a00ae6a39e90f4d270052a22cf7447ef processes_a2020v8n12p1672.pdf 25cb2a659d60b4536356c69afca7a51559bdf86b processes_a2020v8n12p1672.pdf 5a6a1741ae62168274297c371ba4469554fede10c7fae0361101959a29516fe3 processes_a2020v8n12p1672.pdf Title: Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control Subject: This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance. Keywords: membrane bioreactor; fouling; artificial neural network; internal model control Author: Norhaliza Abdul Wahab, Nurazizah Mahmod and Ramon Vilanova Creator: LaTeX with hyperref package Producer: pdfTeX-1.40.18 CreationDate: Thu Dec 17 15:05:32 2020 CET ModDate: Thu Dec 17 15:05:32 2020 CET Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 23 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4813036 bytes Optimized: no PDF version: 1.5 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- UTEDZJ+URWPalladioL-Ital Type 1 Custom yes yes no 103 0 ZROETK+URWPalladioL-Bold Type 1 Custom yes yes no 104 0 TMCCPJ+URWPalladioL-Roma Type 1 Custom yes yes no 105 0 VTONWS+Rpxr Type 1 Builtin yes yes no 106 0 BHUVWR+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 111 0 MCBCCI+SourceSansRoman-Regular Type 1C Custom 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