Fault Diagnostics on Steam Boilers and Forecasting System Based on Hybrid Fuzzy Clustering and Artificial Neural Networks in Early Detection of Chamber Slagging/Fouling  

作　　者：Mohan Sathya Priya Radhakrishnan Kanthavel Muthusamy Saravanan Mohan Sathya Priya;Radhakrishnan Kanthavel;Muthusamy Saravanan(Indra Institute of Engineering and Technology, Chennai, India;Velammal Engineering College, Chennai, India;SSM Institute of Engineering and Technology, Dindigul, India)

机构地区：[1]Indra Institute of Engineering and Technology, Chennai, India [2]Velammal Engineering College, Chennai, India [3]SSM Institute of Engineering and Technology, Dindigul, India

出　　处：《Circuits and Systems》2016年第12期4046-4070,共25页电路与系统（英文）

摘　　要：The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.The slagging/fouling due to the accession of fireside deposits on the steam boilers decreases boiler efficiency and availability which leads to unexpected shut-downs. Since it is inevitably associated with the three major factors namely the fuel characteristics, boiler operating conditions and ash behavior, this serious slagging/fouling may be reduced by varying the above three factors. The research develops a generic slagging/fouling prediction tool based on hybrid fuzzy clustering and Artificial Neural Networks (FCANN). The FCANN model presents a good accuracy of 99.85% which makes this model fast in response and easy to be updated with lesser time when compared to single ANN. The comparison between predictions and observations is found to be satisfactory with less input parameters. This should be capable of giving relatively quick responses while being easily implemented for various furnace types.

关 键 词：Steam Boiler Fouling and Slagging Fuzzy Clustering Artificial Neural Networks 

分 类 号：TK2[动力工程及工程热物理—动力机械及工程]