Fouling Prediction using Neural Network Model for Membrane Bioreactor System

2017 ◽  
Vol 6 (1) ◽  
pp. 200 ◽  
Author(s):  
Nurazizah Mahmod ◽  
Norhaliza Abdul Wahab
Keyword(s):  
Neural Network ◽  
Membrane Bioreactor ◽  
Maintenance Cost ◽  
Good Prediction ◽  
Water And Wastewater Treatment ◽  
Conventional Activated Sludge ◽  
Filtration System ◽  
Water Quality Regulations ◽  
Bioreactor System ◽  
Water And Wastewater

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.

Permeate Flux Measurement and Prediction of Submerged Membrane Bioreactor Filtration Process Using Intelligent Techniques

2015 ◽  
Vol 73 (3) ◽  
Author(s):  
Zakariah Yusuf ◽  
Norhaliza Abdul Wahab ◽  
Shafishuhaza Sahlan ◽  
Abdul Halim Abdul Raof
Keyword(s):  
Neural Network ◽  
Membrane Filtration ◽  
Membrane Bioreactor ◽  
Flux Measurement ◽  
Maintenance Cost ◽  
Transmembrane Pressure ◽  
Permeate Flux ◽  
Filtration Process ◽  
Water And Wastewater Treatment ◽  
Suction Pump

Recently, membrane technology has become more attractive particularly in solid-liquid separation process. Membrane bioreactor (MBR) has found to be a reliable technology to replace the conventional activated sludge (CAS) process for water and wastewater treatment by adopting membrane filtration technology and bioreactor. However, numerous drawbacks arise when using membrane which includes high maintenance cost and fouling problem. An optimal MBR plant operation is needed to be determined in order to reduce fouling and at the same time reduce the cost of running the MBR. It is crucial to have a reliable MBR filtration prediction that can measure and predict the filtration dynamic performance especially the effect of fouling to the filtration and cleaning operations. With this prediction tool, suitable action can be taken to improve the operation in order to find the optimum setting of the filtration process. This paper presents the permeate flux measurement and prediction development for submerged membrane filtration process. Three input filtration parameters were used to predict the permeate flux in the filtration process. This work  employed feed forward artificial neural network (FFNN) and radial basis function neural network (RBFNN) for the prediction purpose. The permeate flux prediction method was developed using operation settings such as aeration airflow, suction pump voltage and transmembrane pressure (TMP) under schedule relaxation condition.  The result shows that FFNN method gives better performance compared with RBFNN method in terms of accuracy and reliability. 

Integration of Multi AI Paradigms for Intelligent Operation Support Systems – Fuzzy Rule Extraction from a Neural Network

1993 ◽  
Vol 28 (11-12) ◽  
pp. 333-340 ◽  
Author(s):  
I. Enbutsu ◽  
K. Baba ◽  
N. Hara ◽  
K. Waseda ◽  
S. Nogita
Keyword(s):  
Neural Network ◽  
Historical Data ◽  
Fuzzy Rule ◽  
Rule Extraction ◽  
Water And Wastewater Treatment ◽  
Plant Data ◽  
Water And Wastewater ◽  
Artificial Neural Network Ann ◽  
Operational Guidance ◽  
Wastewater Treatment Systems

Some artificial intelligence (AI) paradigms have been applied to water and wastewater treatment systems. An artificial neural network (ANN), which can learn historical data of a plant, provides operational guidance for plant operators, and a fuzzy system (FS) provides a framework to put operators' heuristics into practical use as fuzzy rules in a fuzzy rulebase. In application, however, the practical problems remain that the ANN is a blackbox model which is unfamiliar to plant operators, and the FS usually requires much time-consuming work by system engineers and operators for knowledge acquisition and rulebase maintenance. The authors think that integration of the paradigms can give appropriate solutions to these problems. As one method which realizes such integration, an automatic fuzzy rule extraction method using an ANN is proposed. Simulation results of the proposed method using full-scale plant data demonstrated that an FS whose rulebase was modified automatically with extracted rules had better performance than a conventional FS whose rulebase included only operators' heuristics. This effect is thought to be realized by enhancement of knowledge source with the proposed method.

scholarly journals Modelling and control of fouling in submerged membrane bioreactor using neural network internal model control

2020 ◽  
Vol 9 (1) ◽  
pp. 100
Author(s):  
Nurazizah Mahmod ◽  
Norhaliza Abdul Wahab ◽  
Muhammad Sani Gaya
Keyword(s):  
Neural Network ◽  
Internal Model ◽  
Membrane Filtration ◽  
Membrane Bioreactor ◽  
Internal Model Control ◽  
Superior Performance ◽  
Water And Wastewater Treatment ◽  
Control Structures ◽  
Model Control ◽  
Artificial Neural Network Ann

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.

Production of Sludge in a Submerged Membrane Bioreactor and Dewatering Aspects

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Watsa Khongnakorn ◽  
Christelle Wisniewski
Keyword(s):  
Activated Sludge ◽  
Water Reuse ◽  
Membrane Bioreactor ◽  
Operating Conditions ◽  
Good Prediction ◽  
Activated Sludge Process ◽  
Conventional Activated Sludge ◽  
Sludge Dewaterability ◽  
Conventional Activated Sludge Process ◽  
Sludge Production

In wastewater treatment, the membrane bioreactor (MBR) holds the potential to become one of the new generation processes, ensuring effluent quality and disinfection of sufficiently high levels to allow water reuse and recycle. Furthermore, the possibility to operate with high biomass concentrations (2 to 5 times higher than in conventional activated sludge process, CAS) allows to impose high solid retention times(SRT) that can be beneficial to a sludge production reduction and so to a reduction of disposal costs. These non-conventional operating conditions (high SRT) can also induce different sludge characteristics and dewatering aptitude, which are essential parameters for the optimization of the sludge post-treatment, like mechanical dewatering. The objective of this work was to study the performances of a complete sludge retention membrane bioreactor, in terms of organic removal efficiency, sludge production and sludge dewaterability. The adaptability of Activated Sludge Model 3 (ASM3) to provide good prediction results of high SRT-MBR was studied. Typical parameters adopted to describe sludge dewaterability were quantified and compared with the conventional activated sludge process (CAS).

Evaluation of a long-term operation of a submerged nanofiltration membrane bioreactor (NF MBR) for advanced wastewater treatment

2006 ◽  
Vol 53 (6) ◽  
pp. 131-136 ◽  
Author(s):  
J.-H. Choi ◽  
K. Fukushi ◽  
H.Y. Ng ◽  
K. Yamamoto
Keyword(s):  
Organic Matter ◽  
Wastewater Treatment ◽  
Membrane Bioreactor ◽  
Nanofiltration Membrane ◽  
Treatment Efficiency ◽  
Water And Wastewater Treatment ◽  
Term Operation ◽  
Water And Wastewater ◽  
High Rejection

Nanofiltration (NF) is considered as one of the most promising separation technologies to obtain a very good-quality permeate in water and wastewater treatment. A submerged NF membrane bioreactor (NF MBR) using polyamide membranes was tested for a long-term operation and the performance of the NF MBR was compared with that of a microfiltration MBR (MF MBR). Total organic carbon (TOC) concentration in the permeate of the NF MBR ranged from 0.5 to 2.0 mg/L, whereas that of the MF MBR showed an average of 5 mg/L. This could be explained by the tightness of the NF membrane. Although the concentration of organic matter in the supernatant of the NF MBR was higher than that in the permeate due to high rejection by the NF membrane, the NF MBR showed excellent treatment efficiency and satisfactory operational stability for a long-term operation.

scholarly journals Physico–chemical Characteristics and Dewatering Aptitude of sMBR Sludge

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
W. Khongnakorn ◽  
S. P. Choksuchart ◽  
C. Wisniewski
Keyword(s):  
Membrane Bioreactor ◽  
Bound Water ◽  
High Viscosity ◽  
Post Treatment ◽  
Operating Conditions ◽  
Conventional Activated Sludge ◽  
Solids Concentration ◽  
Physico Chemical ◽  
Bioreactor System ◽  
Constant Characteristics

Biomass concentrations 2 to 5 times higher than in a conventional activated sludge (CAS) process can be achieved in a membrane bioreactor system (MBR). These non–conventional operating conditions, i.e. high sludge concentration, can induce different sludge characteristics and dewatering aptitude, essential parameters for the optimization of the sludge post–treatment, like mechanical dewatering. The objective of this work is to study the dewatering behavior of MBR sludge, and particularly the influence of high total suspended solids concentration, on viscosity and on the key dewaterability indicators. Operating conditions are chosen to obtain MBR sludge with constant characteristics, except TSS concentration. The results confirm that the sludge viscosity is dependent on the TSS concentration. The high viscosity obtained for high TSS concentration can be unfavorable to an efficient mixing in the MBR unit, as well as to acceptable membrane permeability. However, good settleability is obtained with high TSS concentration although the sludge presents high compressibility property and a large part of bound water. This large part of bound water seems to not disturb the sludge filterability, which stays relatively good in comparison with CAS. Consequently, high–TSS concentration in MBR system can be coherent with an efficient sludge post–treatment.

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