Methodology For Predicting Fiber Quality And Developing An Intelligent Control System

Abstract

This article investigates, through a comprehensive approach, the issues of predicting fiber quality and developing an intelligent control system based on the initial technological parameters of raw cotton during primary processing. The experiments evaluated the influence of initial moisture content and contamination level of raw cotton, drying drum capacity, and drying agent temperature on fiber quality using a multivariate regression model. The model demonstrated a coefficient of determination of R² = 0.87, indicating high predictive accuracy