Intelligent Control System for Wood Drying: Scalable Architecture, Predictive Analytics, and Future Enhancements

Abstract

This article explores the research and development undertaken as part of a Master’s degree in Computer Engineering, with a primary focus on enhancing control mechanisms for natural wood drying. While this method is known for its cost-effectiveness in terms of labor and energy, it suffers from slower and unstable drying cycles. The project’s objective is to implement an intelligent control system that significantly improves monitoring and recording of humidity levels in each wooden stack. Additionally, the system incorporates the capability to predict humidity based on data sourced from a weather forecasting API. The proposed solution entails a three-layer system: data collection, relay, and analysis. In the data collection layer, low-computing devices, utilizing a Raspberry Pi, measure humidity levels in individual wood stacks. These devices then transmit the data via Low Power Bluetooth to the subsequent layer. The data relay layer incorporates an Android application designed to aggregate, normalize, and transmit collected data. Furthermore, it provides users with visualization tools for comprehensive data understanding. The data storage and analysis layer, developed with Django, serves as the back-end, offering management functionalities for stacks, sensors, overall data, and analysis capabilities. This layer can generate humidity forecasts based on real-time weather information. The implementation of this intelligent control system enables accurate insights into humidity levels, triggering alerts for any anomalies during the drying process. This reduces the necessity for constant on-site supervision, optimizes work efficiency, lowers costs, and eliminates repetitive tasks.