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@article{179920,
        author = {M. Sabari Ramachandran and A. Mohamed Thahir},
        title = {OPTIMISING POLYCOTTON RECYCLING PROCESS WITH  DATA MINING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8436-8440},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179920},
        abstract = {The existing polycotton processing system 
relies on baseline weight measurements, assuming a 
65% polyester and 35% cotton blend, to guide 
processing. Key components include wastage analysis to 
quantify losses and assess usable cotton, an ammonia 
bicarbonate additive module for chemical processing, 
and a recovery module to evaluate material efficiency. 
However, the system faces limitations such as poor data 
integration, manual and error-prone wastage analysis, 
inefficient additive use due to manual control, and no 
predictive optimization. 
The proposed system enhances efficiency and quality 
through full data integration and real-time analytics. It 
dynamically validates fiber content, automates wastage 
analysis using data mining, and optimizes ammonia 
bicarbonate dosing through intelligent algorithms. 
Predictive analytics support consistent quality 
assessment. Advantages include end-to-end data 
integration, accurate automated analysis, optimized 
chemical usage, and improved product quality through 
predictive insights.},
        keywords = {},
        month = {May},
        }