Membrane bioreactor (MBR) technology is a new method for water and wastewater treatment due to its ability to produce better and high-quality effluent that meets water quality regulations. MBR also is an advanced way to displace the conventional activated sludge (CAS) process. Even this membrane gives better performances compared to CAS, it does have few drawbacks such as high maintenance cost and fouling problem. In order to overcome this problem, an optimal MBR plant operation need to be developed. This can be achieved through an accurate model that can predict the fouling behaviour which could optimise the membrane operation. This paper presents the application of artificial neural network technique to predict the filtration of membrane bioreactor system. The Radial Basis Function Neural Network (RBFNN) is applied to model the developed submerged MBR filtration system. RBFNN model is expected to give good prediction model of filtration system for estimating the fouling that formed during filtration process.
Membrane bioreactor (MBR) is one of the best solutions for water and wastewater treatment systems in producing high quality effluent that meets its standard regulations. However, fouling is one of the main issues in membrane filtration for membrane bioreactor system. The prediction of fouling is crucial in the membrane bioreactor control system design. This paper presents an intelligence modeling system so called artificial neural network (ANN). The feedforward neural network (FFNN), radial basis function neural network (RBFNN) and nonlinear autoregressive exogenous neural network (NARXNN) are applied to model the submerged MBR filtration system. The simulation results show good predictions for all methods which the highest performance of the model given by RBFNN. Based on the developed models, the neural network internal model control (NNIMC) is implemented to control fouling development in membrane filtration process. Three different control structures of the NNIMC are proposed. The FFNN IMC, RBFNN IMC and NARXNN IMC controllers are compared to the conventional IMC. The RBFNN IMC has a superior performance both in tracking and disturbance rejections.
Aerobic Granular Sludge (AGS) technology is a promising development in the field of aerobic wastewater treatment system. Aerobic granulation usually happened in sequencing batch reactors (SBRs) system. Most available models for the system are structurally complex with the nonlinearity and uncertainty of the system makes it hard to predict. A reliable model of AGS is essential in order to provide a tool for predicting its performance. This paper proposes a dynamic neural network approach to predict the dynamic behavior of aerobic granular sludge SBRs. The developed model will be applied to predict the performance of AGS in terms of the removal of Chemical Oxygen Demand (COD). The simulation uses the experimental data obtained from the sequencing batch reactor under three different conditions of temperature (30˚C, 40˚C and 50˚C). The overall results indicated that the dynamic of aerobic granular sludge SBR can be successfully estimated using dynamic neural network model, particularly at high temperature.
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.
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