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The human factor in licensing and operating the next generation of nuclear plants
As human factors specialists working at the intersection of human performance and nuclear operations, we are witnessing one of the nuclear sector’s most significant transitions in decades. The emergence of small modular reactors, microreactors, and other advanced designs is reshaping the industry’s landscape. Digital instrumentation and controls, passive safety systems, and increased automation are creating opportunities for greater safety margins and more flexible operation. These same features also fundamentally redefine what it means to “operate” a nuclear plant. Interactions among human roles, automation, and passive systems shape how people maintain awareness, exercise judgment, and intervene when necessary. These developments affect both operational realities and the regulatory foundations on which nuclear safety is built.
Kenji Takeshita, Yoshio Nakano
Nuclear Technology | Volume 133 | Number 3 | March 2001 | Pages 338-345
Technical Paper | Reprocessing | doi.org/10.13182/NT01-A3178
Articles are hosted by Taylor and Francis Online.
An adsorption process of iodine using Ag0-loaded adsorbents was studied for the removal of radioactive iodine in the process off-gas from a spent nuclear fuel reprocessing plant. A mathematical model to predict a breakthrough curve of I2 on the adsorbent bed was proposed. This model consists of the mass balance equation of I2 in the adsorbent bed, the mass transfer equation of I2 through the boundary layer surrounding the adsorbent particle, the intraparticle diffusion equation of I2, and the kinetic equation for the gas-solid reaction between I2 and loaded Ag0. Two unknown parameters in the model, the intraparticle diffusivity De and the apparent rate constant for the gas-solid reaction kr were determined simultaneously from the adsorption data measured by a thermogravimetric analyzer. The breakthrough curves predicted by the model using these parameters were in good agreement with the experimental ones. The rate-controlling step was evaluated by the effectiveness factor calculated from the kr value and the concentration gradient of I2 in the adsorbent particles, which was estimated by the model. From these results, the adsorbent structure required to improve the process performance is discussed. The proposed model is available as a calculation tool to support the design of the adsorption process.