Adaptive Dust Suppression System for Open-Cast Mining Using Wind-Based Trajectory Prediction

Abstract

Open-cast mining poses significant environmental and occupational health risks due to the dispersion of dust by wind, which degrades air quality and increases pollution in mining areas and surrounding communities. Conventional dust suppression systems, which are either manually operated or run continuously, often suffer from inefficient water usage and limited control over dust dispersion. To overcome these limitations, this paper proposes an adaptive dust suppression system based on an ESP32 embedded controller integrated with wind direction and particulate matter (PM) sensors. The system continuously monitors real-time dust concentration and wind conditions to estimate the most likely dust dispersion path and automatically activates directional water spray mechanisms for targeted dust control. The system architecture includes an ESP32 microcontroller, wind direction sensor, dust sensor, relay-controlled water pump, spray nozzles, and an LCD module for local display of environmental parameters. The ESP32’s built-in Wifi enables real-time data transmission to a cloud-based IoT platform for remote monitoring and data logging. Experimental results demonstrate improved dust suppression efficiency and optimized water consumption compared to conventional fixed spraying methods. The proposed solution is scalable, cost-effective, and suitable for smart mining environments, with future potential for AI-based dust prediction and integration with mine-wide environmental management systems.