Hydrogenated microcrystalline silicon （~c-Si：H） films with a high deposition rate of 1.2nm/s were prepared by hot-wire chemical vapor deposition （HWCVD）. The growth-front roughening processes of the μc-Si..H fil...Hydrogenated microcrystalline silicon （~c-Si：H） films with a high deposition rate of 1.2nm/s were prepared by hot-wire chemical vapor deposition （HWCVD）. The growth-front roughening processes of the μc-Si..H films were investi- gated by atomic force microscopy. According to the scaling theory, the growth exponent β≈0.67, the roughness exponent α≈0.80,and the dynamic exponent 1/z = 0.40 are obtained. These scaling exponents cannot be explained well by the known growth models. An attempt at Monte Carlo simulation has been made to describe the growth process of μc-Si： H film using a particle reemission model where the incident flux distribution,the type and concentration of growth radical, and sticking,reemission,shadowing mechanisms all contributed to the growing morphology.展开更多
Although human beings have come to understand and utilize coal for a very long history, no theoretical breakthrough in the study of coal structure has been made, which still needs continuous efforts of coal chemical w...Although human beings have come to understand and utilize coal for a very long history, no theoretical breakthrough in the study of coal structure has been made, which still needs continuous efforts of coal chemical workers. Based on the viewpoint of ‘vague/clear', the species classification and accurate analysis on coal were conducted by using the natural clustering all-component separation method. A more systematic and detailed coal embedded structure model theory which is suitable for coal of all ranks was developed from the previous one and a more complete theoretical system about the component and structure of coal was constructed. The whole establishment process of the theory was summarized and some of the main support data and analysis test results, including TEM, AFM, FTIR, GC/MS, MALDI/TOF/MS, DART/MSD, fractal analysis and so on were provided. The coal embedded structure theory fully considers both the identity and the particularity of all-rank coal, reflects the coal component and structure in the full range of coal rank, solves the systematic cognitive problem of coal component and structure on macro and micro level, and provides a valuable and meaningful theoretical approach for the coal processing and conversion technology.展开更多
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been success...Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.展开更多
Hazardous gas detection systems play an important role in preventing catastrophic gas-related accidents in process industries. Even though effective detection technology currently exists for hazardous gas releases and...Hazardous gas detection systems play an important role in preventing catastrophic gas-related accidents in process industries. Even though effective detection technology currently exists for hazardous gas releases and a majority of process installations have a large number of sensitive detectors in place, the actual operating performance of gas detection systems still does not meet the expected requirements. In this paper, a riskbased methodology is proposed to optimize the placement of hazardous gas detectors. The methodology includes three main steps, namely, the establishment of representative leak scenarios, computational fluid dynamics(CFD)-based gas dispersion modeling, and the establishment of an optimized solution. Based on the combination of gas leak probability and joint distribution probability of wind velocity and wind direction, a quantitative filtering approach is presented to select representative leak scenarios from all potential scenarios. The commercial code ANSYS-FLUENT is used to estimate the consequence of hazardous gas dispersions under various leak and environmental conditions. A stochastic mixed-integer linear programming formulation with the objective of minimizing the total leak risk across all representative leak scenarios is proposed, and the greedy dropping heuristic algorithm(GDHA) is used to solve the optimization model. Finally, a practical application of the methodology is performed to validate its effectiveness for the optimal design of a gas detector system in a high-sulfur natural gas purification plant in Chongqing, China. The results show that an appropriate number of gas detectors with optimal cost-effectiveness can be obtained, and the total leak risk across all potential scenarios can be substantially reduced. This methodology provides an effective approach to guide the optimal placement of pointtype gas detection systems involved with either single or mixed gas releases.展开更多
In order to apply physical simulation results to natural gas hydrate reservoir parameters to provide a theoretical framework for the design of a development plan,an analytical equation method was used to obtain the si...In order to apply physical simulation results to natural gas hydrate reservoir parameters to provide a theoretical framework for the design of a development plan,an analytical equation method was used to obtain the similarity criteria of natural gas hydrate reservoir development by physical simulation,based on a mathematical model of natural gas hydrate development.Given the approach of numerical simulation,a sensitivity analysis for all parameters was carried out,which specifically demonstrated that initial temperature is the most important parameter.Parameters of thermal conductivity coefficients are not necessary for conducting the NGH dissociation process,which will fundamentally simplify the design and establishment of the model.The analysis provides a sound theoretical basis and design principles for particular similarity.展开更多
The article deals with the methodology of pseudorandom data analysis. As a mathematical tool for carrying out the research the extreme value theory was used that creates one of the directions in mathematical statistic...The article deals with the methodology of pseudorandom data analysis. As a mathematical tool for carrying out the research the extreme value theory was used that creates one of the directions in mathematical statistics, and is related to investigating the extreme deviations from the median values in probability distributions. Also, the methods for estimating unknown parameters and algorithm of random-number generation are discussed. The models of treatment the extreme values are constructed which are based on machine generated sample and approach is proposed for their future application for constructing forecasting models.展开更多
This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the H...This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.展开更多
文摘Hydrogenated microcrystalline silicon （~c-Si：H） films with a high deposition rate of 1.2nm/s were prepared by hot-wire chemical vapor deposition （HWCVD）. The growth-front roughening processes of the μc-Si..H films were investi- gated by atomic force microscopy. According to the scaling theory, the growth exponent β≈0.67, the roughness exponent α≈0.80,and the dynamic exponent 1/z = 0.40 are obtained. These scaling exponents cannot be explained well by the known growth models. An attempt at Monte Carlo simulation has been made to describe the growth process of μc-Si： H film using a particle reemission model where the incident flux distribution,the type and concentration of growth radical, and sticking,reemission,shadowing mechanisms all contributed to the growing morphology.
基金financial provided by the National Natural Science Foundation of China (Nos. 50474066, 50874108, 51274201, and 51674260)the Coal Joint Fund from National Natural Science Foundation of China and Shenhua Group Corporation Limited (No. U1361116)the National Basic Research Program of China (No. 2012CB214900)
文摘Although human beings have come to understand and utilize coal for a very long history, no theoretical breakthrough in the study of coal structure has been made, which still needs continuous efforts of coal chemical workers. Based on the viewpoint of ‘vague/clear', the species classification and accurate analysis on coal were conducted by using the natural clustering all-component separation method. A more systematic and detailed coal embedded structure model theory which is suitable for coal of all ranks was developed from the previous one and a more complete theoretical system about the component and structure of coal was constructed. The whole establishment process of the theory was summarized and some of the main support data and analysis test results, including TEM, AFM, FTIR, GC/MS, MALDI/TOF/MS, DART/MSD, fractal analysis and so on were provided. The coal embedded structure theory fully considers both the identity and the particularity of all-rank coal, reflects the coal component and structure in the full range of coal rank, solves the systematic cognitive problem of coal component and structure on macro and micro level, and provides a valuable and meaningful theoretical approach for the coal processing and conversion technology.
文摘Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.
基金Supported by the National Natural Science Foundation of China(51474184)the Natural Science Foundation of the State Administration of Work Safety in China(2012-387,Sichuan-0021-2016AQ)
文摘Hazardous gas detection systems play an important role in preventing catastrophic gas-related accidents in process industries. Even though effective detection technology currently exists for hazardous gas releases and a majority of process installations have a large number of sensitive detectors in place, the actual operating performance of gas detection systems still does not meet the expected requirements. In this paper, a riskbased methodology is proposed to optimize the placement of hazardous gas detectors. The methodology includes three main steps, namely, the establishment of representative leak scenarios, computational fluid dynamics(CFD)-based gas dispersion modeling, and the establishment of an optimized solution. Based on the combination of gas leak probability and joint distribution probability of wind velocity and wind direction, a quantitative filtering approach is presented to select representative leak scenarios from all potential scenarios. The commercial code ANSYS-FLUENT is used to estimate the consequence of hazardous gas dispersions under various leak and environmental conditions. A stochastic mixed-integer linear programming formulation with the objective of minimizing the total leak risk across all representative leak scenarios is proposed, and the greedy dropping heuristic algorithm(GDHA) is used to solve the optimization model. Finally, a practical application of the methodology is performed to validate its effectiveness for the optimal design of a gas detector system in a high-sulfur natural gas purification plant in Chongqing, China. The results show that an appropriate number of gas detectors with optimal cost-effectiveness can be obtained, and the total leak risk across all potential scenarios can be substantially reduced. This methodology provides an effective approach to guide the optimal placement of pointtype gas detection systems involved with either single or mixed gas releases.
基金supported by the China Petroleum and Chemical Corporation (No.P06070)the National Natural Science Foundation of China (No.50404003)
文摘In order to apply physical simulation results to natural gas hydrate reservoir parameters to provide a theoretical framework for the design of a development plan,an analytical equation method was used to obtain the similarity criteria of natural gas hydrate reservoir development by physical simulation,based on a mathematical model of natural gas hydrate development.Given the approach of numerical simulation,a sensitivity analysis for all parameters was carried out,which specifically demonstrated that initial temperature is the most important parameter.Parameters of thermal conductivity coefficients are not necessary for conducting the NGH dissociation process,which will fundamentally simplify the design and establishment of the model.The analysis provides a sound theoretical basis and design principles for particular similarity.
文摘The article deals with the methodology of pseudorandom data analysis. As a mathematical tool for carrying out the research the extreme value theory was used that creates one of the directions in mathematical statistics, and is related to investigating the extreme deviations from the median values in probability distributions. Also, the methods for estimating unknown parameters and algorithm of random-number generation are discussed. The models of treatment the extreme values are constructed which are based on machine generated sample and approach is proposed for their future application for constructing forecasting models.
基金supported by the National Natural Science Foundation of China under Grant Nos.11671349 and 11601195the Scientific Research Innovation Team of Yunnan Province under Grant No.2015HC028the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20160289
文摘This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.
