Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in pet...Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.展开更多
The derivative expressions between activation energy (E) and the temperature at the maximum mass loss rate(Tmax) and between activation energy (E) and exponent (N) were deduced in the light of Arrhenius theory. It was...The derivative expressions between activation energy (E) and the temperature at the maximum mass loss rate(Tmax) and between activation energy (E) and exponent (N) were deduced in the light of Arrhenius theory. It was found that the increase of activation energy results in the decrease of exponent and the increase of Tmax. The kinetic parameters were involved in the analysis of the thermal degradation of several polymers. The degradation kinetics of these polymers well complied with the prediction of the derivative expressions for the polymer degradation with single mechanism dominated.展开更多
Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting hea...Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.展开更多
A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The pa...A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.展开更多
Randomness plays a major role in the interpretation of many interesting traffic flow phenomena,such as hysteresis,capacity drop and spontaneous breakdown. The analysis of the uncertainty and reliability of traffic sys...Randomness plays a major role in the interpretation of many interesting traffic flow phenomena,such as hysteresis,capacity drop and spontaneous breakdown. The analysis of the uncertainty and reliability of traffic systems is directly associated with their random characteristics. Therefore,it is beneficial to understand the distributional properties of traffic variables. This paper focuses on the dependence relation between traffic flow density and traffic speed,which constitute the fundamental diagram (FD). The traditional model of the FD is obtained essentially through curve fitting. We use the copula function as the basic toolkit and provide a novel approach for identifying the distributional patterns associated with the FD. In particular,we construct a rule-of-thumb nonparametric copula function,which in general avoids the mis-specification risk of parametric approaches and is more efficient in practice. By applying our construction to loop detector data on a freeway,we identify the dependence patterns existing in traffic data. We find that similar modes exist among traffic states of low,moderate or high traffic densities. Our findings also suggest that highway traffic speed and traffic flow density as a bivariate distribution is skewed and highly heterogeneous.展开更多
基金Supported by the National Natural Science Foundation of China(U1462206)
文摘Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.
文摘The derivative expressions between activation energy (E) and the temperature at the maximum mass loss rate(Tmax) and between activation energy (E) and exponent (N) were deduced in the light of Arrhenius theory. It was found that the increase of activation energy results in the decrease of exponent and the increase of Tmax. The kinetic parameters were involved in the analysis of the thermal degradation of several polymers. The degradation kinetics of these polymers well complied with the prediction of the derivative expressions for the polymer degradation with single mechanism dominated.
基金Project(50875265) supported by the National Natural Science Foundation of ChinaProject(20080440992) supported by the Postdoctoral Science Foundation of ChinaProject(2009SK3159) supported by the Technology Support Plan of Hunan Province,China
文摘Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.
文摘A grating eddy current displacement sensor(GECDS) can be used in a watertight electronic transducer to realize long range displacement or position measurement with high accuracy in difficult industry conditions.The parameters optimization of the sensor is essential for economic and efficient production.This paper proposes a method to combine an artificial neural network(ANN) and a genetic algorithm(GA) for the sensor parameters optimization.A neural network model is developed to map the complex relationship between design parameters and the nonlinearity error of the GECDS,and then a GA is used in the optimization process to determine the design parameter values,resulting in a desired minimal nonlinearity error of about 0.11%.The calculated nonlinearity error is 0.25%.These results show that the proposed method performs well for the parameters optimization of the GECDS.
基金Project (No. 50809058) supported by the National Natural Science Foundation of China
文摘Randomness plays a major role in the interpretation of many interesting traffic flow phenomena,such as hysteresis,capacity drop and spontaneous breakdown. The analysis of the uncertainty and reliability of traffic systems is directly associated with their random characteristics. Therefore,it is beneficial to understand the distributional properties of traffic variables. This paper focuses on the dependence relation between traffic flow density and traffic speed,which constitute the fundamental diagram (FD). The traditional model of the FD is obtained essentially through curve fitting. We use the copula function as the basic toolkit and provide a novel approach for identifying the distributional patterns associated with the FD. In particular,we construct a rule-of-thumb nonparametric copula function,which in general avoids the mis-specification risk of parametric approaches and is more efficient in practice. By applying our construction to loop detector data on a freeway,we identify the dependence patterns existing in traffic data. We find that similar modes exist among traffic states of low,moderate or high traffic densities. Our findings also suggest that highway traffic speed and traffic flow density as a bivariate distribution is skewed and highly heterogeneous.
