Enhanced Risk Management in Industrial Processes Using IoT and Random Forest for Hazardous Waste Generation Rates

Safety, environmental protection, and regulatory compliance depend on risk management in industrial operations, particularly those involving hazardous chemicals. This paper introduces a new risk management method that uses the Internet of Things (IoT) and Random Forest algorithm to predict hazardous waste generation rates. In industrial processes, many characteristics, such as temperature, pressure, flow rates, and chemical concentrations, are monitored in real time due to the IoT. These data streams are sent to the Random Forest algorithm, an advanced machine learning (ML) method that can handle complicated datasets and make accurate predictions. Training the Random Forest model on previous operating conditions and waste production rates helps it understand patterns and correlations. This trained algorithm can estimate hazardous waste production rates in real-time, enabling timely risk minimization. The suggested strategy has various benefits over industrial risk management strategies. It identifies possible dangers in time to avert mishaps and reduce environmental damage. IoT allows continuous monitoring and data collecting, which refines and optimizes the prediction model. It proposes a complete methodology for industrial risk management using IoT and ML to estimate hazardous waste production rates properly. Such a strategy might improve industrial safety, sustainability, and regulatory compliance. The advantage of quantifying improvements may be shown in specific metrics, such as a 15% decrease in hazardous waste and a 20% increase in regulatory compliance. Measuring indicators would emphasize the technique's practical benefits and efficacy.