Dynamic Modelling of Aerobic Granular Sludge Artificial Neural Networks

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

Modeling of activated sludge process using various nonlinear techniques: a comparison study

This paper presents a comparison study between radial basis function neural network (RBFNN), feed forward multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy (ANFIS) technique to model the activated sludge process (ASP). All of these techniques are based on the nonlinear autoregressive with eXogenous input (NARX) structure. The ASP inputs and outputs data are generated from activated sludge model 1 (ASM1). This work will cover the dissolved oxygen (DO), substrate and biomass modeling. The performances of the model are evaluated based on R2, mean square error (MSE) and root mean square error RMSE. The simulation result shows that ANFIS with NARX structure given a better performance compared with the other modeling techniques

Dynamic model development for submerged membrane filtration process using recurrent artificial neural network with control application

Modeling of membrane filtration process is challenging task because it is involves many interactions from biological and physical operation behavior. Membrane fouling in filtration process is too complex to understand and to derive a robust model is not possible. The aim of this paper is to study the potential of neural network based dynamic model for submerged membrane filtration process. The purpose of the model is to represent the dynamic behavior of the filtration process therefore suitable control strategy and tuning can be developed to control the filtration process more effectively. In this work, a recurrent neural network (RNN) structure was employed to perform the dynamic model of the filtration process. The random step was applied to the suction pump to obtained the permeate flux and Transmembrane Pressure (TMP) dynamic. The model was evaluated in term of %R2, root mean square error (RMSE,) and mean absolute deviation (MAD). Proportional integral derivative (PID) controller was implemented to the model for different control strategies and several tuning gains were tested for the effective filtration control. The result of proposed modeling technique showed that the RNN structure is able to model the dynamic behavior of the filtration process below critical flux condition. The developed model also can be a reliable assistance for the control strategy development in the filtration process

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux

Modelling and Evaluation of Sequential Batch Reactor Using Artificial Neural Network

The main objective of wastewater treatment plant is to release safe effluent not only to human health but also to the natural environment. An aerobic granular sludge technology is used for nutrient removal of wastewater treatment process using sequential batch reactor system. The nature of the process is highly complex and nonlinear makes the prediction of biological treatment is difficult to achieve. To study the nonlinear dynamic of aerobic granular sludge, high temperature real data at 40˚C were used to model sequential batch reactor using artificial neural network. In this work, the radial basis function neural network for modelling of nutrient removal process was studied. The network was optimized with self-organizing radial basis function neural network which adjusted the network structure size during learning phase. Performance of both network were evaluated and compared and the simulation results showed that the best prediction of the model was given by self-organizing radial basis function neural network

Multivariable PID control of an Activated Sludge Wastewater Treatment Process

In general, wastewater treatment plant (WWTP) consists of several stages before it is released to a receiving water body. There are, preliminary and primary treatment (mechanical treatment), a secondary treatment (biological treatment) and a tertiary treatment (chemical treatment). In this chapter, since the work involve of identification and control design of activated sludge process to improve the performance of the system, and most of the control priorities are centred on the biological treatment process, only the secondary treatment will be highlighted

Neural Network Model Development with Soft Computing Techniques for Membrane Filtration Process

Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IW-PSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO

Modeling of waste water treatment plant via system ID & model reduction technique

This paper investigates the application of Model Order Reduction (MOR) technique to Waste Water Treatment Plant (WWTP) system. The mathematical model of WWTP is obtained by using System Identification. In this paper, Prediction Error Estimate of Linear or Nonlinear Model (PEM) is proposed as the System Identification method which is used to find the parameter of linear or nonlinear system in state-space model from an experimental input­ output data WWTP. The result shows that the estimated model of WWTP is a high order system with good best fit with 91.56% and80.19% compared to the original experimental model. To simplify the obtained model,the MOR technique is proposed to reduce the high order system to lower order system while still retaining the characteristics of the original system. In this paper, the balanced truncation and Frequency Weighted Model Reduction (FWMR) are proposed to obtain a lower order WWTP model. The result shows that by MOR techniques, the higher WWTP system can be simplified to lower order system with a low error of the reduced system. The result of reduced model will be represented in sigma graph and numerical value

Parameter Optimisation of Aerobic Granular Sludge at High Temperature Using Response Surface Methodology

This paper proposes an improved optimisation of sequencing batch reactors (SBR) for aerobic granular sludge (AGS) at high temperature-low humidity for domestic wastewater treatment using response surface methodology (RSM). The main advantages of RSM are less number of experiment required and suitable for complex process. The sludge from a conventional activated sludge wastewater treatment plant and three sequencing batch reactors (SBRs) were fed with synthetic wastewater. The experiment were carried out at different high temperatures (30, 40 and 50°C) and the formation of AGS for simultaneous organics and nutrients removal were examined in 60 days. RSM is used to model and to optimize the biological parameters for chemical oxygen demand (COD) and total phosphorus removal in SBR system. The simulation results showed that at temperature of 45.33°C give the optimum condition for the total removal of COD and phosphorus, which correspond to performance index R2 of 0.955 and 0.91, respectively

Fault detection and monitoring using multiscale principal component analysis at a sewage treatment plant

Safety, environmental regulations, the cost of maintenance and the operation of sewage treatment plants are some of the many reasons researchers have carried out countless research studies into fault detection and monitoring over the years. Conventional principal component analysis (PCA) in particular has been used in the field of fault detection, where the technique is able to separate useful information from multivariate data. However, conventional PCA can only be used on data that has a constant mean, which is rare in sewage treatment plants. Consequently, the success of combining wavelet and conventional PCA has attracted many researchers to apply it to fault detection where the wavelet is capable of separating data into several time scales. The separated data will be approximated to a constant mean. In addition, the conventional PCA only captures the correlation across the data, unlike multiscale PCA (MSPCA) which captures the correlation within the data and across the data. Therefore, in this work, MSPCA is introduced to improve the performance of PCA in fault detection. The objective of this paper is to reduce false alarms that exist in PCA fault detection and monitoring. Data from the Bunus sewage treatment plant (Bunus STP) is used and analysed using conventional PCA with Hotelling’s T2 and the squared prediction error (SPE). MSPCA with Hotelling’s T2 and SPE is used to improve the efficiency of fault detection and monitoring performance in conventional PCA. Therefore, MSPCA is successful in improving conventional PCA in fault detection and monitoring by reducing false alarms