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Analysis of Flue Gas Pollutants Deep-removal Technology in Coal-fired Power Plants

Xiao-lu Zhang

《工程管理前沿（英文）》 2014年 第1卷 第4期   页码 336-340 doi: 10.15302/J-FEM-2014061

摘要： In recent years, frequent haze has made PM become a public hotspot. PM control has been added to the 2012 release “ambient air quality standard.” Currently flue gas pollutant control technology does not easily remove PM . Developing Flue Gas Pollutant Deep-removal Technology (DRT) for coal-fired power plants for deep-removing pollutants such as PM , SO , SO , and heavy metals, is an urgent problem. Based on the analysis of the necessity and existing problems of developing DRT suitable for China, this study focused on PM removal technology, low NO emission of ultra supercritical boiler under all load conditions, and the adaptability of SCR working temperature. Finally, the flue gas pollutant removal system at a 2×660MW supercritical power plant was introduced, and the roadmap for developing DRT for 1,000MW ultra supercritical units was analyzed.

关键词： Coal-fired power plant     flue gas pollutants     deep-removal     PM2.5 removal    

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The R&D of Flue Gas Pollutants Deep-Removal Technology for Coal-fired Power Plants

Xiao-lu Zhang

《工程管理前沿（英文）》 2015年 第2卷 第4期   页码 359-363 doi: 10.15302/J-FEM-2015057

摘要： The flue gas pollutants deep-removal technology (DRT) focusing on PM removal is the prime method of further reducing pollutants emission from coal-fired power plants. In view of the four key technological challenges in developing the DRT, studies were conducted on a series of purification technologies and the DRT was developed and successfully applied in 660 MW and 1000 MW coal-fired units. This paper analyzes the application results of the demonstration project, and proposes a roadmap for the follow-up researches and optimizations.

关键词： coal-fired power plant     pollutants emission reduction     PM2.5     flue gas pollutants     deep-removal    

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燃煤电站烟气污染物深度脱除技术的分析

张晓鲁

《中国工程科学》 2014年 第16卷 第10期   页码 47-51

摘要：

近年来雾霾天气的频繁出现使得细颗粒物（PM2.5）成为了公众关注的热点，PM2.5的控制也已增加到2012 年发布的《环境空气质量标准》中，而目前我国现有的烟气污染物控制技术难以脱除PM2.5，因此，为深度脱除PM2.5、SO2、SO3以及重金属等烟气污染物，开发燃煤电站烟气污染物深度脱除技术（深度脱除技术）成为亟待解决的问题。本文系统分析了开发适用于我国燃煤电站的深度脱除技术的必要性以及存在的问题，重点分析研究了PM2.5脱除技术、全负荷下超超临界锅炉的低NOx排放以及SCR工作温度的适应性。最后，以某电厂2×660 MW超临界机组为例，介绍了烟气污染物深度脱除系统方案，以此为基础，分析提出了1 000 MW超超临界机组烟气污染物深度脱除的技术路线。

关键词： 燃煤机组     烟气污染物     深度脱除     PM2.5脱除    

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Research progress in removal of trace carbon dioxide from closed spaces

ZHANG Yatao, FAN Lihai, ZHANG Lin, CHEN Huanlin

《化学科学与工程前沿（英文）》 2007年 第1卷 第3期   页码 310-316 doi: 10.1007/s11705-007-0057-x

摘要： In this paper, the removal of trace carbon dioxide from closed spaces through membrane process and biotransformation are introduced in detail. These methods include the microalgae photobioreactor, membrane microalgae photobioreactor, supported liquid membrane, membrane gas-liquid contactor, hydrogel membrane, and enzyme membrane bioreactor. The advantages and disadvantages of these methods are compared. It is found that higher CO removal efficiency can be obtained in biotransformation and membrane process. However, a large volume and high energy consumption are needed in biotransformation, while the low permeability and stability must be solved in the membrane process.

关键词： removal efficiency     consumption     removal     CO removal     membrane microalgae    

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Digital image correlation-based structural state detection through deep learning

《结构与土木工程前沿（英文）》 2022年 第16卷 第1期   页码 45-56 doi: 10.1007/s11709-021-0777-x

摘要： This paper presents a new approach for automatical classification of structural state through deep learning. In this work, a Convolutional Neural Network (CNN) was designed to fuse both the feature extraction and classification blocks into an intelligent and compact learning system and detect the structural state of a steel frame; the input was a series of vibration signals, and the output was a structural state. The digital image correlation (DIC) technology was utilized to collect vibration information of an actual steel frame, and subsequently, the raw signals, without further pre-processing, were directly utilized as the CNN samples. The results show that CNN can achieve 99% classification accuracy for the research model. Besides, compared with the backpropagation neural network (BPNN), the CNN had an accuracy similar to that of the BPNN, but it only consumes 19% of the training time. The outputs of the convolution and pooling layers were visually displayed and discussed as well. It is demonstrated that: 1) the CNN can extract the structural state information from the vibration signals and classify them; 2) the detection and computational performance of the CNN for the incomplete data are better than that of the BPNN; 3) the CNN has better anti-noise ability.

关键词： structural state detection     deep learning     digital image correlation     vibration signal     steel frame    

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Development and deep-sea exploration of the Haidou-1

《工程管理前沿（英文）》   页码 546-549 doi: 10.1007/s42524-023-0260-6

摘要： Development and deep-sea exploration of the Haidou-1

关键词： hadal zone     autonomous and remotely-operated vehicle     integrated exploration operation     deep dive exceeding 10000 meters    

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Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

《结构与土木工程前沿（英文）》   页码 994-1010 doi: 10.1007/s11709-023-0942-5

摘要： The moving trajectory of the pipe-jacking machine (PJM), which primarily determines the end quality of jacked tunnels, must be controlled strictly during the entire jacking process. Developing prediction models to support drivers in performing rectifications in advance can effectively avoid considerable trajectory deviations from the designed jacking axis. Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predict the moving trajectory of the PJM. In this framework, operational data are first extracted from a data acquisition system; subsequently, they are preprocessed and used to establish GRU-based multivariate multistep-ahead direct prediction models. To verify the performance of the proposed framework, a case study of a large pipe-jacking project in Shanghai and comparisons with other conventional models (i.e., long short-term memory (LSTM) network and recurrent neural network (RNN)) are conducted. In addition, the effects of the activation function and input time-step length on the prediction performance of the proposed framework are investigated and discussed. The results show that the proposed framework can dynamically and precisely predict the PJM moving trajectory during the pipe-jacking process, with a minimum mean absolute error and root mean squared error (RMSE) of 0.1904 and 0.5011 mm, respectively. The RMSE of the GRU-based models is lower than those of the LSTM- and RNN-based models by 21.46% and 46.40% at the maximum, respectively. The proposed framework is expected to provide an effective decision support for moving trajectory control and serve as a foundation for the application of deep learning in the automatic control of pipe jacking.

关键词： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年 第17卷 第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

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Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

《信息与电子工程前沿（英文）》 2015年 第16卷 第11期   页码 930-939 doi: 10.1631/FITEE.1500125

摘要： Head pose estimation has been considered an important and challenging task in computer vision. In this paper we propose a novel method to estimate head pose based on a deep convolutional neural network (DCNN) for 2D face images. We design an effective and simple method to roughly crop the face from the input image, maintaining the individual-relative facial features ratio. The method can be used in various poses. Then two convolutional neural networks are set up to train the head pose classifier and then compared with each other. The simpler one has six layers. It performs well on seven yaw poses but is somewhat unsatisfactory when mixed in two pitch poses. The other has eight layers and more pixels in input layers. It has better performance on more poses and more training samples. Before training the network, two reasonable strategies including shift and zoom are executed to prepare training samples. Finally, feature extraction filters are optimized together with the weight of the classification component through training, to minimize the classification error. Our method has been evaluated on the CAS-PEAL-R1, CMU PIE, and CUBIC FacePix databases. It has better performance than state-of-the-art methods for head pose estimation.

关键词： Head pose estimation     Deep convolutional neural network     Multiclass classification    

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

《医学前沿（英文）》 2022年 第16卷 第3期   页码 496-506 doi: 10.1007/s11684-021-0828-7

摘要： The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients’ physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost     deep neural network     healthcare     risk prediction    

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Stability analysis on Tingzikou gravity dam along deep-seated weak planes during earthquake

Weiping HE, Yunlong HE

《结构与土木工程前沿（英文）》 2012年 第6卷 第1期   页码 69-75 doi: 10.1007/s11709-012-0146-x

摘要： The stability of a gravity dam against sliding along deep-seated weak planes is a universal and important problem encountered in the construction of dams. There is no recommended method for stability analysis of the dam on deep-seated weak planes under earthquake condition in Chinese design codes. Taking Tingzikou dam as an example, the research in this paper is focused on searching a proper way to evaluate the seismic safety of the dam against sliding along deep-seated weak planes and the probable failure modes of dam on deep-seated weak planes during earthquake. It is concluded that there are two probable failure modes of the dam along the main weak geological planes in the foundation. In the first mode, the concrete tooth under the dam will be cut and then the dam together with part foundation will slide along the muddy layer; in the second mode, the dam together with part foundation will slide along the path consist of the weak rock layer under the tooth and the muddy layer downstream the tooth. While there is no geological structure planes to form the second slip surface, the intersection of the main and the second slip surface is 40 to 80 m downstream from dam toe, and the angle between the second slip surface and the horizontal plane probably be 25 to 45 degrees.

关键词： gravity dam     deep-seated weak planes     stability against sliding     earthquake    

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Removal of SO

Xiaolei LI, Chunying ZHU, Youguang MA

《化学科学与工程前沿（英文）》 2013年 第7卷 第2期   页码 185-191 doi: 10.1007/s11705-013-1326-5

摘要： In this work, the removal of SO from gas mixture with air and SO by ammonium bicarbonate aqueous solution as absorbent was investigated experimentally in a bubble column reactor. The effects of the concentration of ammonium bicarbonate, the SO inlet concentration of gas phase and the gas flow rate on the removal rate of SO were studied. The results showed that the higher the SO inlet concentration and the gas flow rate, the shorter the lasting time of SO completely removed in gas outlet, and then the faster the decrease in the removal rate of SO . The lasting time of SO completely removed in gas outlet increased with increasing ammonium bicarbonate concentration. During the process of SO absorption, there was a critical pH of solution. When the solution pH was less than the critical pH, it would sharply fall, resulting in a rapid decrease of the SO removal rate. A theoretical model for predicting the SO removal rate has been developed by taking the chemical enhancement and the sulfite concentration in the liquid phase into account simultaneously.

关键词： SO2 removal     bubble column reactor     removal rate     ammonium bicarbonate     absorbent    

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Scrap Iron Filings assisted nitrate and phosphate removal in low C/N waters using mixed microbial culture

《环境科学与工程前沿（英文）》 2021年 第15卷 第4期 doi: 10.1007/s11783-020-1358-2

摘要：

• Microbes enhance denitrification under varying DO concentrations and SIF dosages.

关键词： Scrap iron filing     Nitrate removal     Phosphate removal     Mixed culture denitrification     Zero valent iron    

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Mercury removal from flue gas using nitrate as an electron acceptor in a membrane biofilm reactor

《环境科学与工程前沿（英文）》 2022年 第16卷 第2期 doi: 10.1007/s11783-021-1454-y

摘要：

Membrane bioreactor achieved mercury removal using nitrate as an electron acceptor.

关键词： Mercury removal     Oxygen     Ferrous sulfide     Transformation of mercury     Microbial community    

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Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

《医学前沿（英文）》 2020年 第14卷 第4期   页码 450-469 doi: 10.1007/s11684-019-0726-4

摘要： As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

关键词： deep learning     neural networks     pulmonary medical image     survey    

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标题 作者 时间 类型 操作

Analysis of Flue Gas Pollutants Deep-removal Technology in Coal-fired Power Plants

Xiao-lu Zhang

期刊论文

The R&D of Flue Gas Pollutants Deep-Removal Technology for Coal-fired Power Plants

Xiao-lu Zhang

期刊论文

燃煤电站烟气污染物深度脱除技术的分析

张晓鲁

期刊论文

Research progress in removal of trace carbon dioxide from closed spaces

ZHANG Yatao, FAN Lihai, ZHANG Lin, CHEN Huanlin

期刊论文

Digital image correlation-based structural state detection through deep learning

期刊论文

Development and deep-sea exploration of the Haidou-1

期刊论文

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

期刊论文

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

期刊论文

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

期刊论文

Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

期刊论文

Stability analysis on Tingzikou gravity dam along deep-seated weak planes during earthquake

Weiping HE, Yunlong HE

期刊论文

Removal of SO

Xiaolei LI, Chunying ZHU, Youguang MA

期刊论文

Scrap Iron Filings assisted nitrate and phosphate removal in low C/N waters using mixed microbial culture

期刊论文

Mercury removal from flue gas using nitrate as an electron acceptor in a membrane biofilm reactor

期刊论文

Survey on deep learning for pulmonary medical imaging

Jiechao Ma, Yang Song, Xi Tian, Yiting Hua, Rongguo Zhang, Jianlin Wu

期刊论文