Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neur...Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.展开更多
Geometrical optimization and electrostatic potential calculations have been performed for a series of halogenated hydrocarbons at the HF/Gen-6d level. A number of electrostatic potentials and the statistically based s...Geometrical optimization and electrostatic potential calculations have been performed for a series of halogenated hydrocarbons at the HF/Gen-6d level. A number of electrostatic potentials and the statistically based structural descriptors derived from these electrostatic potentials have been obtained. Multiple linear regression analysis and artificial neural network are employed simultaneously in this paper. The result shows that the parameters derived from electrostatic 2 potentials σtot^2, V s and ∑ Vs^＋, together with the molecular volume （Vine） can be used to express the quantitative structure-infinite dilution activity coefficients （γ^∞） relationship of halogenated hydrocarbons in water. The result also demonstrates that the model obtained by using BFGS quasiNewton neural network method has much better predictive capability than that from multiple linear regression. The goodness of the model has been validated through exploring the predictive power for the external test set. The model obtained via neural network may be applied to predict γ^∞ of other halogenated hydrocarbons not present in the data set.展开更多
Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydro...Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.展开更多
The modeling of hydrocarbon selectivity and CO conversion of the Fischer-Tropsch synthesis over Fe-Ni/Al2O3 catalyst by using coupled artificial neural networks (ANN) and design of experiment (DOE) approaches were inv...The modeling of hydrocarbon selectivity and CO conversion of the Fischer-Tropsch synthesis over Fe-Ni/Al2O3 catalyst by using coupled artificial neural networks (ANN) and design of experiment (DOE) approaches were investigated. The variable parameters for modeling consisted of the pressure range between 2 and 10 bar and the temperature range of 523-573 K. After training of data by ANN and determination of DOE points by central composite design (CCD), the results were compiled together for producing simulated data used in the response surface method (RSM). The RSM was used as an applied mathematics model to dem on strate the CO conversi on and selectivity of hydrocarbons depende nee on the CO hydrogenation conditions. The results indicated that CO conversion and Cg selectivity increased with rising both temperature and pressure. The methane selectivity showed upward trend as the temperature in creased. It also in creased by decreasing pressure. Finally, the optimization of the catalytic process was carried out and conditions with maximum desired product were obtained. A comparison of experimental values and RSM values show that the RSM equations are able to predict the behavior of experimental data.展开更多
This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network(ANN)analysis on the basis of the back ...This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network(ANN)analysis on the basis of the back propagation algorithm.The derivation of the proposed model relies on a large number of experimental data obtained from the tests performed with the platform of supercritical flow and heat transfer.Different topology structures,training algo-rithms and transfer functions are employed in model optimization.The performance of the optimal ANN model is evaluated with the mean relative error,the determination coefficient,the number of iterations and the convergence time.It is demonstrated that the model has high prediction accuracy when the tansig transfer function,the Levenberg-Marquardt training algo-rithm and the three-layer topology of 4-9-1 are selected.In addition,the accuracy of the ANN model is observed to be the highest compared with other classic empirical correlations.Mean relative error values of 4.4%and 3.4%have been achieved for modeling of the Nusselt number and friction factor respectively over the whole experimental data set.The ANN model estab-lished in this paper is shown to have an excellent performance in learning ability and general-ization for characterizing the flow and heat transfer law of hydrocarbon fuel,which can provide an alternative approach for the future study of supercritical fluid characteristics and the associ-ated engineering applications.展开更多
介绍了美国The Leading Edge杂志第21卷第10期地震属性专辑中的主要地质成果,包括:用综合地震反演求泊松比,用参数谱估计求吸收系数,用密度数据体直接找油气的成果,寻找河道砂、高孔隙带、高裂缝带等含油气特殊圈闭的10个成功实例,预测...介绍了美国The Leading Edge杂志第21卷第10期地震属性专辑中的主要地质成果,包括:用综合地震反演求泊松比,用参数谱估计求吸收系数,用密度数据体直接找油气的成果,寻找河道砂、高孔隙带、高裂缝带等含油气特殊圈闭的10个成功实例,预测超高压带以预防钻井风险成功的实例和两个油气预测失败的例子。着重指出,为了使地震属性技术和人工智能技术在直接找油气方面富有成果,地质家和油气工程专家应持有的态度,以及要使属性技术上升为属性科学所要继续研究的问题。展开更多
文摘Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity, in the current investigation a feed-forward artificial neural network(ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257–338 K and atmospheric pressure. For this purpose, 956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature.During the modeling stage, to discriminate different substances, critical temperature(Tc), critical pressure(Pc)and acentric factor(ω) are utilized as the network inputs besides the temperature. During the examination, effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides, statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of the proposed method. Respect to the obtained results, it can be concluded that the proposed neural network consisted of three layers namely, input, hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally, the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent(AARD), mean square error(MSE), and correlation coefficient(R^2) of lower than 0.2%, 1.05 × 10^(-7) and 0.9994, respectively.
文摘Geometrical optimization and electrostatic potential calculations have been performed for a series of halogenated hydrocarbons at the HF/Gen-6d level. A number of electrostatic potentials and the statistically based structural descriptors derived from these electrostatic potentials have been obtained. Multiple linear regression analysis and artificial neural network are employed simultaneously in this paper. The result shows that the parameters derived from electrostatic 2 potentials σtot^2, V s and ∑ Vs^＋, together with the molecular volume （Vine） can be used to express the quantitative structure-infinite dilution activity coefficients （γ^∞） relationship of halogenated hydrocarbons in water. The result also demonstrates that the model obtained by using BFGS quasiNewton neural network method has much better predictive capability than that from multiple linear regression. The goodness of the model has been validated through exploring the predictive power for the external test set. The model obtained via neural network may be applied to predict γ^∞ of other halogenated hydrocarbons not present in the data set.
基金supported by the Natural Science Foundation of Fujian Province (D0710019)the Natural Science Foundation of Overseas Chinese Affairs Office of the State Council (06QZR09)
文摘Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.
文摘The modeling of hydrocarbon selectivity and CO conversion of the Fischer-Tropsch synthesis over Fe-Ni/Al2O3 catalyst by using coupled artificial neural networks (ANN) and design of experiment (DOE) approaches were investigated. The variable parameters for modeling consisted of the pressure range between 2 and 10 bar and the temperature range of 523-573 K. After training of data by ANN and determination of DOE points by central composite design (CCD), the results were compiled together for producing simulated data used in the response surface method (RSM). The RSM was used as an applied mathematics model to dem on strate the CO conversi on and selectivity of hydrocarbons depende nee on the CO hydrogenation conditions. The results indicated that CO conversion and Cg selectivity increased with rising both temperature and pressure. The methane selectivity showed upward trend as the temperature in creased. It also in creased by decreasing pressure. Finally, the optimization of the catalytic process was carried out and conditions with maximum desired product were obtained. A comparison of experimental values and RSM values show that the RSM equations are able to predict the behavior of experimental data.
基金The authors gratefully acknowledge funding support from the Program for National Natural Science Foundation of China(51876005 and 52122604).
文摘This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network(ANN)analysis on the basis of the back propagation algorithm.The derivation of the proposed model relies on a large number of experimental data obtained from the tests performed with the platform of supercritical flow and heat transfer.Different topology structures,training algo-rithms and transfer functions are employed in model optimization.The performance of the optimal ANN model is evaluated with the mean relative error,the determination coefficient,the number of iterations and the convergence time.It is demonstrated that the model has high prediction accuracy when the tansig transfer function,the Levenberg-Marquardt training algo-rithm and the three-layer topology of 4-9-1 are selected.In addition,the accuracy of the ANN model is observed to be the highest compared with other classic empirical correlations.Mean relative error values of 4.4%and 3.4%have been achieved for modeling of the Nusselt number and friction factor respectively over the whole experimental data set.The ANN model estab-lished in this paper is shown to have an excellent performance in learning ability and general-ization for characterizing the flow and heat transfer law of hydrocarbon fuel,which can provide an alternative approach for the future study of supercritical fluid characteristics and the associ-ated engineering applications.
文摘介绍了美国The Leading Edge杂志第21卷第10期地震属性专辑中的主要地质成果,包括:用综合地震反演求泊松比,用参数谱估计求吸收系数,用密度数据体直接找油气的成果,寻找河道砂、高孔隙带、高裂缝带等含油气特殊圈闭的10个成功实例,预测超高压带以预防钻井风险成功的实例和两个油气预测失败的例子。着重指出,为了使地震属性技术和人工智能技术在直接找油气方面富有成果,地质家和油气工程专家应持有的态度,以及要使属性技术上升为属性科学所要继续研究的问题。
