AI Enabled Facility Management & Control System (FMCS) for Factories

Sammanfattning

This project proposes the development of an AI-enhanced Facility Management Control System (FMCS) for industrial energy optimization, specifically targeting resource management in reflow ovens. Traditional FMCS solutions provide real-time monitoring of key utilities such as cooling water, electricity, and compressed air. The advancement in machine learning technologies enables the transition from passive monitoring to predictive control, facilitating proactive resource dispatching and cost reduction. The key challenge lies in building an accurate prediction model using a limited dataset. To address this, a deep learning-based FMCS was designed, utilizing a Long Short-Term Memory (LSTM) architecture tailored for time-series data. The model comprises 4-6 LSTM layers followed by a two-layer neural network with ReLU activation functions. The system specifically focuses on monitoring nitrogen consumption while maintaining oxygen concentration within acceptable limits, with the aim of identifying inefficiencies and reducing operational waste. The empirical results demonstrated that the model could help save approximately 60% of the nitrogen cost, demonstrating the feasibility of the solution in production.