Recurrent neural network based detection of faults caused byparticle attrition in chemical looping systems

Abstract

Chemical looping is a novel and promising technology that converts fossil fuels to electricity and high value chemicals with in-situ carbon capture without significant cost penalties. Smooth and controlled solid circulation is the key to the successful operation of the chemical looping system. Bridging or arching, which may occur in the moving bed standpipe of the system, caused by a combination of fines accumulation and gas flows, is a major impediment to the smooth solid circulation and can lead to the failure of the entire system operation. Thus, early detection of the tendency of bridging or arching is important. This paper describes a model that applies the long short-term memory based recurrent neural network scheme to detect the tendency of arching in the standpipe of a chemical looping system. The arching tendency can be ascertained by early detection of the bubble formation in the standpipe. The bubble movement or local fluidization is recognized to precede the arching formation due to local accumulation of fine particles generated from coarse particle attrition. The early detection of the bubbles thus renders it possible to prevent arching through the prompt action of fines removal from the system. In this study, the recurrent neural network model which detects the fines induced fault manifested as bubbles in the standpipe is developed. It is over the data generated from an experimental sub-pilot scale, cold-flow chemical looping unit. To improve the robustness of the diagnosis, a number of networks with different structures are considered, and an ensemble decision strategy is used to conduct the diagnosis. The recall value obtained of the diagnosed result, which represents the extent of the fraction of real bubbles that are detected, can reach higher than 86.7%. This result reflects a good accuracy of the recurrent neural network model in the fault detection for the chemical looping system.