Yusuf, Zakariah and Abd. Wahab, Norhaliza and Ab. Halim, Mohd. Hakim and Nor Anuar, Aznah and Ujang, Zaini and Bob, Mustafa M. (2015) Modeling of SBR aerobic granular sludge using neural network with GSA and IW-PSO. In: The 10th Asian Control Conference (ASCC2015), 31 May-3 Jun, 2015, Kota Kinabalu, Malaysia.
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Official URL: http://ieeexplore.ieee.org/document/7244690/
Abstract
This paper presents a modeling technique of sequential batch reactor (SBR) for aerobic granular sludge (AGS) using artificial neural network (ANN). A SBR fed with synthetic wastewater was operated at high temperature of 50 C to study the formation of AGS for simultaneous organics and nutrients removal in 60 days. The feed forward neural network (FFNN) was used to model the nutrients removal process. In this work, inertia weight particle swarm optimization (PSO) and gravitational search algorithm (GSA) were employed to optimize the neural network weights and biases. It was observed that the inertia weight GSA-NN give better prediction of nutrient removal compared with Inertia weight PSO. The performance of the models was measured using the R2, mean square error (MSE) and root mean square error (RMSE).
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | particle swarm optimization (PSO), gravitational search algorithm (GSA) |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Electrical Engineering |
| ID Code: | 60778 |
| Deposited By: | Fazli Masari |
| Deposited On: | 28 Feb 2017 01:44 |
| Last Modified: | 28 Feb 2017 01:44 |
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