Efficient Regeneration Of Chemical Solvents For Carbon Dioxide Capture By Polymeric Membrane Contactors

Although extensive research attention has been drawn to using membranes for carbon dioxide (CO2) capture from flue gas, the use of membranes for stripping CO2 solvents has rarely been studied. The technical feasibility of using polymeric membrane based separation technology to recover CO2 from CO2 saturated chemical solvents such as monoethanolamine is investigated in the present research. A membrane system was built to study the performance of several common polymeric porous membranes for the recovery of CO2 from saturated aqueous MEA solution by the thermal swing process. The stripped CO2 gas was swept by mass flow controlled N2 reference gas and was measured by a non-dispersive infrared CO2 analyzer and gas chromatography. Substantial CO2 permeation flux through the membrane together with superior selectivity suggests the promises of membrane contactors as an alternative stripping configuration for CO2 recovery. Parametric screening design of experiments studied parameters of process temperature, retentate flow rate, and sweep gas rate. Process temperature was identified as the only significant factor, which is consistent with individual parametric study results. Heat energy efficiency characterization of this system showed that roughly half of the heat energy was used for the stripping process at 80ºC and above. The membrane material candidates screening experiment results showed that polypropylene and polytetrafluoroethylene porous membranes outperformed polyester, polyamide, polyvinylidene fluoride, polysulfone and cellulose acetate. Compositional, structural and surface morphological characterization was also utilized on the membranes before and after this process. Mass transfer mechanism study and mass transfer coefficients calculation reveals that the liquid boundary layer resistance is responsible for more than 90% of the overall mass transfer resistance, much greater than either the membrane resistance or gas layer resistance. Membrane wetting and fouling effects were found to deteriorate membrane performance. Polypropylene membranes with different pore size were studied and compared. There was no significantly change of CO2 flux for membrane pore size from 0.1micron to 2.5 micron. The membrane with pore size of 0.6 micron was found to have best selectivity. The energy utilization efficiency did not change significantly for membranes with different pore size. Membranes with pore size 2.5 micron and below were found to be not wetted during the experiments and membranes with pore size of 5 micron and 10 micron were wetted during the process

Orthonormal Product Quantization Network for Scalable Face Image Retrieval

Recently, deep hashing with Hamming distance metric has drawn increasing attention for face image retrieval tasks. However, its counterpart deep quantization methods, which learn binary code representations with dictionary-related distance metrics, have seldom been explored for the task. This paper makes the first attempt to integrate product quantization into an end-to-end deep learning framework for face image retrieval. Unlike prior deep quantization methods where the codewords for quantization are learned from data, we propose a novel scheme using predefined orthonormal vectors as codewords, which aims to enhance the quantization informativeness and reduce the codewords' redundancy. To make the most of the discriminative information, we design a tailored loss function that maximizes the identity discriminability in each quantization subspace for both the quantized and the original features. Furthermore, an entropy-based regularization term is imposed to reduce the quantization error. We conduct experiments on three commonly-used datasets under the settings of both single-domain and cross-domain retrieval. It shows that the proposed method outperforms all the compared deep hashing/quantization methods under both settings with significant superiority. The proposed codewords scheme consistently improves both regular model performance and model generalization ability, verifying the importance of codewords' distribution for the quantization quality. Besides, our model's better generalization ability than deep hashing models indicates that it is more suitable for scalable face image retrieval tasks

Estimating the accuracy of the random walk simulation of mass transport

The mass transport processes always accompanies the flow phenomena and have attracted many researches. A lot of numerical methods have been developed to study them. These numerical methods can be classified into the Eulerian and the Lagrangian approaches. The Lagrangian approach has advantages in high stability and simplicity over the Eulerian approach, but suffers from heavy computational cost. In this paper, we are mainly concerned with the trade-offs between the accuracy and computational cost when applying the random walk method, which is a Lagrangian approach for examining the mass transport scenario. We introduce a linear model to assess the accuracy of the random walk method in several computational configurations. Studies on computational parameters, i.e. the size of time step and number of particles, are conducted with the focus on estimation of the longitudinal dispersion coefficient in steady flows. The results show that the proposed linear model can satisfactorily explain the computational accuracy, both in sample and out-of-sample. Furthermore, we find a constant dimensionless parameter, which quantifies a generic relationship between the accuracy and the number of particles regardless of the flow and diffusion conditions. This dimensionless parameter is of theoretic value and offers guidelines for choosing the correct computational parameters to achieve the required numerical accuracy.National Natural Science Foundation of China (51809219), State key Laboratory of Hydroscience and Engineering (sklhse-2019-B-02), Royal Academy of Engineering (Grant No. UUFRIP\100051), Ministry of Education and State Administration of Foreign Experts Affair

The Application of Post-Humanism in Digital Media Visual Design——Cyberpunk 2077 as an Example

This paper answers the meaning of “post-humanism” and explains the characteristics of Cybopunk digital media style under the influence of post-humanism. Based on this, we analyze the application of this art style in games by taking Cybopunk 2077 as an example. Finally, I will explain the value of posthumanism to modern media technology and digital culture. To explain the value of post-humanism in modern media technology and digital culture is to better convey the art philosophy of “post-humanism” and the artistic attitude behind it to the public, promote the development of media technology, and further promote and expand the diversity of digital culture

An Evaluation Scheme for the Quality of Reviews

In recent years, with the development of e-commerce, the research of product reviews has become more and more important. The high-quality reviews can provide sufficient information to help customers choose the products. In this paper, we built an evaluation scheme for the quality of reviews base on machine learning. The dataset was obtained by the crawler and labeled through manual annotation. We vectorize the reviews by TF-IDF for the SVM model as the basic algorithm. We used the sentiment lexicon analysis method to get a score as the sentiment feature to improve the SVM model. We compared SENTIWORDNET, AFINN, VADER three different sentiment lexicons and chose the best lexicon—VADER to get the sentiment score and add it to the SVM model. The Self-training which is a semi-supervised learning method combined with the unlabeled dataset was built based on the SVM model to solve the problem of insufficient dataset. We also built the LSTM neural network with word embedding of global vectors for word representation and improved it by virtual adversarial training method. We compared the neural network methods with SVM methods. The result shows that the virtual adversarial training model worked better in the evaluation scheme for the quality of reviews