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Simultaneous removal of NO and chlorobenzene on VO/TiO granular catalyst: Kinetic study and performanceprediction

《环境科学与工程前沿（英文）》 2021年 第15卷 第4期 doi: 10.1007/s11783-020-1363-5

摘要：

• A V2O5/TiO2 granular catalyst for simultaneous removal of NO and chlorobenzene.

关键词： NOx     Chlorobenzene     Simultaneous removal     Kinetic study     Performance prediction     V2O5/TiO2     Graphical abstract    

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A control scheme with performance prediction for a PV fed water pumping system

Ramesh K GOVINDARAJAN,Pankaj Raghav PARTHASARATHY,Saravana Ilango GANESAN

《能源前沿（英文）》 2014年 第8卷 第4期   页码 480-489 doi: 10.1007/s11708-014-0334-6

摘要： This paper focuses on modeling and performance predetermination of a photovoltaic (PV) system with a boost converter fed permanent magnet direct current (PMDC) motor-centrifugal pump load, taking the converter losses into account. Sizing is done based on the maximum power generated by the PV array at the average irradiation. Hence optimum sizing of the PV array for the given irradiation at the geographical location of interest is obtained using the predetermined values. The analysis presented here involves systems employing maximum power point tracking (MPPT) as they are more efficient than directly coupled systems. However, the voltage and power of the motor might rise above rated values for irradiations greater than the average when employing MPPT, hence a control scheme has been proposed to protect the PMDC motor from being damaged during these conditions. This control scheme appropriately chooses the optimum operating point of the system, ensuring long-term sustained operation. The numerical simulation of the system is performed in Matlab/Simulink and is validated with experimental results obtained from a 180 V, 0.5 hp PMDC motor coupled to a centrifugal pump. The operation of the system with the proposed control scheme is verified by varying the irradiation levels and the relevant results are presented.

关键词： photovoltaic system     boost converter     maximum power point tracking (MPPT)     DC permanent-magnet motor     centrifugal pump    

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Optimization and performance prediction of a new near-zero emission coal utilization system with combined

GUAN Jian, WANG Qinhui, LI Xiaomin, LUO Zhongyang, CEN Kefa

《能源前沿（英文）》 2007年 第1卷 第1期   页码 113-119 doi: 10.1007/s11708-007-0013-y

摘要： In accordance with the new near-zero emission coal utilization system with combined gasification and combustion, which is based on the CO acceptor gasification process, the product gas composition of the gasifier and the combustor was calculated by means of thermodynamic equilibrium calculation software FactSage 5.2. Based on these calculations, the whole system efficiency calculation method that complies with the mass and energy conservation principle was established. To enhance the system efficiency, the system pressure and the gasifier carbon conversion ratio were optimized. The results indicate that the system efficiency increases with increasing pressure and gasifier carbon conversion ratio. After taking into consideration the influence of the pressure and carbon conversion ratio on the performance of the system, the gasifier and the combustor were synthetically studied. The optimum system pressure and carbon conversion ratio were obtained as 2.5 MPa and 0.7, respectively. The system efficiency could reach around 62.1% when operated in these two optimum parameters. If the advanced ion transport membrane (ITM) air separation technology is used, there would be an increase of another 1.3%.

关键词： influence     efficiency calculation     optimum     software FactSage     transport    

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Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

《能源前沿（英文）》 2013年 第7卷 第1期   页码 56-68 doi: 10.1007/s11708-012-0216-8

摘要： This paper presents the complete mathematical model and predicts the performance of switched reluctance generator with time average and small signal models. The complete mathematical model is developed in three stages. First, a switching model is developed based on quasi-linear inductance profile. Next, based on the switching behaviour, a time average model is obtained to measure the difference between the excitation and generation time in each switching cycle. Finally, to track control voltage and current wave shapes, a small signal model is designed. The effectiveness of the complete multilevel model combining electrical machine, power converter, load and control with programming language is demonstrated through simulations. A PI controller is used for controlling the voltage of the generator. The results presented show that the controller exhibits accurate tracking control of load voltage under different operating conditions. This demonstrates that the proposed model is able to perform an accurate control of the generated output voltage even in transient situations. The simulation is performed to choose the control parameters and study the performance of switched reluctance generator prior to its actual implementation. Initial experimental results are presented using NI-Data acquisition card to control the output power according to load requirements.

关键词： generator     reluctance     switching model     small signal model     time average model    

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Prediction of performance, combustion and emission characteristics of diesel-thermal cracked cashew nut

Arunachalam VELMURUGAN,Marimuthu LOGANATHAN,E. James GUNASEKARAN

《能源前沿（英文）》 2016年 第10卷 第1期   页码 114-124 doi: 10.1007/s11708-016-0394-x

摘要： This paper explores the use of artificial neural networks (ANN) to predict performance, combustion and emissions of a single cylinder, four stroke stationary, diesel engine operated by thermal cracked cashew nut shell liquid (TC-CNSL) as the biodiesel blended with diesel. The tests were performed at three different injection timings (21°, 23°, 25°CA bTDC) by changing the thickness of the advance shim. The ANN was used to predict eight different engine-output responses, namely brake thermal efficiency (BTE), brake specific fuel consumption (BSFC), exhaust gas temperature (EGT), carbon monoxide (CO), oxide of nitrogen (NO ), hydrocarbon (HC), maximum pressure ( ) and heat release rate (HRR). Four pertinent engine operating parameters, i.e., injection timing (IT), injection pressure (IP), blend percentage and pecentage load were used as the input parameters for this modeling work. The ANN results show that there is a good correlation between the ANN predicted values and the experimental values for various engine performances, combustion parameters and exhaust emission characteristics. The mean square error value (MSE) is 0.005621 and the regression value of is 0.99316 for training, 0.98812 for validation, 0.9841 for testing while the overall value is 0.99173. Thus the developed ANN model is fairly powerful for predicting the performance, combustion and exhaust emissions of internal combustion engines.

关键词： cashew nut shell liquid (CNSL)     artificial neural networks (ANN)     thermal cracking     mean square error (MSE)    

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考虑设计参数扰动的芯片多元参数成品率预测算法 Article

Xin LI,Jin SUN,Fu XIAO

《信息与电子工程前沿（英文）》 2016年 第17卷 第12期   页码 1344-1359 doi: 10.1631/FITEE.1601225

摘要： 随着芯片制造工艺的进步，工艺参数、供电电压及片上温度（Process, voltage, and temperature, PVT）等设计参数扰动已成为芯片设计过程的棘手问题，其所产生的性能指标间相关性将导致芯片参数成品率显著下降。但是，当前芯片参数成品率预测算法主要局限于单一性能指标成品率预测或对多个单性能指标成品率进行均衡优化，而不能同时针对多个性能指标约束进行多元参数成品率预测，易造成参数成品率精度缺失。基于以上问题，本文将多个性能指标同时作为约束条件，提出一种芯片多元参数成品率预测方法。该方法首先考虑PVT参数扰动，利用自适应弹性网（Adaptive elastic net, AEN）对芯片性能指标进行建模。然后，基于乘法定理及马尔科夫链蒙特卡罗法，通过求解累积分布函数（Cumulative distribution function, CDF）对单一性能指标的芯片参数成品率进行预测。最后，同时考虑多个芯片性能指标约束，根据Copula方法准确预测芯片多元参数成品率。实验结果表明，本文方法可以在指定性能指标约束下对芯片多元参数成品率进行有效预测，并可为芯片设计人员提供任意性能指标约束下的多元参数成品率预测曲面。

关键词： 成品率预测；参数扰动；多元参数成品率；性能建模；稀疏表示    

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采矿过程中磨料水射流性能通用预测方法

Eugene Averin

《工程（英文）》 2017年 第3卷 第6期   页码 888-891 doi: 10.1016/j.eng.2017.12.004

摘要：
极端采矿条件下的硬岩破碎可采用磨料水射流（AWJ）技术，这种技术能够在不产生粉尘的条件下有效切割难以机械加工的材料。这种技术还可用于爆破、本安和消防安全。就断裂力学而言，每一种可被破坏的材料均可被视为韧性或脆性材料。因此，需要找到一种无论使用AWJ 对何种材料进行切割都能精确预测其效率的方法。该问题可通过能量守恒法加以解决，它显示了材料去除量与AWJ 动能之间的比例。本文介绍了基于能量守恒法的预测方法，并提出如何达到最有效破坏水平的建议，以及关于涉及磨料流量与水流量、靶距和磨料颗粒粒径关系值的合理范围的建议。本文还提供了基于断裂力学的临时结构法确定材料破坏起始阈值条件的参数。

关键词： 磨料水射流     能量守恒法     切割深度     断裂力学     阈值速度     采矿    

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微观移动协议主动切换机制研究

赵阿群

《中国工程科学》 2004年 第6卷 第8期   页码 50-56

摘要：

针对微观移动协议现有切换机制的缺点，提出了主动切换机制，利用移动预测技术，在切换发生前预测移动主机将要切换的下一蜂窝以及切换的时刻，为移动主机预先建立新路径；为了保证主动切换机制的实现，提出了一个适用于主动切换机制并易于实现的移动预测算法；同时还提出了主动切换过程中分组丢失和重复避免方法；通过理论分析和系统仿真的方法对主动切换机制进行了性能评价，结果表明主动切换机制以较小的代价获得了可观的性能改善。

关键词： 微观移动协议     主动切换机制     移动预测     性能评价    

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Spatial prediction of soil contamination based on machine learning: a review

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1

摘要：

● A review of machine learning (ML) for spatial prediction of soil contamination.

关键词： Soil contamination     Machine learning     Prediction     Spatial distribution    

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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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Position-varying surface roughness prediction method considering compensated acceleration in milling

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 855-867 doi: 10.1007/s11465-021-0649-z

摘要： Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.

关键词： surface roughness prediction     compensated acceleration     milling     thin-walled workpiece    

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Improved prediction of pile bending moment and deflection due to adjacent braced excavation

《结构与土木工程前沿（英文）》 doi: 10.1007/s11709-023-0961-2

摘要： Deep excavations in dense urban areas have caused damage to nearby existing structures in numerous past construction cases. Proper assessment is crucial in the initial design stages. This study develops equations to predict the existing pile bending moment and deflection produced by adjacent braced excavations. Influential parameters (i.e., the excavation geometry, diaphragm wall thickness, pile geometry, strength and small-strain stiffness of the soil, and soft clay thickness) were considered and employed in the developed equations. It is practically unfeasible to obtain measurement data; hence, artificial data for the bending moment and deflection of existing piles were produced from well-calibrated numerical analyses of hypothetical cases, using the three-dimensional finite element method. The developed equations were established through a multiple linear regression analysis of the artificial data, using the transformation technique. In addition, the three-dimensional nature of the excavation work was characterized by considering the excavation corner effect, using the plane strain ratio parameter. The estimation results of the developed equations can provide satisfactory pile bending moment and deflection data and are more accurate than those found in previous studies.

关键词： pile responses     excavation     prediction     deflection     bending moments    

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Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

《能源前沿（英文）》 2016年 第10卷 第4期   页码 479-488 doi: 10.1007/s11708-016-0425-7

摘要： In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.

关键词： nuclear power units in service     reliability     reliability prediction     equivalent availability factors    

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Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

《机械工程前沿（英文）》 2010年 第5卷 第2期   页码 171-175 doi: 10.1007/s11465-009-0091-0

摘要： Trend prediction technology is the key technology to achieve condition-based maintenance of mechanical equipment. Large-sized water injection units are key equipment in oilfields. The traditional preventive maintenance is not economical and cannot completely avoid vicious accidents. To ensure the normal operation of units and save maintenance costs, trend prediction technology is studied to achieve condition-based maintenance for water injection units. The main methods of the technology are given, the trend prediction method based on neural network is put forward, and the expert system based on the knowledge is developed. The industrial site verification shows that the proposed trend prediction technology can reflect the operating condition trend change of the water injection units and provide technical means to achieve condition-based predictive maintenance.

关键词： water injection units     condition-based maintenance     trend prediction    

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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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标题 作者 时间 类型 操作

Simultaneous removal of NO and chlorobenzene on VO/TiO granular catalyst: Kinetic study and performanceprediction

期刊论文

A control scheme with performance prediction for a PV fed water pumping system

Ramesh K GOVINDARAJAN,Pankaj Raghav PARTHASARATHY,Saravana Ilango GANESAN

期刊论文

Optimization and performance prediction of a new near-zero emission coal utilization system with combined

GUAN Jian, WANG Qinhui, LI Xiaomin, LUO Zhongyang, CEN Kefa

期刊论文

Performance prediction of switched reluctance generator with time average and small signal models

Jyoti KOUJALAGI, B. UMAMAHESWARI, R. ARUMUGAM

期刊论文

Prediction of performance, combustion and emission characteristics of diesel-thermal cracked cashew nut

Arunachalam VELMURUGAN,Marimuthu LOGANATHAN,E. James GUNASEKARAN

期刊论文

考虑设计参数扰动的芯片多元参数成品率预测算法

Xin LI,Jin SUN,Fu XIAO

期刊论文

采矿过程中磨料水射流性能通用预测方法

Eugene Averin

期刊论文

微观移动协议主动切换机制研究

赵阿群

期刊论文

Spatial prediction of soil contamination based on machine learning: a review

期刊论文

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

期刊论文

Position-varying surface roughness prediction method considering compensated acceleration in milling

期刊论文

Improved prediction of pile bending moment and deflection due to adjacent braced excavation

期刊论文

Reliability prediction and its validation for nuclear power units in service

Jinyuan SHI,Yong WANG

期刊论文

Trend prediction technology of condition maintenance for large water injection units

Xiaoli XU, Sanpeng DENG

期刊论文

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

期刊论文