Study Of The Effect Of Surface Morphology On Mass Transfer And Fouling Behavior Of Reverse Osmosis And Nanofiltration Membrane Processes

Reverse osmosis (RO) and nanofiltration (NF) membranes are pressure driven, diffusion controlled process. The influence of surface characteristics on membrane process performance is considered significant and is not well understood. Current mass transport models generally assume constant mass transfer coefficients (MTCs) based on a homogeneous surface. This work evaluated mass transfer processes by incorporating surface morphology into a diffusion-based model assuming MTCs are dependent on the thickness variation of the membrane’s active layer. To mathematically create such a surface layer, Gaussian random vectors embedded in a software system (MATLAB) were used to generate a three-dimensional ridge and valley active layer morphologies. A “SMOOTH” script was incorporated to reduce the influence of outlying data and make the hypothetical surfaces visually comparable to the AFM images. A nonhomogeneous solution diffusion model (NHDM) was then developed to account for surface variations in the active layer. Concentration polarization (CP) is also affected by this nonhomogeneous surface property; therefore, the NHDM was modified by incorporating the CP factor. In addition, recent studies have shown that the membrane surface morphology influences colloidal fouling behavior of RO and NF membranes. With consideration of the spatial variation of the cake thickness along the membranes, a fouling model was established by assuming cake growth is proportional to the localized permeate flow. Flux decline was assumed to be controlled by the resistance of cake growth and accumulated particle back diffusion at the membrane surface. A series of simulations were performed using operating parameters and water qualities data collected from a full-scale brackish water reverse osmosis membrane water treatment plant. The membrane channel was divided into a thousand uniform slices and the water qualities were iii determined locally through a finite difference approach. Prediction of the total dissolved solid (TDS) permeate concentration using the model was found to be accurate within 5% to 15% as an average percentage of difference (APD) using the NHDM developed in this research work. A comparison of the NHDM and the modified NHDM for concentration polarization (CP) with the commonly accepted homogeneous solution diffusion model (HSDM) using pilot-scale brackish water RO operating data indicated that the NHDM is more accurate when the solute concentration in the feed stream is low, while the NHDMCP appears to be more predictive of permeate concentration when considering high solute feed concentration. Simulation results indicated that surface morphology affects the water qualities in the permeate stream. Higher salt passage was expected to occur at the valley areas when diffusion mass transfer would be greater than at the peaks where the thin-film membrane is thicker. A rough surface tends to increase the TDS accumulation on the valley areas, causing an enhanced osmotic pressure at the valleys of membrane. To evaluate the impact of surface morphology on RO and NF performance, fouling experiments were conducted using flat-sheet membrane and three different nanoparticles, which included SiO2, TiO2 and CeO2. In this study, the rate and extent of fouling was markedly influenced by membrane surface morphology. The atomic force microscopy (AFM) analysis revealed that the higher fouling rate of RO membranes compared to that of NF membranes is due to the inherent ridge-and-valley morphology of the RO membranes. This unique morphology increases the surface roughness, leading to particle accumulation in the valleys, causing a higher flux decline than in smoother membranes. Extended fouling experiments were conducted using one of the RO membranes to compare the effect of different particles on actual water. It was determined that membrane flux decline was not affected by particle type when the feed water iv was laboratory grade water. On the other hand, membrane flux decline was affected by particle type when diluted seawater served as the feed water. It was found that CeO2 addition resulted in the least observable flux decline and fouling rate, followed by SiO2 and TiO2. Fouling simulation was conducted by fitting the monitored flux data into a cake growth rate model. The model was discretized by a finite difference method to incorporate the surface thickness variation. The ratio of cake growth term (�1) and particle back diffusion term (�2) was compared in between different RO and NF membranes. Results indicate that �2 was less significant for surfaces that exhibited a higher roughness. It was concluded that the valley areas of thin-film membrane surfaces have the ability to capture particles, limiting particle back diffusion

Why does the apparent mass of a coronal mass ejection increase?

Mass is one of the most fundamental parameters characterizing the dynamics of a coronal mass ejection (CME). It has been found that CME apparent mass measured from the brightness enhancement in coronagraph images shows an increasing trend during its evolution in the corona. However, the physics behind it is not clear. Does the apparent mass gain come from the mass outflow from the dimming regions in the low corona, or from the pileup of the solar wind plasma around the CME when it propagates outwards from the Sun? We analyzed the mass evolution of six CME events. Their mass can increase by a factor of 1.6 to 3.2 from 4 to 15 Rs in the field of view (FOV) of the coronagraph on board the Solar Terrestrial Relations Observatory (STEREO). Over the distance about 7 to 15 Rs, where the coronagraph occulting effect can be negligible, the mass can increase by a factor of 1.3 to 1.7. We adopted the `snow-plough' model to calculate the mass contribution of the piled-up solar wind in the height range from about 7 to 15 Rs. For 2/3 of the events, the solar wind pileup is not sufficient to explain the measured mass increase. In the height range from about 7 to 15 Rs, the ratio of the modeled to the measured mass increase is roughly larger than 0.55. Although the ratios are believed to be overestimated, the result gives evidence that the solar wind pileup probably makes a non-negligible contribution to the mass increase. It is not clear yet whether the solar wind pileup is a major contributor to the final mass derived from coronagraph observations. However, our study suggests that the solar wind pileup plays increasingly important role in the mass increase as a CME moves further away from the Sun.Comment: 27 pages, 2 tables, 9 figures, accepted by Ap

A Perceptually Optimized and Self-Calibrated Tone Mapping Operator

With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid. We then use two lightweight deep neural networks (DNNs), taking the normalized representation as input and estimating the Laplacian pyramid of the corresponding LDR image. We optimize the tone mapping network by minimizing the normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with human judgments of tone-mapped image quality. In Stage two, the input HDR image is self-calibrated to compute the final LDR image. We feed the same HDR image but rescaled with different maximum luminances to the learned tone mapping network, and generate a pseudo-multi-exposure image stack with different detail visibility and color saturation. We then train another lightweight DNN to fuse the LDR image stack into a desired LDR image by maximizing a variant of the structural similarity index for multi-exposure image fusion (MEF-SSIM), which has been proven perceptually relevant to fused image quality. The proposed self-calibration mechanism through MEF enables our TMO to accept uncalibrated HDR images, while being physiology-driven. Extensive experiments show that our method produces images with consistently better visual quality. Additionally, since our method builds upon three lightweight DNNs, it is among the fastest local TMOs.Comment: 20 pages,18 figure

Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network

MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks