Ultrasonic vibration - assisted pelleting and dilute acid pretreatment of cellulosic biomass for biofuel manufacturing

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringZhijian PeiDonghai WangIn the U.S. and many other countries, the transportation sector is almost entirely dependent on petroleum-based fuels. In 2011, half of the petroleum used in the U.S. was imported. The dependence on foreign petroleum is a real threat to national energy security. Furthermore, the transportation sector is responsible for about 30% of U.S. greenhouse gas emissions and is growing faster than any other major economic sector. National energy security, economy, environment sustainability are all driving the U.S. to develop alternative liquid transportation fuels that are domestically produced and environmentally friendly. Promoting biofuel is one of the efforts to reduce the use of petroleum-based fuels in the transportation sector. Cellulosic biomass are abundant and diverse. Thus, the ability to produce biofuel from cellulosic biomass will be a key to making ethanol competitive with petroleum-based fuels. Ultrasonic vibration- assisted (UV-A) pelleting can increase not only the density of cellulosic biomass but also the sugar yield. This PhD dissertation consists of fourteen chapters. Firstly, an introduction of the research is given in Chapter 1. Chapters 2, 3, 4, and 5 present experimental investigations on effects of input variables in UV-A pelleting on pellet quality. Chapter 6 investigates effects of input variables on energy consumption in UV-A pelleting. Chapter 7 develops a predictive model for energy consumption in UV-A pelleting using the response surface method. Chapter 8 investigates effects of input variables on energy consumption, water usage, sugar yield, and pretreatment energy efficiency in dilute acid pretreatment. Chapter 9 develops a predictive model for energy consumption in dilute acid pretreatment using the response surface method. Chapter 10 studies ultrasonic vibration-assisted (UV-A) dilute acid pretreatment of poplar wood for biofuel manufacturing. Chapter 11 compares sugar yields in terms of total sugar yield and enzymatic hydrolysis sugar yield between two kinds of materials: pellets processed by UV-A pelleting and biomass not processed by UV-A pelleting in terms of total sugar yield and enzymatic hydrolysis sugar yield. Chapter 12 develops a physics-based temperature model to predict temperature in UV-A pelleting. Chapter 13 develops a physics-based density model to predict pellet density in UV-A pelleting. Finally, conclusions and contributions of this research are summarized in Chapter 14

Biofuel Manufacturing from Woody Biomass: Effects of Sieve Size Used in Biomass Size Reduction

Size reduction is the first step for manufacturing biofuels from woody biomass. It is usually performed using milling machines and the particle size is controlled by the size of the sieve installed on a milling machine. There are reported studies about the effects of sieve size on energy consumption in milling of woody biomass. These studies show that energy consumption increased dramatically as sieve size became smaller. However, in these studies, the sugar yield (proportional to biofuel yield) in hydrolysis of the milled woody biomass was not measured. The lack of comprehensive studies about the effects of sieve size on energy consumption in biomass milling and sugar yield in hydrolysis process makes it difficult to decide which sieve size should be selected in order to minimize the energy consumption in size reduction and maximize the sugar yield in hydrolysis. The purpose of this paper is to fill this gap in the literature. In this paper, knife milling of poplar wood was conducted using sieves of three sizes (1, 2, and 4 mm). Results show that, as sieve size increased, energy consumption in knife milling decreased and sugar yield in hydrolysis increased in the tested range of particle sizes

Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: https://github.com/lutsong/LCCRN

Prediction for the surface settlement of double-track subway tunnels for shallow buried loess based on peck formula

In the process of constructing double-track subway tunnels in shallow buried loess areas, the interaction of double-track tunnels is significantly influenced by the net distance and the cross-section size, which is challenging to control the surface settlement. Therefore, the surface settlement prediction is essential while constructing double-track subway tunnels in shallow buried loess areas. The paper analyzed the surface settlement law of shallow buried double-track tunnels in loess areas through theoretical research and numerical simulation. The research results show that with the decrease of the net distance, the surface settlement superimposed curve was double V shape -W shape - single V shape. When the superimposed curve is double V shape and W shape, the Peck formula was used to calculate the surface settlement curve of the single-track tunnel, then superimposed to obtain the final surface settlement curve. When the superimposed surface settlement curve was V shape, based on the Peck formula, the formula for predicting the surface settlement suitable for symmetry and asymmetry was established. The net distance ratio and the area ratio were defined, and considering the tunnel’s interaction, the value and position of the maximum were corrected. Then numerical tests were carried out 16 times with different net distance ratios and area ratios, to determine the parameters of increments and position offsets of the maximum regarding the net distance ratio and the area ratio. Finally, two engineering were conducted for verifying the rationality and applicability exhibited by the prediction formula. The prediction formula served for predicting the surface settlement of double-track subway tunnels in shallow buried loess areas. Which can reduce construction risks and assure the safety of buildings above the ground

Clinical Effects of Shenqi Fuzheng Injection in the Neoadjuvant Chemotherapy for Local Advanced Breast Cancer and the Effects on T-lymphocyte Subsets

ObjectiveTo evaluate clinical effects of Shenqi Fuzheng Injection in the neoadjuvant chemotherapy for local advanced breast cancer and the effects on T-lymphocyte subsets.MethodsDuring the period from 2000 to 2005, 126 patients with local advanced breast cancer were treated with the neoadjuvant chemotherapy. They were randomly divided into the following two groups: a control group of 61 cases treated by chemotherapy alone and a study group of 65 cases treated by chemotherapy plus Shenqi Fuzheng Injection. All the cases of both groups were given the CEF (CTX 500 mg/m2, d1, 8; EPI 40 mg/m2, d1, 8; and 5-Fu 500 mg/m2, d1,8) regimen. The clinical effects, the effects on T-lymphocyte subgroup and NK cells, and the toxic side effects were observed.ResultsAll the patients completed two cycles of the chemotherapy, and the efficacy and the toxic side effects were evaluated. For the primary tumor in the breast, the total effective rate was 69.2% (45/65) in the study group and 49.2% (30/61) in the control group with a statistically significant difference in the intergroup comparison (χ 2=5.251, P=0.022, < 0.05). There was no progression of the disease in both the groups, and there were no grade IV toxic side effects in the two groups. The major toxic responses were myelosuppression and gastrointestinal reaction, which were milder in the study group than the control group, and with a shorter recovery course in the former than the latter. Besides, an obvious rise of the T-lymphocyte subgroup and NK cells was found in the study group after the neoadjuvant chemotherapy, with a very significant difference from the controls (P < 0.01).ConclusionShenqi Fuzheng Injection can improve and regulate immune function of the patients with local advanced breast cancer given the neoadjuvant chemotherapy, and therefore it can enhance the curative effect and reduce the side effect as well

Characterizing Depression Issues on Sina Weibo

The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders.The authors acknowledge the support from the Harbin Institute of Technology’s Visiting Scholar Program and the National Natural Science Foundation of China (grant no. 71531013)

Dual Prototypical Network for Robust Few-shot Image Classification

Deep neural networks have outperformed humans on some image recognition and classification tasks. However, with the emergence of various novel classes, it remains a chal-lenge to continuously expand the learning capability of such networks from a limited number of labeled samples. Metric-based approaches have been playing a key role in few-shot image classification, but most of them measure the distance between samples in the metric space using only a single metric function. In this paper, we propose a Dual Prototypical Network (DPN) to improve the test-time robustness of the classical prototypical network. The proposed method not only focuses on the distance of the original features, but also adds perturbation noise to the image and calculates the distance of noisy features. By enforcing the model to predict well under both metrics, more representative and robust class prototypes are learned and thus lead to better generalization performance. We validate our method on three fine-grained datasets in both clean and noisy settings

Prediction of high-Tc superconductivity in ternary lanthanum borohydrides

The study of superconductivity in compressed hydrides is of great interest due to measurements of high critical temperatures (Tc) in the vicinity of room temperature, beginning with the observations of LaH10 at 170-190 GPa. However, the pressures required for synthesis of these high Tc superconducting hydrides currently remain extremely high. Here we show the investigation of crystal structures and superconductivity in the La-B-H system under pressure with particle-swarm intelligence structure searches methods in combination with first-principles calculations. Structures with six stoichiometries, LaBH, LaBH3, LaBH4, LaBH6, LaBH7 and LaBH8, were predicted to become stable under pressure. Remarkably, the hydrogen atoms in LaBH8 were found to bond with B atoms in a manner that is similar to that in H3S. Lattice dynamics calculations indicate that LaBH7 and LaBH8 become dynamically stable at pressures as low as 109.2 and 48.3 GPa, respectively. Moreover, the two phases were predicted to be superconducting with a critical temperature (Tc) of 93 K and 156 K at 110 GPa and 55 GPa, respectively. Our results provide guidance for future experiments targeting new hydride superconductors with both low synthesis pressures and high Tc.Comment: 16 pages, 5 figures