ARTIFICIAL INTELLIGENCE FOR ENVIRONMENTAL RISK MANAGEMENT IN AUTOMATED BOILER SYSTEMS
DOI:
https://doi.org/10.18623/rvd.v23.5823Keywords:
Neural Networks, Risk Management, Automated Boiler Systems, Environmental ProtectionAbstract
This paper examines the application of artificial intelligence in automated boiler systems with the aim of improving environmental risk management and reducing emissions generated during combustion processes. The study focuses on the use of neural network models as intelligent monitoring and predictive control tools in industrial heating systems. The main objective of the research is to evaluate how artificial intelligence can support safer and more efficient operation of automated boilers while contributing to lower fuel consumption and reduced environmental impact. The research methodology is based on experimental data collected from an automated boiler system of the OZON 55 type equipped with sensor-based monitoring devices. Operational parameters such as temperature, air supply, fuel characteristics, and gas emissions were recorded and analyzed using recurrent neural network models designed to predict deviations in combustion behavior. The obtained results indicate that neural network–based predictive monitoring can detect anomalies in operational parameters at an early stage and enable timely adjustments of combustion conditions. Such improvements contribute to increased operational safety, improved fuel efficiency, and lower emissions of harmful gases. The findings suggest that the integration of artificial intelligence into automated boiler systems represents an effective technological approach for enhancing environmental protection, improving risk management, and supporting more sustainable energy use in industrial heating systems.
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