Fouling prediction using neural network model for membrane bioreactor system

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 needs 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

Fouling effect on controller tuning in membrane bioreactor filtration process

This paper presents an initial investigation on controller tuning with the effect on membrane fouling in submerged membrane bioreactor (SMBR). This work employed proportional integral derivative (PID) controller to control SMBR filtration process. The PID controller is tuned using three different methods which are Ziegler Nichols (ZN), Cohen Coon (CC) and integral time-weight absolute error (ITAE) tuning. The PID controller is used to control the SMBR filtration permeate flux. Transmembrane pressure (TMP) was observed during the filtration process that will determine fouling effect on controller tuning. The simulation work is done using artificial neural network (ANN) model that was developed in our previous work. Different set points were tested to see the robustness of the controller tuning. The overall result shows the ITAE tuning method performs better compare with other tuning methods in term of its overshoot, settling time and integral absolute error (IAE) with 0.66, 9.1 second and 82.68 respectively. This tuning method provides precise control performance in the same time it will prevent from decrement of flux in the filtration cycle

Current reversals in a rocking ratchet: the frequency domain

Motivated by recent work [D. Cubero et al., Phys. Rev. E 82, 041116 (2010)], we examine the mechanisms which determine current reversals in rocking ratchets as observed by varying the frequency of the drive. We found that a class of these current reversals in the frequency domain are precisely determined by dissipation-induced symmetry breaking. Our experimental and theoretical work thus extends and generalizes the previously identified relationship between dynamical and symmetry-breaking mechanisms in the generation of current reversals

Vibrational mechanics in an optical lattice: controlling transport via potential renormalization

We demonstrate theoretically and experimentally the phenomenon of vibrational resonance in a periodic potential, using cold atoms in an optical lattice as a model system. A high-frequency (HF) drive, with frequency much larger than any characteristic frequency of the system, is applied by phase-modulating one of the lattice beams. We show that the HF drive leads to the renormalization of the potential. We used transport measurements as a probe of the potential renormalization. The very same experiments also demonstrate that transport can be controlled by the HF drive via potential renormalization.Comment: Phys. Rev. Lett., in pres

Emergency shutdown valve reliability function test by automated Partial Stroke Testing System

Partial stroke testing (PST) is a technique that is regularly practiced in oil and gas industries to test the emergency shutdown (ESD) valve by closing a certain percentage of the valve position and stop any flow through the pipeline. Generally, it only functions when there is an emergency occurring in the production system. When the ESD valve remains in one position for a long period, there is a risk and potential of fail on demand which is, the ESD valve fail to operate during the emergency shutdown. This testing can reveal approximately 75 of unrevealed failures in valves. It can also provide predictive maintenance data that can contribute to the extension of the preventive maintenance for the ESD valve. The objectives of this paper are to design, simulate, build and test the performance of the automated PST system based on PLC. Four guidelines and methodology are used in this work. First, understanding the operation of the PST system. Then, the utilization of the capability of MATLAB-Simulink software as the simulation tool for the PST design system. Next, designing the PST automated system based on PLC design and lastly, testing the performance of the PST design system using lab scale PST system prototype that has been built. Results of the project shows that the PST system is successfully designed and simulated via MATLAB-Simulink and the PLC programming is working in the correct order as performed on the prototype

Recursive subspace identification algorithm using the propagator based method

Subspace model identification (SMI) method is the effective method in identifying dynamic state space linear multivariable systems and it can be obtained directly from the input and output data. Basically, subspace identifications are based on algorithms from numerical algebras which are the QR decomposition and Singular Value Decomposition (SVD). In industrial applications, it is essential to have online recursive subspace algorithms for model identification where the parameters can vary in time. However, because of the SVD computational complexity that involved in the algorithm, the classical SMI algorithms are not suitable for online application. Hence, it is essential to discover the alternative algorithms in order to apply the concept of subspace identification recursively. In this paper, the recursive subspace identification algorithm based on the propagator method which avoids the SVD computation is proposed. The output from Numerical Subspace State Space System Identification (N4SID) and Multivariable Output Error State Space (MOESP) methods are also included in this paper

Nano-confined synthesis of highly ordered mesoporous carbon and its performance as electrode material for electrochemical behavior of riboflavin (vitamin B2) and dopamine

Highly ordered mesoporous carbon (MC) has been synthesized from sucrose, a non-toxic and costeffective source of carbon. X-ray diffraction, N2 adsorption–desorption isotherm and transmission electron micrograph (TEM) were used to characterize the MC. The XRD patterns show the formation of highly ordered mesoporous structures of SBA15 and mesoporous carbon. The N2 adsorptiondesorption isotherms suggest that the MC exhibits a narrow pore-size distribution with high surface area of 1559 m2/g. The potential application of MC as a novel electrode material was investigated using cyclic voltammetry for riboflavin (vitamin B2) and dopamine. MC-modified glassy carbon electrode (MC/GC) shows increase in peak current compared to GC electrode in potassium ferricyanide which clearly suggest that MC/GC possesses larger electrode area (1.8 fold) compared with bare GC electrode. The electrocatalytic behavior of MC/GC was investigated towards the oxidation of riboflavin (vitamin B2) and dopamine using cyclic voltammetry which show larger oxidation current compared to unmodified electrode and thus MC/GC may have the potential to be used as a chemically modified electrode

Empirical modelling of activated sludge process via system identification

Activated sludge process is an important stage in Wastewater Treatment Plant (WWTP). In this study, model of the activated sludge process from Bunus Regional Sewage Treatment Plant Kuala Lumpur, Malaysia is developed. This paper focuses on modelling and model reduction of the WWTP system. The model with best fits of higher than 80 and the order of less than 10 is selected. For modelling purposes, data obtained is stimulated and modelled using System Identification technique which employ linear model ARX. For model reduction purposes, the high order model is reduced using model order reduction (MOR) of a combination of Singular Perturbation Approximation (SPA) and Frequency Domain Gramian based Model Reduction (FDIG) method. From the modelling results obtained, the ARX model with best fit of 85.11 is selected. Meanwhile, for the MOR FD-SPA technique, a 9th order model is selected with 2.5 x 10-2 reduction error between frequencies 0.05 rad/s and 1.4 rad/s