Steam–gas pressurizers are self-pressurizing, and since steam and noncondensable gas are used to sustain their pressure, they experience very complicated thermal–hydraulic phenomena owing to the presence of the latt...Steam–gas pressurizers are self-pressurizing, and since steam and noncondensable gas are used to sustain their pressure, they experience very complicated thermal–hydraulic phenomena owing to the presence of the latter. A steam–gas pressurizer model was developed using Relap5 code to investigate such a pressurizer's thermal–hydraulic characteristics.The important thermal–hydraulic processes occurring in the pressurizer model include bulk flashing, rainout, wall condensation with noncondensable gas, and interfacial heat and mass transfer. The pressurizer model was verified using results from insurge experiments performed at the Massachusetts Institute of Technology. It was found that noncondensable gas was one of the important factors governing the pressure response, and the accuracy of the developed model would change with different mass fractions and types of noncondensable gas.展开更多
A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cos...A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.展开更多
文摘Steam–gas pressurizers are self-pressurizing, and since steam and noncondensable gas are used to sustain their pressure, they experience very complicated thermal–hydraulic phenomena owing to the presence of the latter. A steam–gas pressurizer model was developed using Relap5 code to investigate such a pressurizer's thermal–hydraulic characteristics.The important thermal–hydraulic processes occurring in the pressurizer model include bulk flashing, rainout, wall condensation with noncondensable gas, and interfacial heat and mass transfer. The pressurizer model was verified using results from insurge experiments performed at the Massachusetts Institute of Technology. It was found that noncondensable gas was one of the important factors governing the pressure response, and the accuracy of the developed model would change with different mass fractions and types of noncondensable gas.
基金Sponsored by National Natural Science Foundation of China(51304053)International Science and Technology Cooperation Program of China(2013DFA10810)
文摘A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.
