Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater

Schmitt, F. G.; Banu, Rajesh; Yeom, Ikjun; Do, Khac‐Uan

doi:10.1016/j.bej.2018.02.001

Cited by 109 publications

(25 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Arbitrary variables selection in this type of models allows to omit kinetic variables, which may be difficult to determine, especially for new media such as aerobic granulated activated sludge [14,15]. In case of wastewater treatment processes, both quantities describing chemical properties of wastewater inflowing to reactor and indicators approximating activity of microorganisms present in activated sludge, expressed as technological parameters of activated sludge [16], may be important. Ability of arbitrary selection of variables in creation process of ANN model also involves cognitive aspect of given phenomenon [17].…”

Section: Resultsmentioning

confidence: 99%

Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Ofman

Struk-Sokołowska

2019

Water

View full text Add to dashboard Cite

Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.

show abstract

Section: Resultsmentioning

confidence: 99%

Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Ofman

Struk-Sokołowska

2019

Water

View full text Add to dashboard Cite

show abstract

“…The activation or transfer function adds non-linearity to the neural network [ 39 ]. The most commonly used activation functions are logistic sigmoid (log-sigmoid), hyperbolic tangent sigmoid (tan-sigmoid), and pure linear functions (purelin).…”

Section: Methodsmentioning

confidence: 99%

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

2021

View full text Add to dashboard Cite

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

show abstract

“…ANN is computational models able to simulate the processing and learning functions of a human brain [15,17]. In accordance with the human brain, an ANN model is composed of simple elements operating in parallel [18]. Neurons in a certain layer of the ANN are connected to those from the previous layer by a number of weighted connections.…”

Section: Ann Modelmentioning

confidence: 99%

“…Every input is multiplied by its corresponding weight and the node uses summation of these weighted inputs (W ij × x 1 ) to estimate an output signal using a transfer function. These weighted inputs are then summed and added to a threshold value (θ j ) to produce the node input (I j ) as shown in the equation below [17,18]:…”

Section: Ann Modelmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of long-term performance of full-scale reverse osmosis desalination plant using artificial neural network and tree model

Choi

Lee

Shin

et al. 2019

Environmental Engineering Research

View full text Add to dashboard Cite

The reverse osmosis (RO) technology is currently the leading desalination method. However, until recently, application of RO technology on a large scale has been primarily limited by membrane fouling. The mechanism of fouling is complex, which is not well understood in full-scale plants. Although many studies about modeling and prediction of fouling have been done, in most cases, the experimental data set of lab or pilot scale systems, which may not show fouling characteristics well in full-scale systems were used. In this study, both artificial neural network (ANN) model and tree model (TM) was evaluated to analyze long-term performance of full scale reverse osmosis desalination plant. The results of application of the ANN and TM indicated high correlation coefficients between the measured and simulated output variables. However, it is not easy to use ANN for the full scale plant operation because the final model is not expressed as a form of mathematical functions. TM has advantages over ANN because the model can be obtained as forms of simple function and it showed reasonably high <i>R</i><sup>2</sup>. Therefore, TM is shown to be more adequate than ANN for developing models in which the full-scale RO plant data is considered as an input.

show abstract

Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater

Cited by 109 publications

References 35 publications

Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Analysis of long-term performance of full-scale reverse osmosis desalination plant using artificial neural network and tree model

Contact Info

Product

Resources

About