Overall kinetic studies on the oxidative coupling of methane,OCM,have been conducted in a tubular fixed bed reactor,using perovskite titanate as the reaction catalyst.The appropriate operating conditions were found to...Overall kinetic studies on the oxidative coupling of methane,OCM,have been conducted in a tubular fixed bed reactor,using perovskite titanate as the reaction catalyst.The appropriate operating conditions were found to be：temperature 750-775 ℃,total feed flow rate of 160 ml/min,CH4 /O2 ratio of 2 and GHSV of 100·min-1 .Under these conditions,C 2 yield of 28% was achieved.Correlations of the kinetic data have been performed with lumped rate equations for C2 and COx formation as functions of temperature,O2 and CH4 partial pressures.Six models have been selected among the common lumped kinetic models.The selected models have been regressed with the experimental data which were obtained from the Catatest system by genetic algorithm in order to obtain optimized parameters.The kinetic coefficients in the overall reactions were optimized by different numerical optimization methods such as：the Levenberg-Marquardt and genetic algorithms and the results were compared with one another.It has been found that the Santamaria model is in good agreement with the experimental data.The Arrhenius parameters of this model have been obtained by linear regression.It should be noted that the Marquardt algorithm is sensitive to the first guesses and there is possibility to trap in the relative minimum.展开更多
In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marqu...In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.展开更多
Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural netw...Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.展开更多
Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is ...Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is limited because of the low convergent speed of the algorithm. This paper presents a new algorithm incorporating the Marquardt algorithm into the BP algorithm for training feedforward BP neural networks. The new algorithm was tested with several case studies and used to model the Reid vapor pressure (RVP) of stabilizer gasoline. The new algorithm has faster convergence and is much more efficient than the GDR algorithm.展开更多
基金supported by Iran Polymer and Petrochemical Institute (IPPI)
文摘Overall kinetic studies on the oxidative coupling of methane,OCM,have been conducted in a tubular fixed bed reactor,using perovskite titanate as the reaction catalyst.The appropriate operating conditions were found to be：temperature 750-775 ℃,total feed flow rate of 160 ml/min,CH4 /O2 ratio of 2 and GHSV of 100·min-1 .Under these conditions,C 2 yield of 28% was achieved.Correlations of the kinetic data have been performed with lumped rate equations for C2 and COx formation as functions of temperature,O2 and CH4 partial pressures.Six models have been selected among the common lumped kinetic models.The selected models have been regressed with the experimental data which were obtained from the Catatest system by genetic algorithm in order to obtain optimized parameters.The kinetic coefficients in the overall reactions were optimized by different numerical optimization methods such as：the Levenberg-Marquardt and genetic algorithms and the results were compared with one another.It has been found that the Santamaria model is in good agreement with the experimental data.The Arrhenius parameters of this model have been obtained by linear regression.It should be noted that the Marquardt algorithm is sensitive to the first guesses and there is possibility to trap in the relative minimum.
基金This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 61502240,61502096,61304205,61773219in part,by the Natural Science Foundation of Jiangsu Province under grant number BK20191401+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund.
文摘In order to solve the problem of high computing cost and low simulation accuracy caused by discontinuity of incision in traditional meshless model,this paper proposes a soft tissue deformation model based on the Marquardt algorithm and enrichment function.The model is based on the element-free Galerkin method,in which Kelvin viscoelastic model and adjustment function are integrated.Marquardt algorithm is applied to fit the relation between force and displacement caused by surface deformation,and the enrichment function is applied to deal with the discontinuity in the meshless method.To verify the validity of the model,the Sensable Phantom Omni force tactile interactive device is used to simulate the deformations of stomach and heart.Experimental results show that the proposed model improves the real-time performance and accuracy of soft tissue deformation simulation,which provides a new perspective for the application of the meshless method in virtual surgery.
文摘Objective To investigate the prediction effect of neural networks for seismic response of structure under the Levenberg Marquardt(LM) algorithm. Results Based on identification and prediction ability of neural networks for nonlinear systems, and combined with LM algorithm, a multi layer forward networks is adopted to predict the seismic responses of structure. The networks is trained in batch by the shaking table test data of three floor reinforced concrete structure firstly, then the seismic responses of structure are predicted under the unused excitation data, and the predict responses are compared with the experiment responses. The error curves between the prediction and the experimental results show the efficiency of the method. Conclusion LM algorithm has very good convergence rate, and the neural networks can predict the seismic response of the structure well.
文摘Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is limited because of the low convergent speed of the algorithm. This paper presents a new algorithm incorporating the Marquardt algorithm into the BP algorithm for training feedforward BP neural networks. The new algorithm was tested with several case studies and used to model the Reid vapor pressure (RVP) of stabilizer gasoline. The new algorithm has faster convergence and is much more efficient than the GDR algorithm.
