Integrated TPM, Machine Learning, and Layout Planning Model for Efficiency Improvement in Melamine Refining

Sinche J.J.M.; Uzuriaga N.R.S.; Falcon P.C.G. · Proceedings of the 2025 IEEE 32nd International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2025 (2025) · DOI: 10.1109/intercon67304.2025.11244709

Resumen

The melamine industry in Peru faces operational challenges such as reprocessing, unscheduled shutdowns, and inefficient material handling. This study presents a comprehensive model designed to enhance efficiency in the refining process at a melamine trading company in Lima. The main objectives are to increase the Overall Equipment Effectiveness (OEE) from 55.6% to 85%, reduce the reprocessing rate to 2%, and improve material transportation efficiency by 31.9%. The proposed model integrates Total Productive Maintenance (TPM), Machine Learning (ML) for predictive maintenance, and Systematic Layout Planning (SLP) for plant redesign. It is developed through a process diagnosis, root cause analysis, and validated using Arena simulation software, incorporating new cycle times and planned maintenance activities based on predictive insights. Simulation results demonstrate significant improvements in operational performance, validating the feasibility and scalability of the proposed model for the melamine sector.

Instituciones: Universidad Peruana de Ciencias Aplicadas