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Baxter, L.L., Miles, T.R., Miles Jr., TR., Jenkins, B.M., Milne, T., Dayton, D., Bryers, R.W. and Oden, L.L. (1998) The Behaviour of Inorganic Material in Biomass-Fired Power Boilers: Field and Laboratory Experiences. Fuel Processing Technology, 54, 47-78.
http://dx.doi.org/10.1016/S0378-3820(97)00060-X

has been cited by the following article:

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

  AUTHORS: Mohan Sathya Priya, Radhakrishnan Kanthavel, Muthusamy Saravanan

  KEYWORDS: Steam Boiler, Fouling and Slagging, Fuzzy Clustering, Artificial Neural Networks

  JOURNAL NAME: Circuits and Systems, Vol.7 No.12, October 31, 2016

  ABSTRACT: 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.