Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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
Environmental Modelling & Software
Predictive modeling for wastewater applications: Linear and nonlinear approaches
Environmental Modelling & Software
A modelling and optimization system for fluidized bed power plants
Expert Systems with Applications: An International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of evolutionary optimisers in data-based calibration of Activated Sludge Models
Expert Systems with Applications: An International Journal
Modelling of water quality: an application to a water treatment process
Applied Computational Intelligence and Soft Computing
Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview
Environmental Modelling & Software
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This paper presents an overview of an analysis method based on self-organizing maps (SOM) which was applied to an activated sludge treatment process in a pulp mill. The aim of the study was to determine whether the neural network modeling method could be a useful and time-saving way to analyze this kind of process data. The following analysis procedure was used. At first, the process data was modeled using the SOM algorithm. Next, the reference vectors of the map were classified by K-means algorithm into clusters, which represented different states of the process. At the final stage, the reference vectors of the map and the centre vectors of the clusters were used for subtraction analysis to indicate differences of the process states. The results show that the method presented here can be an efficient way to analyze the data of an activated sludge treatment process.