Latex Sphere Retention by Microporous Membranes in Liquid Filtration

1993 ◽  
Vol 36 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Jae-Keun Lee ◽  
Benjamin Liu ◽  
Kenneth Rubow
Keyword(s):  
Test System ◽  
Polystyrene Latex ◽  
Microporous Membranes ◽  
Cake Filtration ◽  
Hydrophobic Membrane ◽  
Worst Case ◽  
Liquid Filtration ◽  
Filter Surface ◽  
Filter Performance ◽  
Latex Sphere

An experimental study of particulate matter retention by microporous membranes during liquid filtration has been conducted using 0.1, 0.22, 0.45, and 0.65-μm-rated hydrophilic and hydrophobic membrane filters. Retention measurements have been made with polystyrene latex spheres using an automated filter test system and a laser particle counter to measure the upstream and downstream particle concentration. Particle filtration during loading tests was found to begin with a sieving dominant regime followed by a transition regime and a cake filtration regime as particles accumulate inside the filter pores and on the filter surface. For latex sphere sizes equal to the nominal pore size of the filter, the initial filter efficiencies ranged from 97 to 99.9 percent. Complete retention (>99.9999999 percent) was achieved for a range of particle sizes two to three times the rated pore sizes of the filter. With the addition of a surfactant to the liquid, the retention was found to be lowered as a result of enhanced particle passage through the filter due to modified surface adsorption and steric stabilization. It was found that particle retention by sieving with the addition of surfactant provided the "worst-case" test for filter performance.

Development of Hydrophobic Microporous Isotactic Polypropylene Membrane for Membrane Contactor Application

2015 ◽  
Vol 1113 ◽  
pp. 36-42
Author(s):  
Wan Jusoh Wan Zulaisa Amira ◽  
Abdul Rahman Sunarti
Keyword(s):  
Isotactic Polypropylene ◽  
Test System ◽  
Membrane Contactor ◽  
Thermally Induced Phase Separation ◽  
Hydrophobic Membrane ◽  
Operating Period ◽  
Thermally Induced ◽  
Polypropylene Membrane ◽  
Membrane Fabrication ◽  
Contact Instrument

Membrane Contactor (MC) is a well-known membrane technology to provide significant advantages required by industries. For MC, a hydrophobic membrane required as a barrier so that liquid absorbent and flue gaseous do not disperse with one another. However, the major concern in hydrophobic membrane is getting swelling by liquid after a short operating period. To minimize the swelling, this study focused on the exploration on membrane fabrication by Thermally Induced Phase Separation (TIPS). As the immersion in solvents is one of the important step to extract the diluent from membranes pores, the effect of the immersion in methanol was studied. The productions of hydrophobic microporous flat sheets were accomplished by using isotactic Polypropylene (iPP) and two type diluents: Dipenyl Ether (DPE) and Methyl Salicylate (MS). The measurement of hydophobicity of membranes produced was conducted by Test System of JY-82 Video Contact Instrument. Membranes produced were characterized by Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR). The highest hydrophobicity obtained were 124°(three hours immersed in methanol) and 107° (two hours immersed in methanol) by DPE and MS respectively. All membranes show spherical pores, indicating that membranes were formed via liquid-liquid TIPS and strong bond alkane group by Infrared (IR) spectras show that the membranes produce did not change when undergo TIPS process.

The Study of Switching Overvoltages under Power System Restoration Scenario Using Extended Delta-Bar-Delta Algorithm

2013 ◽  
Vol 14 (3) ◽  
pp. 219-230 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Random Search ◽  
Test System ◽  
Power Equipment ◽  
Case Scenario ◽  
Worst Case ◽  
Power System Restoration ◽  
System Restoration ◽  
Artificial Neural Network Ann

Abstract This paper presents an intelligent approach to evaluate switching overvoltages during power equipment energization. Switching action is one of the most important issues in power system restoration schemes. This action may lead to overvoltages that can damage some equipment and delay ‎power system restoration. In this work, transient overvoltages caused by power equipment energization are analyzed and estimated using artificial neural network (ANN)-based approach. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. In the cases of transformer and shunt reactor energization, ANNs are trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎duration of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

scholarly journals Robust Event-Triggered Energy-to-Peak Filtering for Polytopic Uncertain Systems over Lossy Network with Quantized Measurements

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jidong Wang ◽  
Kezhen Han
Keyword(s):  
Performance Analysis ◽  
Linearization Method ◽  
Packet Dropout ◽  
Event Generator ◽  
Optimization Approach ◽  
Packet Losses ◽  
Worst Case ◽  
Filter Performance ◽  
Analysis And Synthesis ◽  
Event Triggered

The event-triggered energy-to-peak filtering for polytopic discrete-time linear systems is studied with the consideration of lossy network and quantization error. Because of the communication imperfections from the packet dropout of lossy link, the event-triggered condition used to determine the data release instant at the event generator (EG) can not be directly applied to update the filter input at the zero order holder (ZOH) when performing filter performance analysis and synthesis. In order to balance such nonuniform time series between the triggered instant of EG and the updated instant of ZOH, two event-triggered conditions are defined, respectively, whereafter a worst-case bound on the number of consecutive packet losses of the transmitted data from EG is given, which marginally guarantees the effectiveness of the filter that will be designed based on the event-triggered updating condition of ZOH. Then, the filter performance analysis conditions are obtained under the assumption that the maximum number of packet losses is allowable for the worst-case bound. In what follows, a two-stage LMI-based alternative optimization approach is proposed to separately design the filter, which reduces the conservatism of the traditional linearization method of filter analysis conditions. Subsequently a codesign algorithm is developed to determine the communication and filter parameters simultaneously. Finally, an illustrative example is provided to verify the validity of the obtained results.

Filtration and Pulse Cleaning System for Particle Removal from Gas

2010 ◽  
Vol 42 ◽  
pp. 188-191
Author(s):  
Hai Xia Li ◽  
Zhi Jun Sun ◽  
Li Jun Zhao ◽  
Zhan Xu Tie
Keyword(s):  
Test System ◽  
Ceramic Filter ◽  
Surface Velocity ◽  
Particle Removal ◽  
Gas Temperature ◽  
Gas Filtration ◽  
Operation Condition ◽  
Filter Length ◽  
Filter Surface ◽  
Ceramic Filtration

The application of rigid ceramic filter for gas filtration on an industrial scale has shown unstable operation over periods of days. The behavior of the ceramic filtration in further application can be predict by measuring properties of a variety of dust and filtration operation condition. The aim of the experimental investigation is to analysis the pressure distribution inside and outside the ceramic filter along the filter length, and to analyze the pressure drop across the filter along the filter length. Ceramic filter filtration system composes multi-pipe filters for gas clean up is introduced. The operation parameters can be measured by this test system, such as the pressure in the filter cavity and outside the filter surface, velocity outside the filter, gas temperature and humidity, flow rate, dust particle size concentration before gad entering the filter tank and after the gas leaving the filer tank, the pulse cleaning pressure.

scholarly journals Radial Basis Function Neural Network Application to Measurement and Control of Shunt Reactor Overvoltages Based on Analytical Rules

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Early Stage ◽  
Rbf Neural Network ◽  
Test System ◽  
Worst Case ◽  
Shunt Reactor ◽  
Radial Basis

This paper presents an artificial intelligence application to measure switching overvoltages caused by shunt reactor energization by applying analytical rules. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a reactor with residual flux. A radial basis function (RBF) neural network has been used to estimate the overvoltages due to reactor energization. Equivalent circuit parameters of network have been used as artificial neural network (ANN) inputs; thus, RBF neural network is applicable to every studied system. The developed ANN is trained with the worst case of the switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can measure the peak values and duration of switching overvoltages with good accuracy.

scholarly journals Artificial-Intelligence-Based Techniques to Evaluate Switching Overvoltages during Power System Restoration

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Test System ◽  
Power Equipment ◽  
Worst Case ◽  
Power System Restoration ◽  
Switching Action ◽  
System Restoration ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm

This paper presents an approach to the study of switching overvoltages during power equipment energization. Switching action is one of the most important issues in the power system restoration schemes. This action may lead to overvoltages which can damage some equipment and delay power system restoration. In this work, switching overvoltages caused by power equipment energization are evaluated using artificial-neural-network- (ANN-) based approach. Both multilayer perceptron (MLP) trained with Levenberg-Marquardt (LM) algorithm and radial basis function (RBF) structure have been analyzed. In the cases of transformer and shunt reactor energization, the worst case of switching angle and remanent flux has been considered to reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. Developed ANN is tested for a partial of 39-bus New England test system, and results show the effectiveness of the proposed method to evaluate switching overvoltages.

Study of Transformer Switching Overvoltages during Power System Restoration Using Delta-Bar-Delta and Directed Random Search Algorithms

2012 ◽  
Vol 13 (3) ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Power System ◽  
New England ◽  
Early Stage ◽  
Random Search ◽  
Test System ◽  
Worst Case ◽  
Power System Restoration ◽  
Three Phase ◽  
Equivalent Circuit Parameters ◽  
System Restoration

Abstract In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a power system that appears in an early stage of a black ‎start of a power system, an overvoltage could be caused by core ‎saturation on the energization of a three-phase transformer with residual flux. ‎Such an overvoltage might damage some equipment and delay ‎power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate the temporary overvoltages (TOVs) due to three-phase transformer ‎energization. ‎ Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. ANN Training is performed based on equivalent circuit parameters of the network; thus trained ANN is applicable to every studied system. The ‎developed ANN is trained with the worst case of the switching condition and remanent flux, and ‎tested for typical cases. The simulated results for a partial of 39-bus New England test system, ‎show that the proposed technique can estimate the peak values and ‎durations of switching overvoltages with good accuracy and EDBD algorithm presents best performance.

scholarly journals Influence of Carbon Sorbent Quantity on Breakthrough Time in Absorbent Filters for Antismog Half Mask Application

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 584
Author(s):  
Agnieszka Brochocka ◽  
Aleksandra Nowak ◽  
Paweł Kozikowski
Keyword(s):  
Textural Properties ◽  
Woven Fabrics ◽  
Carbon Sorbent ◽  
Breakthrough Time ◽  
Surface Mass ◽  
Carbon Sorbents ◽  
Filter Surface ◽  
Filter Performance ◽  
Efficient Protection ◽  
Available Carbon

In this article, we present polymer non-woven fabrics with the addition of carbon sorbents being tested to estimate the breakthrough time and efficient protection against vapors present in smog. For this purpose, three substances were selected, which constitute an inhalation hazard and are smog components: cyclohexane, toluene, and sulfur dioxide. It was demonstrated that an increased quantity of carbon sorbent in polymeric filters significantly prolongs the breakthrough time. However, high sorbent quantities may increase the filter surface mass and air flow resistance. To optimize the protective parameters with functionality, a compromise between the two has to be found. By comparing the breakthrough times for different carbon sorbent quantities, the optimal filter composition was elaborated. The analyzed non-woven fabrics were manufactured by the melt-blown process and filled with ball-milled carbon sorbents supplied directly into the fabric blowing nozzle. Both protective performance and textural properties were analyzed for two commercially available carbon sorbents. Furthermore, it was proven that high values of sorbent-specific surface area translates directly into greater filter performance.

scholarly journals Radial Basis Function Neural Network Application to Power System Restoration Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Iman Sadeghkhani ◽  
Abbas Ketabi ◽  
Rene Feuillet
Keyword(s):  
Neural Network ◽  
Power System ◽  
Radial Basis Function ◽  
Basis Function ◽  
Test System ◽  
Case Scenario ◽  
Worst Case ◽  
Power System Restoration ◽  
Radial Basis ◽  
System Restoration

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy.

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