Process Monitoring and Modeling Using the Self-Organizing Map
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
Environmental Modelling & Software
Review of the Self-Organizing Map (SOM) approach in water resources: Commentary
Environmental Modelling & Software
Environmental Modelling & Software
Neural computing modeling of the reference crop evapotranspiration
Environmental Modelling & Software
Data-driven modeling approaches to support wastewater treatment plant operation
Environmental Modelling & Software
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
Hi-index | 0.00 |
Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant.