Prediction of Sewage Wastewater Quality Based on PSO-LIBSVM

DOI

10.2991/isci-15.2015.39How to use a DOI?

Keywords

Activated Sludge Process; LIBSVM; Particle Swarm Optimization

Abstract

Aiming at the problems of nonlinearity, time-varying and big lagging in an activated sludge wastewater treatment process, the forecast modeling of COD can be established according to the historical data of chemical oxygen demand(COD) collected from sewage plant, and using the LIBSVM toolbox to determine the model structure and parameters. With the use of the output error data and the particle swarm algorithm, we can optimize the parameters of support vector machine(SVM) and correct model, until the output error is minimum. The results on simulation show that the more simple modeling process, the prediction effect will be much better . Compared with the BP neural network, the standard SVM model, it can reflect the characteristics of COD distribution in the future time.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bin Qin
AU  - Bang Liu
PY  - 2015/01
DA  - 2015/01
TI  - Prediction of Sewage Wastewater Quality Based on PSO-LIBSVM
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 280
EP  - 286
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.39
DO  - 10.2991/isci-15.2015.39
ID  - Qin2015/01
ER  -