AIMC Topic: Filtration
Clear Filters Showing 11 to 20 of 21 articles
Deep learning model for simulating influence of natural organic matter in nanofiltration.
Water research
Mar 20, 2021
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
Feb 24, 2021
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
Jun 8, 2019
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
Sep 4, 2017
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
Aug 22, 2016
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
Dec 11, 2014
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
May 27, 2025
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
Dec 1, 2018
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...
An In-Vitro Study Comparing the GME Handling of Two Contemporary Oxygenators.
The journal of extra-corporeal technology
Dec 1, 2017
Gaseous microemboli (GME) are a potential complication of cardiopulmonary bypass (CPB). Though it is difficult to prove that GME is the only major cause of neurological deficits, it may increase the chance of post-operative cognitive dysfunction if n...
Predicting the performance of multi-media filters using artificial neural networks.
Water science and technology : a journal of the International Association on Water Pollution Research
Nov 1, 2016
The impact of flow rate and turbidity on the performance of multi-media filtration has been studied using an artificial neural network (ANN) based model. The ANN model was developed and tested based on experimental data collected from a pilot scale m...
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