Feed Spacer Of Spiral Wound Membrane Module For Nanofiltration And Reverse Osmosis : Modeling, Simulation And Design [TP248.25.M46 L366 2008 f rb].

Sejak 1970an, permintaan untuk modul membran “spiral wound” (SWM) meningkat dengan mendadak di kedua-dua pasaran tempatan dan antarabangsa. Since 1970s, the demand for spiral wound membrane (SWM) has been rapidly increasing in both local and worldwide market. Current market available SWM possess lifespan between one to three years depends on the applications

Feed Spacer Of Spiral Wound Membrane Module For Nanofiltration And Reverse Osmosis: Modeling, Simulation And Design

Sejak 1970an, permintaan untuk modul membran “spiral wound” (SWM) meningkat dengan mendadak di kedua-dua pasaran tempatan dan antarabangsa. Since 1970s, the demand for spiral wound membrane (SWM) has been rapidly increasing in both local and worldwide market. Current market available SWM possess lifespan between one to three years depends on the applications

Synthesis and Characterization of Polyethersulfone/Carbon Molecular Sieve Based Mixed Matrix Membranes for Water Treatment Applications

AbstractNovel mixed matrix membrane was prepared by incorporating the carbon molecular sieves (CMS) into polyethersulfone (PES) matrix. Flat sheet membranes of different filler concentrations were synthesized through phase inversion technique. Scanning electron microscope and Thermogravimetric analyzer were used to investigate the morphology and thermal stability of synthesized membranes respectively. Finally membranes were tested for their pure water flux and sodium chloride (NaCl) rejection (100ppm aqueous solution). Investigation has shown that all synthesized membranes had asymmetric structure with thin dense top and well-defined macropores in sublayer. It is noticed that inclusion of inorganic filler has improved the thermal stability as well as pure water flux of mixed matrix membranes (upto 33.8LMH at 3bar). Moreover, synthesized mixed matrix membranes also showed better NaCl rejection (upto 26.13% at 3bar) than pure polymeric membranes

Towards Improving Resilience of Smart Urban Electricity Networks by Interactively Assessing Potential Microgrids

When a city adds a renewable generation to improve its carbon footprint, this step towards a greener city can be a step towards a smarter city. Strategical positioning of new urban electricity components makes the city more resilient to electricity outages. Money and resilience are two conflicting goals in this case. In case of blackouts, renewable generation, other than distributed combustion generations, can serve critical demand to essential city nodes, such as hospitals, water purification facilities, and police stations. Not the last, the city level stakeholders might be interested in envisioning monetary saving related to introducing a renewable. To provide decision makers with resilience and monetary information, it is needed to analyze the impact of introducing the renewable into the grid. This paper introduces a novel tool suitable for this purpose and reports on the validation efforts. The outcomes indicate that predicted outcomes of two alternative points of introducing renewables into the grid can be analyzed with the help of the tool and ultimately be meaningfully compared

Evaluation of the Hybrid Pedagogic Method in Students’ Progression in Learning Using Neural Network Modelling and Prediction

The COVID-19 pandemic has changed dramatically the way how universities ensure the continuous and sustainable way of educating students. This paper presents the evaluation of the hybrid pedagogic methods in students’ progression in Learning using neural network (NN) modelling and prediction. The hybrid pedagogic approach is based on the revised Bloom’s taxonomy in combination with the flipped classroom, asynchronous and cognitive learning approach. Educational data of labs and class test scores, as well as students’ total engagement and attendance metrics for the programming module are considered in this study. Conventional statistical evaluations are performed to evaluate students’ progression in learning. The NN is further modelled with six input variables, two layers of hidden neurons, and one output layer. Levenberg-Marquardt algorithm is employed as the back propagation training rule. The performance of neural network model is evaluated through the error performance, regression, and error histogram. Overall, the NN model presents how the hybrid pedagogic method in this case has successfully quantified students’ progression in learning throughout the COVID-19 period

Bulk CO2/CH4 separation for offshore operating conditions using membrane process

The increasing demands of natural gas pushes energy industries to explore the reservoirs contain high CO2 concentration and impurities including heavy hydrocarbons. High efficiency of using membrane technology in CO2-natural gas separation has extended its potential application to offshore environment. Due to the limited studies related with the separation of CO2 under offshore conditions, the present work has investigated the separation performance of a commercial membrane in removing bulk CO2 from methane at elevated pressure condition. A wide range of offshore operating conditions including pressure from 10 to 50 bar, CO2 concentration from 25 to 70% and temperature of 30oC, 40oC and 50oC were studied. High relative CO2 permeance and relative CO2/CH4 selectivity were observed when the pressure and the CO2 concentration increased. This work, therefore substantial is to bridge the gap and facilitates the application of membrane technology for offshore operating conditions

Prediction of CO2 Permeability in NH2-MIL-53(Al)/Cellulose Acetate Mixed matrix Membranes using Theoretical Models

Estimation of CO2 permeability of mixed matrix membranes (MMMs) using models has importance for the design of membrane separation system. In the current article, the previously reported models were used for the calculations of CO2 permeability through new type of MMMs, NH2-MIL-53(Al)/CA. It was found that modified Maxwell model demonstrated the absolute average error (AARE %) of 1.66%, which is lower than the AARE% obtained from the other theoretical models. Besides, the results also showed that AARE% of models for the prediction of CO2 permeability was in the order of modified Maxwell model < Lewis-Nielsen model < Fleski model < Bruggeman model < Pal model< modified Fleski model < Maxwell model. Therefore, it can be concluded that modified Maxwell model is more accurate compared to other theoretical models for the prediction of CO2 permeability through NH2-MIL-53(Al)/CA MMMs