An initial differential mathematical model of the process of catalytic reactions coupled in a membrane reactor is developed.The "donor" reaction refers to one that produces the key substance that may permeat...An initial differential mathematical model of the process of catalytic reactions coupled in a membrane reactor is developed.The "donor" reaction refers to one that produces the key substance that may permeate the membrane and be consumed by the "acceptor" reaction.According to the maximum principle in mathematics,the analysis indicates that,in order to obtain maximum yield of the "acceptor" reaction,there should exist our optimal profile of the packed-bed catalytic activity on the "acceptor" reaction side of the membrane reactor,which is a one-time open-close function at the optimum position,as followsξ *(z)=0 ? (0<z<z r ) ξ max (z r ≤z≤1)where ξ *(z) is optimal function of catalytic activity, ξ max its maximum value, z the position,and z r the optimum position.The function means physically that catalytic activity equals to zero near the inlet and that,at the optimum position,activity should be kept maximum constant value on the "acceptor" reaction side.Finally the effect of membrane permeability on the optimum catalytic activity is also investigated.It is found that,as the membrane permeability increases,the optimum open-close position of optimal catalytic activity profile function will move closer to the inlet of the membrane reactor.展开更多
In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear...In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear problem as the system integrates enzymatic hydrolysis, membrane separation and periodic backflushing together. In this paper an alternative artificial neural network approach is developed to predict the backflushing efficiency as a function of duration and interval. A contour plot of backflushing performance is presented to model these effects, and the backflushing conditions have been optimized as duration of 10 s and interval of 10 min using this neural network. Also, simple neural networks are established to predict the time evolution of flux before and after backflushing. The results predicted by the models are in good agreement with the experimental data, and the average deviations for all the cases are well within ±5%. The neural network approach is found to be capable of modeling the backflushing with confidence.展开更多
文摘An initial differential mathematical model of the process of catalytic reactions coupled in a membrane reactor is developed.The "donor" reaction refers to one that produces the key substance that may permeate the membrane and be consumed by the "acceptor" reaction.According to the maximum principle in mathematics,the analysis indicates that,in order to obtain maximum yield of the "acceptor" reaction,there should exist our optimal profile of the packed-bed catalytic activity on the "acceptor" reaction side of the membrane reactor,which is a one-time open-close function at the optimum position,as followsξ *(z)=0 ? (0<z<z r ) ξ max (z r ≤z≤1)where ξ *(z) is optimal function of catalytic activity, ξ max its maximum value, z the position,and z r the optimum position.The function means physically that catalytic activity equals to zero near the inlet and that,at the optimum position,activity should be kept maximum constant value on the "acceptor" reaction side.Finally the effect of membrane permeability on the optimum catalytic activity is also investigated.It is found that,as the membrane permeability increases,the optimum open-close position of optimal catalytic activity profile function will move closer to the inlet of the membrane reactor.
基金Supported by the National Natural Science Foundation of China (No. 20306023).
文摘In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear problem as the system integrates enzymatic hydrolysis, membrane separation and periodic backflushing together. In this paper an alternative artificial neural network approach is developed to predict the backflushing efficiency as a function of duration and interval. A contour plot of backflushing performance is presented to model these effects, and the backflushing conditions have been optimized as duration of 10 s and interval of 10 min using this neural network. Also, simple neural networks are established to predict the time evolution of flux before and after backflushing. The results predicted by the models are in good agreement with the experimental data, and the average deviations for all the cases are well within ±5%. The neural network approach is found to be capable of modeling the backflushing with confidence.
