Design and control of pressure-swing distillation(PSD) with different heat integration modes for the separation of methyl acetate/methanol azeotrope are explored using Aspen Plus and Aspen Dynamics. First, an optimum ...Design and control of pressure-swing distillation(PSD) with different heat integration modes for the separation of methyl acetate/methanol azeotrope are explored using Aspen Plus and Aspen Dynamics. First, an optimum steady-state separation configuration conditions are obtained via taking the total annual cost(TAC) or total reboiler heat duty as the objective functions. The results show that about 27.68% and 25.40% saving in TAC can be achieved by the PSD with full and partial heat integration compared to PSD without heat integration. Second,temperature control tray locations are obtained according to the sensitivity criterion and singular value decomposition(SVD) analysis and the single-end control structure is effective based on the feed composition sensitivity analysis. Finally, the comparison of dynamic controllability is made among various control structures for PSD with partial and full heat integration. It is shown that both control structures of composition/temperature cascade and pressure-compensated temperature have a good dynamic response performance for PSD with heat integration facing feed flowrate and composition disturbances. However, PSD with full heat integration performs the poor controllability despite of a little bit of economy.展开更多
This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural ne...This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.展开更多
文摘Design and control of pressure-swing distillation(PSD) with different heat integration modes for the separation of methyl acetate/methanol azeotrope are explored using Aspen Plus and Aspen Dynamics. First, an optimum steady-state separation configuration conditions are obtained via taking the total annual cost(TAC) or total reboiler heat duty as the objective functions. The results show that about 27.68% and 25.40% saving in TAC can be achieved by the PSD with full and partial heat integration compared to PSD without heat integration. Second,temperature control tray locations are obtained according to the sensitivity criterion and singular value decomposition(SVD) analysis and the single-end control structure is effective based on the feed composition sensitivity analysis. Finally, the comparison of dynamic controllability is made among various control structures for PSD with partial and full heat integration. It is shown that both control structures of composition/temperature cascade and pressure-compensated temperature have a good dynamic response performance for PSD with heat integration facing feed flowrate and composition disturbances. However, PSD with full heat integration performs the poor controllability despite of a little bit of economy.
基金Project supported by the National High-Technology Research and Development Program Foundation of China(Grant No.2003AA517020)
文摘This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.
