An automated gaseous tritium sampling measurement method

By using a dynamic memory entropy observer and a reinforcement learning decision model, the problems of sensor wall adsorption and environmental interference in the gaseous tritium monitoring system were solved, achieving high-precision, long-cycle tritium concentration measurement and extending sensor lifespan.

CN121721677BActive Publication Date: 2026-05-26SHAANXI QINZHOU NUCLEAR & RADIATION SAFETY TECHNONLOY CO LTD
View PDF 2 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHAANXI QINZHOU NUCLEAR & RADIATION SAFETY TECHNONLOY CO LTD
Filing Date
2026-02-26
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing gaseous tritium monitoring systems suffer from memory effects and environmental interference caused by adsorption on the sensor wall under high concentrations or long-term operation, which are difficult to effectively remove. This leads to measurement blind spots and deterioration of the signal-to-noise ratio. Furthermore, the lack of an adaptive cleaning and maintenance mechanism makes it difficult to achieve high-precision long-term operation.

Method used

By combining a dynamic memory entropy observer model with reinforcement learning decision-making, sampling and cleaning modes are dynamically switched using real-time ionization current data and environmental state data. The extended Kalman filter algorithm is used to calculate the degree of adsorption on the sensor wall, generate the optimal sampling control strategy, remove environmental interference, and extend the sensor life.

Benefits of technology

It achieves high-precision tritium concentration measurement under complex working conditions, eliminates environmental interference, prevents sensor blind spots, extends sensor lifespan, and ensures monitoring reliability and accuracy throughout the entire life cycle.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN121721677B_ABST
    Figure CN121721677B_ABST
Patent Text Reader

Abstract

This invention relates to the field of nuclear radiation monitoring technology, specifically to an automated gaseous tritium sampling and measurement method, comprising: a feature space mapping step: acquiring ionization current and environmental data, and extracting multidimensional signal features; a memory entropy observation step: inputting the features into a dynamic memory entropy observer model, and calculating the current memory entropy value characterizing the degree of nuclide adsorption; an intelligent decision-making step: generating an optimal sampling control strategy based on the memory entropy value using a reinforcement learning decision model; and a dynamic control and output step: switching the valve between continuous sampling and pulse cleaning modes in response to the strategy, and outputting the measurement results. This invention, by introducing a memory entropy state variable, transforms the sensor into an active player, maximizing the effective service life of the sensor.
Need to check novelty before this filing date? Find Prior Art