Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Self-Organizing Maps
Applications of Neural Networks and Other Learning Technologies in Process Engineering
Applications of Neural Networks and Other Learning Technologies in Process Engineering
Electronics Process Technology: Production Modelling, Simulation and Optimisation
Electronics Process Technology: Production Modelling, Simulation and Optimisation
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Application of self-organizing maps in analysis of wave soldering process
Expert Systems with Applications: An International Journal
SOM-Based method for process state monitoring and optimization in fluidized bed energy plant
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Analysis of flue gas emission data from fluidized bed combustion using self-organizing maps
Applied Computational Intelligence and Soft Computing
Expert system for analysis of quality in production of electronics
Expert Systems with Applications: An International Journal
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In this study, a self-organizing map (SOM) -based process analysis and parameter approximation method was used to the emission analysis of a circulating fluidized bed process. The aim was to obtain the optimal process parameters in respect to the flue gas nitrogen oxide (NOx) content in different predefined states of process. The data processing procedure in the research went as follows. First, the process data were processed by using a self-organizing map and k-means clustering to generate subsets representing the separate process states in the boiler. These process states represent the higher level process conditions in the combustion, and can include for example start-ups, shutdowns, and idle times in addition to the normal process flow. Next, optimal areas were discovered from the map within each process state, and the reference vectors of the optimal neurons were used to approximate the values of desired process parameters. In addition, a subtraction analysis of reference vectors was performed to analyze the optimal situations. In conclusion, the method showed potential considering its wider use in the field of energy production.