The generalization of total ordering cones and vectorization to separable Hilbert spaces

This research paper reports a study on thermal and sound insulation samples developed from recycled cotton/polyester (recycled cotton/PET) for construction industry applications. The waste recycled cotton and polyester fiber is a potential source of raw material that can be considered for thermal and sound insulation applications, but its quantities are limited. While the quantities are limited, waste recycled cotton fiber was mixed with recycled/PET fiber in 50/50 proportions in the form of two-layer nonwoven mats with a chemical bonding method. The samples such as cotton (color and white), polyester (color and white), and cotton-polyester blend (color and white) were prepared. All the samples were tested for thermal insulation, sound absorption, moisture absorption, and fiber properties as per the ASTM standard. Also, behaviors of six recycled cotton/polyester nonwoven samples under high humidity conditions were evaluated. The sound absorption coefficients were measured according to ASTM E 1050 by an impedance tube method, the sound absorption coefficient over six frequencies 125, 250, 500, 1000, 2000, and 4000 Hz were calculated. The result revealed that nonwoven mats that are prepared from recycled/PET/cotton waste have confirmed more than 70% of the sound absorption coefficient and the recycled nonwoven mats provided the best insulation, sound absorption, moisture absorption, and fiber properties. The recycled waste cotton/ polyester nonwoven mats have adequate moisture resistance at high humidity conditions without affecting the insulation and sound-absorbing properties.

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“…These samples were suitable for roof ceiling insulation applications in a building. The study has conducted by Küçük et al 36…”

Section: Influence Of Thermal Conductivitymentioning

confidence: 99%

Thermal and sound insulation properties of recycled cotton/polyester chemical bonded nonwovens

Sakthivel¹,

Kumar²,

Mekonnen³

et al. 2020

Journal of Engineered Fibers and Fabrics

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“…But they aren't compatible with addition. For example, although ≼ , + ⋠ + for = [3,4], = [5,6], = [−1,3]. ≼ is reflexive, transitive and antisymmetric relation, that is partial order relation.…”

Section: Methodsmentioning

confidence: 99%

“…Any two intervals may not be compared according to these order relations because order relation ≼ is not a total order relation. For example, = [3,5] and = [4,6]. Now, we give some basic properties of order relations ≼ and ≺.…”

Section: Methodsmentioning

confidence: 99%

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Optimality Conditions of Interval-Valued Optimization Problems by Using Subdifferantials

Karaman

2020

Gazi University Journal of Science

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“…Vectorization based on total ordering cones was first introduced by Küçük et al [17,19]. They showed that a set-valued optimization problem can be represented as a vector-valued problem.…”

Section: Introductionmentioning

confidence: 99%

A vectorization for nonconvex set-valued optimization

Karaman¹,

Güvenç²,

Soyertem³

et al. 2018

Turk J Math

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Vectorization is a technique that replaces a set-valued optimization problem with a vector optimization problem. In this work, by using an extension of Gerstewitz function [1], a vectorizing function is defined to replace a given set-valued optimization problem with respect to set less order relation. Some properties of this function are studied. Also, relationships between a set-valued optimization problem and a vector optimization problem, derived via vectorization of this set-valued optimization problem, are examined. Furthermore, necessary and sufficient optimality conditions are presented without any convexity assumption.

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The generalization of total ordering cones and vectorization to separable Hilbert spaces

Cited by 9 publications

References 4 publications

Thermal and sound insulation properties of recycled cotton/polyester chemical bonded nonwovens

Thermal and sound insulation properties of recycled cotton/polyester chemical bonded nonwovens

Optimality Conditions of Interval-Valued Optimization Problems by Using Subdifferantials

A vectorization for nonconvex set-valued optimization

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