METHODS FOR DEVELOPING A REGRESSION MODEL FOR FIBER SEPARATION IN WASTE AND DETERMINING MACHINE PRODUCTIVITY

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Rashid Raxmonkulov
Shavkat Ermatov

Abstract

This work investigates the formulation of a regression model to assess fibre separation efficiency and machine productivity in textile waste processing. The experiment examined two primary variables: the distance between the grid and drum pins (x₁) and the drum rotation speed (x₂), with fibre cleaning efficiency (Y₁) as the result variable. A rotating centre composite design was used to develop the experimental plan. Regression coefficients were computed and confirmed using Student’s t-test and Fisher’s criteria to confirm model adequacy. The resultant model precisely forecasts cleaning efficiency at various operational tiers. The optimal efficiency was seen at a grid-to-drum spacing of 9 mm and a drum speed of 700–740 rpm. The experimental findings demonstrated a maximum divergence of 5% from theoretical predictions, therefore affirming the model's trustworthiness. The research establishes a solid foundation for enhancing fibre cleaning apparatus and aids in augmenting the efficiency of cotton waste recycling operations.

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How to Cite

METHODS FOR DEVELOPING A REGRESSION MODEL FOR FIBER SEPARATION IN WASTE AND DETERMINING MACHINE PRODUCTIVITY. (2025). Innovative: International Multidisciplinary Journal of Applied Technology (2995-486X), 3(6), 20-25. https://multijournals.org/index.php/innovative/article/view/3537

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