Razali, Mashitah Che and Wahab, Norhaliza Abdul and Sunar, Noorhazirah and Shamsudin, Nur Hazahsha and Gaya, Muhammad Sani and Zainal, Azavitra (2024) NARXNN modeling of ultrafiltration process for drinking water treatment. In: 22nd Asia Simulation Conference, AsiaSim 2023, 25 October 2023 through 26 October 2023, Langkawi.
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NARXNN Modeling of Ultrafiltration Process for Drinking Water Treatment.pdf Restricted to Registered users only Download (952kB) |
Abstract
Ultrafiltration (UF) process has gained attention over times, particularly in treating drinking water treatment. A major challenge in achieving high quality of drinking water in UF process is membrane fouling. Membrane fouling has great effects on the performance of filtration process. To overcome fouling accurately, a prediction model is necessary. With prediction model, membrane fouling can be handled in a right way, so that, efficiency of filtration process can be maximize. This paper presents a study on modeling based on non-linear autoregressive with exogenous input neural network (NARXNN) of UF pilot plant specifically from treating drinking surface water. TheNARXNN was used to model the permeate flux and transmembrane pressure (TMP). In this work, LM training algorithm was employed. The performance of the model was measured based on mean square error (MSE), root mean square error (RMSE) and correlation of coefficient (R). The simulation results demonstrate that proposed NARXNN modelling able to give high prediction rate with R value of 0.91743. It shows, the prediction values agree well with the actual values. With this model, membrane fouling can be successfully simulated and monitor accordingly.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Drinking water treatment, NARXNN, Permeate Flux, Transmembrane pressure, Ultrafiltration |
| Divisions: | Faculty Of Electrical Technology And Engineering |
| Depositing User: | Norhairol Khalid |
| Date Deposited: | 05 Jun 2025 08:22 |
| Last Modified: | 05 Jun 2025 08:22 |
| URI: | http://eprints.utem.edu.my/id/eprint/28751 |
| Statistic Details: | View Download Statistic |
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