End-point detection of the aerobic phase in a biological reactor using SOM and clustering algorithms

Authors:
Iván Machón González;Hilario López García
Affiliations:
Departamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas, Universidad de Oviedo, Escuela Politécnica Superior de Ingeniería, Edificio Departamental 2, ...;Departamento de Ingeniería Eléctrica, Electrónica de Computadores y Sistemas, Universidad de Oviedo, Escuela Politécnica Superior de Ingeniería, Edificio Departamental 2, ...
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

Citing 3
Cited 5

Process Monitoring and Modeling Using the Self-Organizing Map

Integrated Computer-Aided Engineering
A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering of the self-organizing map

IEEE Transactions on Neural Networks

FLSOM with Different Rates for Classification in Imbalanced Datasets

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Identifying clusters of user behavior in intranet search engine log files

Journal of the American Society for Information Science and Technology
Fuzzy labeled self-organizing map with kernel-based topographic map formation

ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ASOD: Arbitrary shape object detection

Engineering Applications of Artificial Intelligence
Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview

Environmental Modelling & Software

Quantified Score

Hi-index	0.00

Visualization

Abstract

The estimation of the aerobic phase end-point is usually used to improve the operating capacity in a sequencing batch reactor. In this paper, a software tool and a configuration of the dissolved oxygen control closed loop are proposed to achieve the aerobic end-point detection of a sequencing batch reactor in a coke wastewater treatment plant. The proposed software tool consists of self-organizing map (SOM) and clustering algorithms. Moreover a validation method for SOM training is outlined and a predefined criterion to determine the SOM size is tested.