ACM Computing Surveys (CSUR)
Self-Organizing neural networks: recent advances and applications
Self-Organizing neural networks: recent advances and applications
Applications of Neural Networks and Other Learning Technologies in Process Engineering
Applications of Neural Networks and Other Learning Technologies in Process Engineering
Computers in Industry - Special issue: Soft computing in industrial applications
Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
Environmental Modelling & Software
Application of self-organizing maps in analysis of wave soldering process
Expert Systems with Applications: An International Journal
Review of the Self-Organizing Map (SOM) approach in water resources: Commentary
Environmental Modelling & Software
A modelling and optimization system for fluidized bed power plants
Expert Systems with Applications: An International Journal
Emission analysis of a fluidized bed boiler by using self-organizing maps
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Quality-oriented optimization of wave soldering process by using self-organizing maps
Applied Soft Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Condition monitoring of 3G cellular networks through competitive neural models
IEEE Transactions on Neural Networks
Data-driven modeling of surface temperature anomaly and solar activity trends
Environmental Modelling & Software
A software platform for process monitoring: Applications to water treatment
Expert Systems with Applications: An International Journal
Conceptual evaluation of continental land-surface model behaviour
Environmental Modelling & Software
Hybrid modeling of spatial continuity for application to numerical inverse problems
Environmental Modelling & Software
Environmental Modelling & Software
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Efforts to reduce harmful emissions and the increasing demands for combustion efficiency have generated a number of challenges for power plants. Changes in the operation of a combustion process, for example, can induce fluctuations that have unexpected consequences such as an increased level of emissions. Despite the importance of these changes, their impact and relevance are often ignored in analyses of industrial process data due to the complexity of these phenomena. It seems that the behavioral evolution of a process could be understood more easily by monitoring the transition of the process from one characteristic state to another. We demonstrate here that the self-organizing map (SOM) provides an efficient method for revealing the most characteristic features of input data, making it a powerful tool for discovering general phenomena and visualizing the behavior and evolution of a combustion process. In this approach the process data are analyzed using a SOM and K-means clustering to create subsets representing the separate process states in the boiler. A trajectory analysis is then performed to indicate fluctuations in the process and their implications. The results show that process fluctuations can significantly affect the levels of nitrogen oxides released. The method enables efficient diagnosis, and provides a clear illustration of the evolution of the process and an applicable means of defining the best path for achieving a more efficient process that produces less emissions.