Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)

Modelling of sludge discharge based on PSO-RVM

Authors
Long Luo
Corresponding Author
Long Luo
Available Online February 2018.
DOI
10.2991/ifeesm-17.2018.208How to use a DOI?
Keywords
sludge discharge;PSO; RVM
Abstract

The sludge discharge model in wastewater biochemical treatment is highly nonlinear,Accurate prediction of sludge discharge will provide basic data for the precise control of subsequent sludge dewatering process In the research based on this method, due to the relevance vector machine(RVM) has high sparseness and uses probability factor in predict, which is superior to the support vector machine(SVM) in the sludge discharge. However, the kernel function parameters of RVM are estimated by experience. Therefore, a kind of RVM method based on the particle swarm optimization(PSO) algorithm is proposed, which adopts the PSO algorithm to determine the kernel parameter of RVM, then builds RVM model and uses forecast the final sludge discharge.

Copyright
© 2018, 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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Volume Title
Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)
Series
Advances in Engineering Research
Publication Date
February 2018
ISBN
10.2991/ifeesm-17.2018.208
ISSN
2352-5401
DOI
10.2991/ifeesm-17.2018.208How to use a DOI?
Copyright
© 2018, 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/).

Cite this article

TY  - CONF
AU  - Long Luo
PY  - 2018/02
DA  - 2018/02
TI  - Modelling of sludge discharge based on PSO-RVM
BT  - Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)
PB  - Atlantis Press
SP  - 1138
EP  - 1141
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeesm-17.2018.208
DO  - 10.2991/ifeesm-17.2018.208
ID  - Luo2018/02
ER  -