Environmental Case Study: Artificial Intelligence Model to Enhance Fog Cannon Performance

Mining companies are concerned about air pollution caused by dust emissions, as suspended dust particles can be a nuisance for workers and nearby populations. Fog dust suppression devices, also known as fog cannons, are one of the strategies in the modern mining industry to control dust emissions and reduce particulate emissions and dispersion. However, an internal analysis revealed that the cannon's default settings or manual operation have induced resource wastefulness and inefficiency. Based on artificial intelligence and real-time environmental sensor data, a Catboost Decision Tree Regressor prediction model was utilized to estimate the activation time and duration of the cannon. The results demonstrated that fog cannon performance rose by 80% in terms of true diffuse emission, resulting in a 36% reduction in dust emission and a 20% decrease in water use.