Propane Dehydrogenation on PtSnNa/AlSBA-15 Catalyst:Influence of Tin as a Promoter

PtSnNa/AlSBA-15 catalysts with different amounts of Sn were prepared for propane dehydrogenation.The catalysts were characterized by XRF,BET,H2 chemisorption,NH3-TPD,H2-TPR,and TPO techniques.Test results indicated that the presence of tin not only modified the acid function and the interfacial character between metal and support,but also reduced the coke deposition effectively.Among these catalysts investigated thereby,the PtSn(0.7%)Na/AlSBA-15 catalyst had the best catalytic performance in terms of propane conversion and stability.With the continuous addition of Sn,more amounts of Sn0 species appeared,which was unfavorable to the reaction.The PtSn(0.7%)Na/AlSBA-15 catalyst was parametrically characterized in order to obtain necessary information to integrate the process operating conditions.A weight hourly space velocity of 3 h-1,a reaction temperature of 610 ℃ and a H2/C3H8 molar ratio of 0.25 were found to be optimum conditions for achieving a higher dehydrogenation activity of the catalyst.

Adaptive Optimal Control of a Heavy Lathe Operation

To create control laws of the cutting process on the heavy lathe, the temperature-force model of optimization of cutting conditions for turning was selected. The models to manage the process of cutting on heavy lathe in real time were created. It was found that the optimization of the cutting process must be carried out according to the criteria： productivity, cost and tool life. The hardware structure of the adaptive control system for heavy lathe was developed and its dynamic performance was investigated. The system provides function of the cutting speed of adaptive control and the possibility of compensation of linear, nonlinear and temperature-related inaccuracies. Research results were implemented in the prototype of adaptive control system for heavy lathe and the integral complex of optimal control of an adaptive technological system.

Predictive control of a direct internal reforming SOFC using a self recurrent wavelet network model

In this paper,an application of a nonlinear predictive controller based on a self recurrent wavelet network (SRWN) model for a direct internal reforming solid oxide fuel cell (DIR-SOFC) is presented. As operating temperature and fuel utilization are two important parameters,the SOFC is identified using an SRWN with inlet fuel flow rate,inlet air flow rate and current as inputs,and temperature and fuel utilization as outputs. To improve the operating performance of the DIR-SOFC and guarantee proper operating conditions,the nonlinear predictive control is implemented using the off-line trained and on-line modified SRWN model,to manipulate the inlet flow rates to keep the temperature and the fuel utilization at desired levels. Simulation results show satisfactory predictive accuracy of the SRWN model,and demonstrate the excellence of the SRWN-based predictive controller for the DIR-SOFC.