An approach to choose the suitable blend ratio of virgin cotton, pre-consumer, and spinning process wastes for rotor-spun yarn using an analytic hierarchy process
Source abstract
Abstract
With rising environmental awareness, and financial rewards, recycled fibres are increasingly being employed in non-woven fabrics, composites, and regenerated fibres. This study investigated the use of recycled fibers with virgin cotton in rotor spinning to produce sustainable yarns. Although pre-consumer and spinning process waste have been studied, limited research has been undertaken on the use of statistical methodologies such as one-way ANOVA and Analytic Hierarchy Process (AHP) to identify the optimum blending ratios based on yarn performance. The study presented a novel technique for optimizing these ratios that included ANOVA, AHP, and cost analysis. In this study, five samples of 84 tex rotor yarn were produced by blending virgin cotton (30%) and flat strips (20%) with variable quantities of comber noil, pre-consumer waste, and hard waste in three different combinations for each. Yarn quality characteristics were assessed, including mass variation, imperfection index, hairiness, tenacity, elongation, and Total Quality Index. Samples D (30:15:20: 15:20) and E (30:10:30: 10:20), made of Virgin Cotton, Comber Noil, Hard Waste, Pre-Consumer Waste, and Flat Strips, had better yarn quality than the other blends. ANOVA ( p < 0.05) revealed substantial changes in yarn quality among blend ratios, whereas AHP ranked samples by important characteristics. The results showed that Sample E exhibited good yarn qualities, which were consistent with both statistical validation and expert opinion. Furthermore, the study emphasized the economic feasibility of appropriate blending, emphasizing attainable raw material savings, minimizing waste, and encouraging sustainability in spinning mills.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Color perception and design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Ahsanullah University of Science and Technology
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