, and 18%, respectively, when the aforementioned volume fractions were applied. The theoretical values for the tensile modulus were calculated using the models proposed by Manera, Pan, and Halpin–Tsai–Nielsen. The best prediction was made when the model proposed by Halpin–Tsai–Nielsen was applied.     

A data‐driven approach to study the role of interconnectors in a future low‐carbon electricity supply system

This paper investigates the potential role of the electricity interconnectors in improving the security of supply in Great Britain (GB) in 2030. Real electricity demand and price data for GB and France in 2016 were used to understand the relationship between power exchange between the two countries and their wholesale electricity prices. A linear programming optimisation model was developed to find the economic power dispatch. Two interconnection links were considered; two‐way trade interconnector with a capacity of 5.4 GW and a 12.3 GW import‐only interconnector between GB and other states. The GB–France link transmits electricity from cheaper system to the more expensive one. The total electricity demand in 2030 will be 406 TWh. Gas‐fired power plants w/wo CCS will provide 83 TWh of the total electricity demand, whereas nuclear power plants will produce 74 TWh. In addition, wind farms and solar PVs are expected to deliver ~120 TWh electricity. CHP units will provide 88 TWh electricity in 2030. The electricity traded between GB and France in 2030 was found to be 33 TWh, which is 160% larger compared with 2016. The power import from France is about 27 TWh and occurs in 59% of the time. For 64% of the time, the interconnector with France is fully loaded. The electricity imported via the 12.3 GW interconnector in 2030 is 1 TWh and mainly occurs during winter‐time when the demand in GB is high. De‐rated capacity margin was calculated based on instantaneous electricity demand and varies between ?2% and 139%. The impact of the price of the imported electricity via the 12.3 GW link was investigated. Increasing the price of the imported electricity via the 12.3 GW link results in a higher capacity factor for all the generation options except the 12.3 GW interconnector link.