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Application of Adaptive Fuzzy Neural Network for Forecasting Coal Slagging in a Power Station
Author(s) -
Zheng Chuguang,
Liu Gehui,
Huang Yongli
Publication year - 2002
Publication title -
developments in chemical engineering and mineral processing
Language(s) - English
Resource type - Journals
eISSN - 1932-2143
pISSN - 0969-1855
DOI - 10.1002/apj.5500100106
Subject(s) - artificial neural network , coal , adaptability , convergence (economics) , gradient descent , power station , fuzzy logic , power (physics) , computer science , engineering , process engineering , artificial intelligence , waste management , ecology , physics , electrical engineering , quantum mechanics , economics , biology , economic growth
Ash related problems in coal‐fired power plants result in decreased efficiency, unscheduled outages, equipment failures and cleaning problems Assessing the potential impact of ash on power plant performance is extremely complicated and difficult due to coal variability, complexity of the ash behavior processes and changes in operating conditions involved This study introduces a new method of coal slagging forecast This method employs a hybrid‐learning rule composed of recursive least‐squares estimation and a gradient descent algorithm The forecast results are compared with measured data The comparison shows that the method based on adaptive fuzzy neural network for the prediction of coal slagging has the advantages of high precision, fast convergence and perfect adaptability