AIMC Topic: Filtration

Clear Filters Showing 11 to 20 of 23 articles

Improvement of DBR routing protocol in underwater wireless sensor networks using fuzzy logic and bloom filter.

PloS one
Routing protocols for underwater wireless sensor networks (UWSN) and underwater Internet of Things (IoT_UWSN) networks have expanded significantly. DBR routing protocol is one of the most critical routing protocols in UWSNs. In this routing protocol,...

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...

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 ...

Application of machine learning methods to pathogen safety evaluation in biological manufacturing processes.

Biotechnology progress
The production of recombinant therapeutic proteins from animal or human cell lines entails the risk of endogenous viral contamination from cell substrates and adventitious agents from raw materials and environment. One of the approaches to control su...

Performance assessment of gas-phase toluene removal in one- and two-liquid phase biotrickling filters using artificial neural networks.

Chemosphere
The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase biotrickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 d in the presence of ...

Prediction of membrane fouling using artificial neural networks for wastewater treated by membrane bioreactor technologies: bottlenecks and possibilities.

Environmental science and pollution research international
Membrane fouling is a major concern for the optimization of membrane bioreactor (MBR) technologies. Numerous studies have been led in the field of membrane fouling control in order to assess with precision the fouling mechanisms which affect membrane...

Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

Biotechnology progress
This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating pa...

Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (...

Predicting Membrane Fouling of Submerged Membrane Bioreactor Wastewater Treatment Plants Using Machine Learning.

Environmental science & technology
Membrane fouling remains a significant challenge in the operation of membrane bioreactors (MBRs). Plant operators rely heavily on observations of filtration performance from noisy sensor data to assess membrane fouling conditions and lab-based protoc...

Removing aflatoxin M1 from milk with native lactic acid bacteria, centrifugation, and filtration.

Arhiv za higijenu rada i toksikologiju
In order to minimise human exposure to aflatoxin M1 (AFM1) the levels of this highly carcinogenic mycotoxin in milk, heat-treated milk, and other dairy products have been limited to <0.05 μg kg-1. However, its removal from dairy products presents a c...