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Data-driven ESP modelling and optimisation
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AI
DOI：10.1016/j.jaerosci.2013.12.013.png)
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The process that takes place in an electrostatic precipitator (ESP) is complex and is influenced by several phenomena. The numerical models present in the literature are continuously growing, but the complexity inherent to the process and the limits of current computers make it impossible to carry out a complete modelling process, being necessary to carry out important simplifications on the models. Taking into account these limitations of numerical models, the use of data mining techniques is proposed for the analysis and modelling of the ESP's performance, the advantages of which are the possibility to include a large amount of complex phenomena and factors and their applicability to any ESP, regardless of its specific configuration or shape. This approach is especially interesting for the analysis of ESPs that have already been implemented, from which we can recover data and take into account the environment's characteristics. Furthermore, the resulting models can be used in optimisation processes in which the best ESP configuration is sought at all times, in order to maximise its yield. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Electrostatic precipitators
Data mining
Optimisation
ESP modelling
期刊
IF:
2.9
论文数:
3.4K
被引数:
8.0K
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