Deep Learning for Real-Time Detection of Pollutants in Bio-Energy Production and Utilization Systems

Bio-energy facilities are critical aspects of sustainability and low-carbon energy transitions, even though emitting pollutants from the combustion of biomass materials, anaerobic fermentation, and biofuel use is a serious environmental issue. Efficient real-time monitoring is the necessary requirement to maintain a clean energy production, satisfy regulations and care human's health. In most previous works, traditional monitoring methods are used which demonstrate obvious disadvantage with slow response time and poor flexibility of operation and unsatisfactory detection performance in a dynamic state; therefore, creating a gap that will only improve with further research for the intelligent in-situ pollution detecting application. The purpose of this project is to explore the use of deep learning to the real-time detection and monitoring of pollutants in bio-energy production/consumption systems. A comprehensive methodology is used to combine multi-sensor measurements of gas concentration, process parameters, and temporal response with state-of-the-art deep learning methods including convolutional and recurrent neural networks. The results suggest that the deep learning-based models provide remarkably high detection accuracy, efficiency and robustness compared to conventional approaches, leading to an earlier abnormal emission pattern detecting process. These findings indicated that intelligent monitoring system can help to achieve the optimized process control, emission reduction and predictive maintenance in bio-energy plant. Practical implications This work contributes directly to Sustainable Development Goals, namely SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) by supporting cleaner bio-energy operations, environmental friendliness and sustainable industrial development.