Sustainable Enzymatic Desizing of Cotton with Bio-surfactant Extracted from Soapnut

Textile & Leather Review, Journal Year: 2024, Volume and Issue: 7, P. 327 - 339

Published: Feb. 27, 2024

Surfactant is one of the major consuming auxiliaries in textile processing. The rising demand for petroleum-based surfactants focus and it tremendously utilized to fulfil need industries. These are pollutants wastewater. Many attempts have been made replace this with low toxicity make process sustainable. present investigation works on same objective surfactant from desizing by using soapnut extract as a wetting agent. was optimised modern statistical technique Response Surface Methodology [RSM]. initial designing conducted 10 g/l 2% enzyme 30 min at 75 °C found satisfactory results. Additional experiments were performed optimize RSM weight loss primary outcome. An recipe provided DOE numerical optimisation, viz., concentration 40 min, validate. findings demonstrate that optimum (6.58%) desirable levels absorbency (14 s), whiteness (73.52), yellowness (22.84 indices, bending length (2.1 cm), Flexural rigidity (98.13 mg.cm), while minimally affecting tensile strength (10.77). Enzymatic synthetic or soapnut-extracted agents yields identical results satisfies performance standards industrial use. Sustainable way enzymatic cotton bio-surfactant extracted may be green alternative surfactant-based desizing.

Language: Английский

Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence

Textile & Leather Review, Journal Year: 2024, Volume and Issue: 7, P. 1021 - 1038

Published: June 10, 2024

Nonwoven fabrics are used in many industries, and surface treatment by plasma can significantly change their physical mechanical properties changing the chemistry morphology. In this paper, oxygen/argon has been applied to spunbond nonwoven predict obtained properties. Therefore, using central composite design considering 4 independent factors including fabric weight, direction, duration oxygen ratio, 51 various samples were prepared bending rigidity formability measured. SEM images showed that roughness increases due treatment. Statistical analysis revealed all mentioned have a significant effect on measured parameters directly or reciprocally. Also, use of neural network model with two hidden layers optimized method according genetic algorithm based errors less than 7% 9%, respectively. The resulting from response for same order about 19% 17%. introduced be as tool help researchers field

Language: Английский