AIMC Topic: Membranes, Artificial

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Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches.

Chemosphere
Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computation...

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models.

Chemosphere
Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on me...

Artificial intelligence and structural design of inorganic hollow fiber membranes: Materials chemistry.

Chemosphere
A key challenge is to produce the uniform morphology and regular pore design of inorganic hollow fiber membranes (HFMs) due to involvement of multiple parameters including, fabrication process and materials chemistry. Inorganic HFMs required technica...

Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review.

Water research
Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to con...

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization.

Environmental science & technology
Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimenta...

Modelling of transmembrane pressure using slot/pore blocking model, response surface and artificial intelligence approach.

Chemosphere
This work investigates the application of empirical, statistical and machine learning methods to appraise the prediction of transmembrane pressure (TMP) by oscillating slotted pore membrane for the treatment of two kinds of deformable oil drops. Here...

Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors.

The Science of the total environment
In this study, data-driven deep learning methods were applied in order to model and predict the treatment of real municipal wastewater using anaerobic membrane bioreactors (AnMBRs). Based on the one-year operating data of two AnMBRs, six parameters r...

Prediction of the chromatographic hydrophobicity index with immobilized artificial membrane chromatography using simple molecular descriptors and artificial neural networks.

Journal of chromatography. A
Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating i...

Deep learning model for simulating influence of natural organic matter in nanofiltration.

Water research
Controlling membrane fouling in a membrane filtration system is critical to ensure high filtration performance. A forecast of membrane fouling could enable preliminary actions to relieve the development of membrane fouling. Therefore, we established ...

Real-time monitoring of forward osmosis membrane fouling in wastewater reuse process performed with a deep learning model.

Chemosphere
Monitoring fouling behavior for better understanding and control has recently gained increasing attention. However, there is no practical method for observing membrane fouling in real time, especially in the forward osmosis (FO) process. In this arti...