Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.
Radioactive waste levels have continued to increase due to the growth and development of nuclear energy, industrial or medical radioactive use. In this regard, conventional radioactive waste generated by nuclear power plants cannot be ignored. Effective management of nuclear radioactive waste plays a vital role in alleviating negative impacts on the society and environment. Despite the progress that has been made concerning separation and recycling of spent nuclear fuel through the PUREX process, several gaps in knowledge still exist especially towards the development of a robust separation system based on solid-phase extraction using porous materials. Solid phase extraction is being viewed as one of the most convenient and effective approaches in the removal of cations in radionuclide solutions. This is due to its ability to increase the selectivity and sensitivity of the method as it permits discriminatory binding of analyte to a solid support where the analyte can be collected and thereafter eluted using small quantity of a different solvent. The review covers the current methods used in aqueous nuclear reprocessing, highlights their deficiencies and introduces the potential of applying solid-phase extraction in management of nuclear waste. This study gives the prospects of functionalized porous sorbent materials as solid support in solid-phase extraction of spent nuclear fuel elements.
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