Soft computing techniques in modelling of membrane filtration system: a review

Yusuf, Zakariah; Wahab, Norhaliza Abdul; Sudin, Shahdan

doi:10.5004/dwt.2019.24294

Cited by 15 publications

(5 citation statements)

References 92 publications

(112 reference statements)

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“…Thus, novel approaches are required for the training of neural networks that incorporate solutions to selectively train neurons by, i.e., identifying their importance through neural interpretation diagrams [ 86 ] or relevance scores [ 87 ] have been proposed. While they focus exclusively on the visualization of the neural importance, they are either too simplistic or highly complicated, requiring further development for efficient applications.…”

Section: Model-based Anti-biofouling Strategiesmentioning

confidence: 99%

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

et al. 2023

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The membrane bioreactor (MBR) is an efficient technology for the treatment of municipal and industrial wastewater for the last two decades. It is a single stage process with smaller footprints and a higher removal efficiency of organic compounds compared with the conventional activated sludge process. However, the major drawback of the MBR is membrane biofouling which decreases the life span of the membrane and automatically increases the operational cost. This review is exploring different anti-biofouling techniques of the state-of-the-art, i.e., quorum quenching (QQ) and model-based approaches. The former is a relatively recent strategy used to mitigate biofouling. It disrupts the cell-to-cell communication of bacteria responsible for biofouling in the sludge. For example, the two strains of bacteria Rhodococcus sp. BH4 and Pseudomonas putida are very effective in the disruption of quorum sensing (QS). Thus, they are recognized as useful QQ bacteria. Furthermore, the model-based anti-fouling strategies are also very promising in preventing biofouling at very early stages of initialization. Nevertheless, biofouling is an extremely complex phenomenon and the influence of various parameters whether physical or biological on its development is not completely understood. Advancing digital technologies, combined with novel Big Data analytics and optimization techniques offer great opportunities for creating intelligent systems that can effectively address the challenges of MBR biofouling.

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Section: Model-based Anti-biofouling Strategiesmentioning

confidence: 99%

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

et al. 2023

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“…The authors compared the performance of ANN with that of the Hermia models [24] and obtained similar performance results for the fitted experiments, but better ANN extrapolation capability for experiments not included in the fitting process. Other AI-based modelling tools, such as fuzzy logic, have proven to be an alternative to ANN modelling [25].…”

Section: Intelligent Control Approaches For Low-pressure Membrane Sys...mentioning

confidence: 99%

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process

et al. 2022

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Peristaltic pumping is used in membrane applications where high and sterile sealing is required. However, control is difficult due to the pulsating pump characteristics and the time-varying properties of the system. In this work, three artificial intelligence control strategies (artificial neural networks (ANN), fuzzy logic expert systems, and fuzzy-integrated local models) were used to regulate transmembrane pressure and crossflow velocity in a microfiltration system under high fouling conditions. A pilot plant was used to obtain the necessary data to identify the AI models and to test the controllers. Humic acid was employed as a foulant, and cleaning-in-place with NaOH was used to restore the membrane state. Several starting operating points were studied and setpoint changes were performed to study the plant dynamics under different control strategies. The results showed that the control approaches were able to control the membrane system, but significant differences in the dynamics were observed. The ANN control was able to achieve the specifications but showed poor dynamics. Expert control was fast but showed problems in different working areas. Local models required less data than ANN, achieving high accuracy and robustness. Therefore, the technique to be used will depend on the available information and the application dynamics requirements.

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“…From an operational point of view, fouling can be controlled and reduced using various techniques such as air bubble (aeration) control, backwashing, relaxation, and chemical cleaning [ 9 ]. Understanding the dynamic and prediction performance of membrane processes is very important, because with that information, the operation and control of the membrane process can be carried out more effectively in the future.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

Ibrahim

Wahab

2022

Membranes

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This paper presents an improved artificial neural network (ANN) training using response surface methodology (RSM) optimization for membrane flux prediction. The improved ANN utilizes the design of experiment (DoE) technique to determine the neural network parameters. The technique has the advantage of training performance, with a reduced training time and number of repetitions in achieving good model prediction for the permeate flux of palm oil mill effluent. The conventional training process is performed by the trial-and-error method, which is time consuming. In this work, Levenberg–Marquardt (lm) and gradient descent with momentum (gdm) training functions are used, the feed-forward neural network (FFNN) structure is applied to predict the permeate flux, and airflow and transmembrane pressure are the input variables. The network parameters include the number of neurons, the learning rate, the momentum, the epoch, and the training functions. To realize the effectiveness of the DoE strategy, central composite design is incorporated into neural network methodology to achieve both good model accuracy and improved training performance. The simulation results show an improvement of more than 50% of training performance, with less repetition of the training process for the RSM-based FFNN (FFNN-RSM) compared with the conventional-based FFNN (FFNN-lm and FFNN-gdm). In addition, a good accuracy of the models is achieved, with a smaller generalization error.

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Soft computing techniques in modelling of membrane filtration system: a review

Cited by 15 publications

References 92 publications

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

Prolonging the Life Span of Membrane in Submerged MBR by the Application of Different Anti-Biofouling Techniques

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process

Improved Artificial Neural Network Training Based on Response Surface Methodology for Membrane Flux Prediction

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